Jad Jebara, Founder and President at Hyperview explains why the infrastructure that powers AI deserves just as much attention as trends such as tokenmaxxing.
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The race to adopt AI inside enterprises has created a new metric for success: usage. Across Silicon Valley and increasingly, the wider business world, organisations are being encouraged to maximise internal AI consumption as aggressively as possible. More prompts, more AI-assisted workflows, more automation and more model interaction are becoming signs of AI maturity, a trend referred to as “tokenmaxxing.”
On the surface, the logic is understandable. Businesses do not want to fall behind on AI adoption, executives are under pressure to demonstrate AI maturity, and employees are increasingly being encouraged to integrate AI into everything from software development and analytics to customer service and internal productivity. However, underneath the excitement sits a much larger operational reality that the industry is only beginning to confront.
The AI conversation is shifting away from experimentation and towards the operational reality of sustaining infrastructure demand at scale, where the challenge is no longer simply whether AI works, but whether the infrastructure supporting it can scale sustainably, securely, reliably and efficiently as usage accelerates. That distinction matters because AI consumption behaves very differently from previous waves of enterprise technology adoption.
AI consumption changes the infrastructure equation
Most users interact with AI through relatively simple interfaces such as chatbot windows, coding assistants or summarisation tools, which makes the experience feel lightweight, fast and almost frictionless.
What remains invisible is the infrastructure required behind every interaction. Each AI request triggers compute workloads running across highly power-intensive GPU infrastructure inside data centres. Those environments require enormous amounts of electricity, cooling capacity and operational coordination to sustain performance. As enterprise usage expands from isolated pilots into everyday operational dependency, the infrastructure implications become exponentially larger. This is one of the reasons governments are dramatically revising their environmental forecasts around AI.
The UK Government originally estimated AI compute would generate around 0.25 MtCO₂ over the decade to 2035. The forecast has now been revised upwards to at least 34 MtCO₂. AI data centres are also projected to account for between 0.9% and 3.4% of the UK’s total carbon emissions by 2035. These are not marginal adjustments, but a reflection of how quickly the scale of long-term AI demand has been underestimated.
Early AI discussions largely assumed usage would remain relatively focused on specialised tasks. Instead, AI is now being embedded simultaneously across productivity platforms, software engineering, search, analytics, cybersecurity and customer operations, with organisations actively encouraging employees to use AI continuously. This shift changes the operational equation entirely.
The hidden cost behind the chatbot interface
One of the biggest misconceptions around AI adoption is that the cost sits primarily inside the model itself, when in reality the operational burden extends far beyond inference costs or API pricing.
AI-heavy environments introduce sustained power loads that traditional enterprise infrastructure was never designed to support at this scale, with GPU workloads creating dense thermal profiles, more volatile cooling requirements and far less predictable capacity behaviour than conventional enterprise applications.
Many operators are still managing these environments across fragmented infrastructure systems built long before AI workloads became mainstream, with power monitoring sitting in one platform, environmental telemetry in another, asset inventories elsewhere and sustainability reporting often remaining disconnected entirely. The result is that infrastructure teams are forced to make operational decisions without a unified understanding of how workloads, energy usage, cooling behaviour and capacity constraints interact in real time.
As AI demand continues to scale, that fragmentation becomes increasingly risky because the challenge is no longer simply about building more compute capacity, but about understanding how infrastructure behaves under sustained AI load and being able to optimise it continuously in real time. Without that level of visibility and operational context, inefficiencies can compound very quickly.
Infrastructure limits are becoming more visible
There is still an assumption in many parts of the market that infrastructure will continue scaling indefinitely to absorb AI demand, but in practice, operators are already encountering very real physical and operational constraints.
Power availability is emerging as one of the biggest bottlenecks, with utilities across multiple regions struggling to keep pace with projected AI-driven demand growth. Unlike many previous enterprise workloads, AI consumption creates highly persistent energy demand concentrated across specific locations, placing increasing pressure on grid resilience, cooling infrastructure and long-term power planning.
Organisations are also facing growing pressure to meet sustainability targets and regulatory obligations at the same time governments are accelerating AI investment and adoption alongside broader net-zero strategies. Those priorities are beginning to collide, creating a difficult balancing act between economic competitiveness, AI innovation and environmental sustainability that will only become more pronounced as enterprise AI usage shifts from optional tooling to core operational dependency.
This is why the conversation around AI infrastructure can no longer focus purely on speed and scale, because efficiency, orchestration and operational intelligence are becoming just as important as compute capacity itself.
Why visibility will matter more than raw compute
The next phase of AI growth will place far greater emphasis on infrastructure visibility and coordination rather than simply adding more hardware.
Operators need a clearer understanding of how environments are performing in real time, including where power is being consumed, how workloads are affecting cooling efficiency and where capacity risks are beginning to emerge. That becomes even more important as AI workloads push organisations to distribute infrastructure across hyperscale facilities, colocation environments and edge locations.
As these environments become more dynamic, operators will increasingly need the ability to orchestrate workloads intelligently across multiple locations while balancing power, performance and efficiency constraints simultaneously. This is where AI-driven operational management becomes increasingly valuable, not because AI is a universal solution to infrastructure complexity, but because the scale and operational density of modern environments are becoming too difficult to manage through fragmented oversight alone.
The organisations that adapt successfully will not necessarily be the ones consuming the largest volume of AI. They will be the ones capable of managing the infrastructure underneath that demand intelligently, efficiently and sustainably.
Moving beyond the AI consumption race
The broader issue with trends like tokenmaxxing is not that organisations are adopting AI too aggressively. It is that the market is increasingly treating AI consumption itself as a measure of progress, where more prompts, more workflows and more automation are becoming signals of AI maturity without enough consideration for the infrastructure required to sustain that demand efficiently over the long term.
As adoption scales further, operational realities become much harder to ignore. Power constraints, cooling pressures and sustainability targets are no longer theoretical concerns sitting in the background of AI growth, but operational challenges that infrastructure teams are already dealing with in real time.
Generating more AI activity is relatively easy by comparison. Sustaining that demand efficiently, reliably, cost-effectively, and in a continuously optimised way at scale is the far greater challenge now beginning to emerge.
Bodo Philipp, CEO at MHP Consulting UK, explains how the successful adoption of new ERP systems alongside digital transformation, will enable automotive companies to respond to today’s challenges and understand how to build a successful future.
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Successful adoption of new ERP systems such as SAP S/4 HANA and digital transformation will not only enable automotive companies to respond to today’s challenges by improving efficiency and productivity, but also provide the data required to better understand how to build a successful future, explains Bodo Philipp, CEO, MHP Consulting UK.
Agile operations
Automotive manufacturers have long been lauded for highly efficient manufacturing and logistics processes. But in a fast-changing market, agility is now a priority. New market entrants, a changing sales model and fundamental technology changes combine to create a new customer driven paradigm that demands faster, dynamic reaction.
While the priority for many organisations’ SAP S/4HANA migration strategy has been adding efficiency to manufacturing and logistics processes, the adoption of modern cloud-based ERP also provides the opportunity to drive additional value from customer data. Automotive is not alone in facing pressure on market share, changing customer perception, even core values, alongside agile and innovative new market entrants. Industries globally face changing customer demands and in response, tailored customer experience is scaling new heights – from AI led engagement to real-time customised offers and the creation of new routes to market.
For successful brands, this level of personalisation and customer recognition must become part of the automotive buying and critically, owning process. Customers expect car brands to reflect their needs, whether that is AI driven air conditioning that automatically adjusts to each different driver’s preferences, or seamless integration with social media accounts to enhance the entire journey experience.
Informing change
One of the most pressing issues to address is how to counter the rise in intended vehicle brand defection, up to 56% in the UK in 2025. In addition to understanding and responding to customers’ continually evolving attitudes towards ICE versus Electric Vehicles versus hybrid, the industry must also address changing perceptions of vehicle ownership. While attitudes vary globally, there is a marked shift towards Mobility as a Service (MaaS) over vehicle ownership amongst younger consumers.
The migration from legacy ERP to cloud-based SAP S4 HANA provides the foundation for automotive companies to respond to changing customer behaviours. With a single source of all operational data, automotive manufacturers can leverage powerful analytics to better understand customer expectations and experiences.
This insight can inform the transformation of customer engagement throughout the changing sales model. It can flag opportunities to safely use AI, both within vehicles and throughout the ownership experience. Critically, it can surface information to support complex decisions about future business models and brand direction – such as carving a niche in the luxury car market or being at the forefront of the self-driving market.
Conclusion
In an industry that is enduring change from every direction, future success will require a different approach at every level, from the way brands interact with and engage customers throughout a vehicle’s lifespan to the pace of innovation and the evolution of vehicle sales and finance models. The migration to SAP S/4 HANA is now pressing, given the deadline, but with the right approach, the shift to the cloud-based ERP solution presents an essential opportunity to redefine the automotive industry.
It will enable automotive manufacturers to gain enormous value from highly accurate predictions and forecasts; it will transform agility and offer the chance to drive, rather than just respond to customer perceptions. It will introduce new opportunities to leverage AI both to support faster product iteration and improve customer engagement.
Improvements in efficiency and productivity will underpin the business case for migration; but it is the information held within an integrated, end to end ERP system that will play a vital role in creating the foundation for future automotive success.
MHP Consulting is Porsche-owned, and is a leading global consulting firm specialising in strategy, digital transformation, and performance improvement across various industries, including automotive, manufacturing and technology.
Sara Sullivan, SVP of Solution Engineering, explains how to avoid getting caught up in the ‘workslop economy’.
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Have you ever read a piece of work, suspected generative AI was involved, and thought the result felt… sloppy? The term “workslop”, used to describe low-quality, AI-generated content that lacks substance, has quickly entered the lexicon. Many teams are now navigating unnecessarily convoluted emails, low-context reports, inaccurate summaries and surface-level content, to name a few. But a bigger challenge emerges when this same low-quality AI-generated content is published externally. Over time, it erodes brand voice and creates a sea of sameness.
But the issue is that core content doesn’t necessarily mean better content. Generative AI has made production effortless, but without discipline and human creativity guiding it, scale can come at the expense of clarity and differentiation.
And this is more than just a quality-control problem. It’s often a signal that organisations are adopting AI tools faster than they are adapting the culture, governance and working practices around them. It’s not the technology itself, but how it is being operationalised. What’s often missing is a shared understanding of how AI should fit into everyday workflows and expectations.
A closer look at marketing
Marketing teams are on the front line of this shift. They’re under constant pressure to increase output, with more channels, more formats and more personalisation, so it’s no surprise AI adoption is accelerating here.
But without disciplined content architecture and governance, AI risks creating what some are calling a ‘workslop economy’; more output, but less value. Organisations also need shared norms around how AI-generated work should be questioned, refined and improved before it reaches customers or colleagues.
Teams want to move fast (40% of marketers say ‘fast and efficient execution’ defines success) while also delivering quality content (42% say it is ‘high content quality and consistency’). These stats from a global report conducted by Contentful and Atlantic Insights, also found that nearly half (48%) of marketers are looking to AI-powered content tools to help them find the middle ground.
The tension is understandable. Marketing has always been a function under pressure to do more with less and AI appears to offer a release valve, promising speed and scale.
As AI tools become faster and more capable, the value of human judgement, context and editorial discipline only increases. The ability to combine human creativity with AI-driven insights is becoming essential to producing and scaling ideas with measurable impact. The marketing skills that matter most today are data analysis and interpretation (46%) and digital experience design (40%), followed by personalisation strategy (37%) and writing for AI tools (37%).
This shift requires a reframing of what good work looks like. It is no longer about who can generate the most content the fastest, but who can apply judgement, context and relevance to ensure that every piece of output serves a clear strategic purpose. Otherwise, the risk is that teams become curators of AI-generated drafts rather than creators of differentiated ideas.
The organisational cost of ‘workslop’
Beyond marketing, the ‘workslop economy’ carries wider organisational implications. Leaders may assume productivity is increasing because there is more visible output. In reality, this is often a cultural signal. When organisations reward visible activity rather than thoughtful outcomes, AI simply amplifies the behaviour already present. Meanwhile, employees quietly absorb the hidden tax of reviewing, editing, clarifying and sense-checking AI-generated material, in addition to maintaining a new set of tools that require continuous inputs. If not managed thoughtfully, this can erode both efficiency and morale.
There is also a reputational risk. Sloppy AI-assisted content can introduce factual inaccuracies, generic phrasing, or inconsistent messaging that chips away at brand credibility. Customers and stakeholders may not always detect the use of AI, but they can detect when something feels off, such as language that’s overly verbose yet oddly vague, polished yet impersonal, confident yet lacking depth.
Crucially, the proliferation of ‘workslop’ can mask deeper strategic gaps. If teams rely on AI to fill in thinking that has not yet been done, the technology amplifies ambiguity rather than resolving it. In this sense, AI becomes a mirror, reflecting the clarity (or lack thereof) within an organisation’s strategy and decision-making processes.
Key steps to pivot away from the “workslop” trap
AI’s real enterprise advantage lies in both acceleration and augmentation. Structured content, clear operating models and strong data foundations are what separate meaningful transformation from short-term experimentation. To avoid the ‘workslop’ trap, organisations need to move beyond ad hoc usage and towards intentional operations.
First, establish clear content architecture. AI performs best when it operates within well-defined frameworks, which includes brand guidelines, tone-of-voice principles, audience personas and approved messaging pillars. Without this scaffolding, outputs will default to generic patterns drawn from the broadest possible training data. With it, AI can become a powerful assistant that reinforces, rather than dilutes, brand distinctiveness.
Second, embed human-in-the-loop workflows. Rather than relying on individuals to apply judgement inconsistently, organisations should design automated workflows that define exactly when and where human review is required. This ensures that AI-generated outputs are systematically validated before reaching customers or stakeholders, particularly for high-impact or high-risk content. Making human oversight a built-in step helps maintain quality at scale.
Third, implement auditability by default. Organisations need visibility into how AI is being used across the content lifecycle: who generated content, where AI was applied, what changes were made and who approved the final output. This level of transparency is critical for accountability, compliance and continuous improvement. It also enables senior leaders to move beyond assumptions and understand how AI is actually being operationalised across teams.
Fourth, invest in AI literacy, as well as AI tools. Employees need to understand how generative models work, where they are strong and where they are prone to hallucination. Training should focus on prompt design, critical evaluation of outputs and ethical considerations. Building literacy also builds confidence, allowing teams to challenge AI outputs rather than accept them at face value. When people feel confident challenging AI-generated suggestions, they are more likely to use the technology as a collaborator rather than a crutch.
Fifth, align incentives with impact. Incentives shape behaviour and behaviour ultimately shapes culture. If performance metrics reward speed and volume alone, ‘workslop’ will inevitably proliferate. Instead, organisations should measure success through outcomes such as engagement quality, customer relevance, strategic clarity and measurable business impact. This encourages teams to use AI selectively and thoughtfully, rather than indiscriminately.
Finally, organisations must be explicit about how AI is expected to reshape each role. More senior leaders, for instance, may need to spend more time critically evaluating AI-assisted outputs, while junior team members may take greater responsibility for drafting and iterating with these tools. Clearly defined, AI-related responsibilities help distribute accountability across the team and ensure that low-quality outputs do not slip through the cracks.
Reclaiming substance in an AI-boosted workplace
The rise of the ‘workslop economy’ is not inevitable, but it is a real risk if organisations conflate technological adoption with meaningful transformation. Ultimately, the organisations that benefit most from AI will be those that double down on the uniquely human strengths of good judgement, curiosity, creativity and contextual understanding.
They will treat AI as a partner that enhances thinking. And they will recognise that the true competitive advantage lies not in producing more content, but in producing better, clearer and more purposeful work.
Dr Abdulrahman Kerim, Computer Science Tutor at Superprof, on how AI will eventually take over time-consuming routine tasks, and will free educators up for supporting students, cultivating curiosity, and shaping meaningful learning experiences
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With education systems continuing to push for more efficient and personalised learning experiences, AI is proving to be a major factor and catalyst for achieving these goals. This year alone, 60% of educators said that they use AI to assist with their workload, a jump from around 48% in 2024.
Here are 5 key predictions of how AI is set to reshape the classroom, the evolving role of tutors, and the overall student educational journey in 2026.
Personalised learning will replace one-size-fits-all classrooms
Despite the progress made when it comes to the adoption of AI, classrooms still rely on fixed reading levels, static worksheets, and uniform methodologies that fail to reflect the diverse backgrounds of students, their interests, strong points, and even goals for the future.
However, in 2026, we’re expecting to see this gap starting to close. AI systems will begin to assist with drawing student data, including assessment results and learning objectives, to automatically update and adapt course materials in real time. This means that students will receive an education that matches their level, and that’s aligned with their goals. This new process will certainly not get rid of human tutors who will still be vital to the learning process. However, their role will become of a more strategic nature where, instead of content creators, they will become learning experience designers, guiding and refining a student’s personalised learning journey.
Immediate, insight driven, grading and feedback
As of this year, manual grading still dominates the educational system. Tutors still spend hours reviewing assignments, and students wait for weeks for feedback. This process certainly delays and weakens the learning process overall.
Next year, we will see a higher rate of adopting the AI-powered grading and feedback systems, which will contribute dramatically to the process. The tools will have the ability to evaluate submissions within minutes, highlight errors, shed light on common patterns of misunderstanding, and provide tailored recommendations of improvement. The result of this will be more consistent and efficient grading, and deeper insights that will guide both students and educators alike.
Around the clock student support
Student support teams continue to be thinly stretched, resulting in long waits for answers to academic or administrative questions. This does not only cause frustration, but also results in missing deadlines a lot of the time.
In 2026, AI chatbots will become more intelligent, responsive, and deeply embedded across educational platforms. These systems will provide immediate support, during or after hours, answering academic queries, clarifying assignments requirements, guiding students through enrolment processes, and reminding them of deadlines and outstanding tasks. The AI chatbot will enhance the FAQ responses with their ability to understand context, and personalise guidance accordingly. This will enhance both engagement and retention.
Clear, consistent, and scalable assessment of soft skills
Soft skills such as leadership, communication, and problem solving remain very hard to measure accurately. This measurement still relies on manual observation, which is often subjective, inconsistent, and nearly impossible to scale.
In 2026, however, AI is set to start cracking the code of these competencies. By analysing group interactions, patterns of students participation, communications dynamics, and even study behaviours, AI tools will generate concise, data-driven insights in students’ performance and progress. Tutors will greatly benefit from this feature by receiving detailed summaries of strengths, and gaps, allowing them to design a more targeted and achievable approach to support.
Rapid and customisable curriculum design
Similar to some of the key things discussed in this article, curriculum design still remains fairly slow and fragmented, with learners looking to move into new fields often struggling to find coherent and updated learning pathways.
In the next year, AI will significantly accelerate curriculum development. The systems will be capable of identifying gaps, recommending learning methodologies, align content with standards, and even generate fully customised learning plans. The tools will be informed by large scale data on how thousands of similar learners have progressed in their subjects, the challenges they faced, the resources that helped them navigate those challenges, and the skills that are highly in demand.
In conclusion, AI is set to dramatically change the way people both teach and learn. However, this certainly does not mean it will replace educators. Instead, it will empower them. AI will eventually take over time-consuming routine tasks, and will free educators up for supporting students, cultivating curiosity, and shaping meaningful learning experiences.
Matthew Biboud-Lubeck, General Manager EMEA at Amperity, on finding the key to creating meaningful engagements that build brand loyalty, drive up the lifetime value of a customer and, ultimately, give businesses the competitive edge
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It’s no secret that personalisation is vital for building lasting customer relationships and nurturing brand loyalty. AI holds the key to creating and delivering successful personalised engagement. And travel and airline businesses are keen to adopt this technology in their customer-facing applications.
The reality, however, is that few are currently able to scale AI across their organisation. According to industry research, published in Amperity’s 2025 State of AI for Hotels & Airlines, AI adoption is widespread across the sector, but applications remain experimental. Just 12.5% of companies say they are ready to scale their use cases.
The challenge isn’t ambition. Nearly all (96%) businesses in the sector are planning to maintain or increase AI spending over the next 12 months. The problem lies in two major barriers that prevent AI from scaling effectively. A lack of technical expertise within teams. And low confidence in the data that AI tools rely upon, due to fragmented or unreliable customer information.
How to reduce reliance on technical teams
Keeping pace with rising consumer expectations means tailoring customer engagement and presenting meaningful, real-time offers based on individual preferences. Although travel businesses know that AI is going to help them achieve this true personalisation, at scale, only 35% currently use AI in guest experiences.
One reason for this is the inability of non-technical teams to independently access data they need, and act on the information provided. AI usage among travel and airline brands remains concentrated in marketing, customer support and sales. But less than a third of professionals in these teams say they can manage customer data without the support of their IT department. This is delaying, and in many cases denying, teams the ability to tailor customer communications.
What teams need are intuitive tools, empowered by GenAI, that simplify the process of accessing customer insight. These platforms allow non-technical people to ask questions using natural language. And discover such things as: what their most valuable customers purchase most often, what form of content they are most likely to engage with and on which channels they are doing this.
AI training is still an important consideration for all business users, who need to understand how AI can add value, where its limitations lie and what safeguards should be put in place to ensure responsible use. But these GenAI capabilities are now helping teams to access crucial intelligence quickly and apply this to customer engagements at scale.
How to build confidence in customer data
Accessing data is one thing, but having confidence in that information is another. This is an area where travel companies struggle. Less than a quarter claim to be very confident in their ability to understand and act on customer data.
This is often because data is separated and siloed across different systems and channels. More than half of businesses (58%) report that their customer data is fragmented or incomplete. This can impair visibility, lead to inaccurate reporting and increase the risk of human error, while also adding IT costs. It also increases the chances that duplicate customer profiles will be created across those different channels.
Consumers commonly engage with travel brands across several touch points including email, mobile, apps and in-store. They will also use various identifiers, such as abbreviated names, alternative email addresses, etc., when they do so. Companies need to deploy AI tools that can unify this data and stitch together information coming from different channels. If they don’t, there is the real possibility that they will end up sending conflicting or irrelevant communications to the same person. The consequences of this can be damaging as it risks annoying and alienating customers.
Building a solid customer data foundation
To enable fast and accurate omnichannel communications, it’s vital for companies to have a customer data platform (CDP) that is enabled by identity resolution. To build high levels of confidence in the operational identities required for personalisation and broader marketing audience targeting, companies will need to blend deterministic and probabilistic matching strategies.
The research shows, however, that just 18% of travel businesses have this identity resolution technology in place. This is preventing companies from accurately viewing booking histories, identifying behaviour patterns and unlocking the potential of AI for the tailored engagements necessary for building brand loyalty and increasing the lifetime value of a customer.
The evidence shows that organisations with a CDP are much better placed to ensure customer data is ready for use in marketing or analytics. The latest generation of CDPs will automatically consolidate data across various communication channels, detect and resolve identity issues. And provide travel businesses with a true, 360 degree profile of each customer, based on all interactions across each channel.
These platforms are also enabling businesses to advance their AI deployments. More than half (54%) of businesses with a CDP report daily use of AI – this compares to 28% for those without. Half of CDP-enabled businesses use AI in guest-facing deployments, compared to less than a fifth (19%) of those without. Businesses are also five times more likely to have adopted AI across business units, when they have a CDP.
Preparing for an Agentic AI era
Next generation CDPs are also helping companies prepare for another major trend reshaping customer data management – the rise of agentic AI. Agents are already capable of observing behaviour, interpreting what it means and taking action autonomously.
For example, imagine a situation where a flight has been delayed or cancelled. In this scenario, a combination of agents can now work together to provide customers with a solution. In an instant, one can spot the problem, another will find alternate solutions, while a third will send the best options to the customer. The customer doesn’t need to wait – they simply receive a notification in real-time. Some human orchestration is still required and guardrails are important to ensure communications hit the mark. Most important to note, however, is that it only works if those agents have access to accurate data.
The travel sector is clearly at an inflection point on its road to full AI adoption. It’s moving from pilot projects and early stage experimentations to broader deployments and scaled execution. Success in customer facing applications, however, will depend on an organisation’s ability to provide non-technical teams with access to high quality, unified customer profiles.
Modern CDPs help businesses to make faster, smarter decisions that will help to drive growth. With these solid data foundations in place, businesses can deploy and scale AI-enabled customer facing applications more readily. This is the key to creating meaningful engagements that build brand loyalty, drive up the lifetime value of a customer and, ultimately, give businesses the competitive edge.
Peter Pugh-Jones, EMEA Field CDO at Confluent, explains why as more AI projects move into production, that gap will become harder to ignore. The conversation will shift away from what AI could do and towards what organisations are actually able to support in practice
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AI is no longer an experiment in financial services. It’s already embedded in day-to-day operations.
Banks are rolling out AI-driven customer service, investing in automation, and exploring how generative and agentic systems can improve decision-making. Around 92% of global banks are already using AI in at least one core function, which suggests the industry is well on its way.
As these initiatives scale, however, a different reality starts to emerge.
What works in a pilot often becomes more complex, not to mention expensive, when rolled out across the business. In many cases, the issue is not the AI itself, but the environment it is being deployed into.
There is no ‘magic bullet’ for AI in financial services. Its impact is shaped by how well organisations can access, connect, and act on their data — increasingly through real-time approaches such as data streaming.
When AI meets reality
This is particularly visible in customer service.
AI-powered chatbots and virtual assistants are now widely used to handle routine queries, categorise transactions, and guide users through basic processes, and in the right conditions they can deliver real efficiency gains.
Scaling those systems, however, is a different challenge altogether. Despite the level of investment, many of these systems continue to rely on fragmented or outdated information, which sees the experience quickly starts to break down.
For example, customer context can be missing or inconsistent. Conversations don’t carry across channels. Employees are forced to double-check or correct outputs, removing much of the efficiency they were meant to deliver.
In a sector where trust matters, that’s difficult to ignore.
Legacy systems are still doing most of the damage
Frameworks like the EU AI Act are shaping how organisations deploy AI, particularly in high-impact use cases such as credit scoring, fraud detection, and customer risk assessment. Requirements around transparency, auditability, and data governance are raising the bar for how AI systems are built and monitored. And rightly so, given the importance of trust in financial services.
In practice, though, legacy infrastructure is often the bigger issue. When data is fragmented across systems or difficult to access in real time, it becomes far harder to evidence how decisions are made or ensure models are operating on complete, up-to-date information.
In many cases, it is these underlying data limitations — rather than regulation itself — that slow progress. With many banks reliant on systems built up over decades through mergers, upgrades, and workarounds, they struggle to create a single, reliable view of a customer or transaction — something AI depends on.
This also helps explain how FinTech startups can outmaneuver industry giants. Free of technical or technological baggage, they can build around modern data architectures from the start and bring new ideas to market more quickly.
AI can’t reach its potential, or in some cases even run, in a legacy environment. Sticking with the customer service example, one such example is personalisation.
Personalisation only works if the data is current
Personalisation has been a goal in financial services for years, but in many cases it has been closer to segmentation than true individualisation. If AI is going to change that, it needs a flow of data that can tell it exactly what’s happening in the moment.
Hyper-personalised experiences depend on real-time context — understanding a customer’s behaviour and situation as it evolves, often enabled by data streaming to ensure systems are working from the most up-to-date information. Without that, interactions quickly become disconnected.
A customer might start a query in an app, follow up by email, and then call support, only to repeat the same information each time. This is not a limitation of AI, but of how information moves between systems.
When that flow is in place, interactions become far more seamless, with context carrying across channels and experiences feeling more consistent.
AI should support people, not replace them
There is a lot of discussion about AI replacing human roles in financial services, but a fully automated model is unlikely.
AI is highly effective at handling repetitive, high-volume tasks, like triaging customer queries or processing transactions. But more complex interactions still require human judgement, empathy, and context — particularly in areas such as lending, investments, or financial advice.
Customer expectations also play a role. While digital channels continue to grow, many customers still value the option to speak to a person, particularly in more sensitive or high-stakes situations.
As a result, the most effective model is increasingly a hybrid one. AI handles routine processes and surfaces insights, while people focus on more complex and sensitive interactions — provided both are working from the same reliable, up-to-date information.
Early investment has focused on what AI can do. Attention is now turning to what is needed to make it work properly.
That starts with data. Organisations need to ensure data is connected, governed, and available when it is needed. Too often, this work is treated as something to address later, when in practice delaying it tends to create more complexity and cost over time.
There is also a cultural element to consider. In many organisations, established ways of working can slow progress just as much as technology. Addressing that is just as important as modernising systems, particularly when AI initiatives span multiple teams.
A question of readiness
Financial services organisations are being asked to move quickly while maintaining high levels of control and accountability. That is not easy.
The organisations that make progress will be those that focus less on AI as a standalone capability and more on the conditions that allow it to work. If those conditions are not in place, results will remain inconsistent.
As more AI projects move into production, that gap will become harder to ignore. The conversation will shift away from what AI could do and towards what organisations are actually able to support in practice.
In most cases, it comes down to a simple question: is the data ready?
Frank Jaquez, Head of Talent & Culture at Skillsoft, on why an AI skills strategy built without skills visibility is not just incomplete; it is fundamentally blind
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Organisations are accelerating their adoption of AI, with 33% of UK businesses planning investment and training in 2026. As investment rises and competitive pressure mounts, leaders increasingly recognise that AI capability will distinguish high-growth performers from the rest. Yet a key question remains unanswered: are workforces equipped with the skills to use AI effectively and productively?
Without clear skills visibility – a transparent picture of required skills & capabilities, existing strengths, emerging gaps and role-specific proficiency – AI strategies rest on assumptions rather than evidence. Leaders are forced to guess who can adapt, which teams need development and whether to prioritise upskilling, reskilling or the deployment of AI agents.
Too often, organisations launch AI initiatives without understanding whether their employees can use these tools effectively. Or translate them into measurable outcomes. The result is predictable: pilots lose momentum, adoption slows, and ROI remains uncertain. When organisations lack skills visibility, decisions around investment, talent and transformation become guesswork. And guesswork is no foundation for a successful AI strategy.
Why skills visibility must come first
The foundation of any effective AI strategy is a clear view of workforce capabilities. Understanding what employees can do today, identifying the skills they will need tomorrow, and how those capabilities align to business goals. It also requires clarity on what AI can currently deliver and the new capabilities it introduces into the organisation. When skills are measurable and tied directly to real tasks, organisations can move decisively from experimentation to execution.
However, this clarity cannot be achieved through learning content alone. High-quality learning is essential, but when it sits apart from roles, skills and outcomes, it cannot provide the clarity, validation or evidence of proficiency that leaders now require. Completing training does not automatically translate into performance, confidence or capability.
The scale of this challenge is reflected in recent data. Nearly a third of UK employers lack a clear view of the skills their workforce will need in the next two to three years. Whilst 60% identify workforce planning as a critical or high priority, only 25% base their approach on skills. This gap highlights a growing disconnect. Between the urgency of AI adoption and the visibility organisations have into the workforce capabilities required to sustain it.
Building a connected ecosystem
To achieve genuine visibility, organisations need to start by identifying the critical skills required to deliver their strategy, and then integrate skills mapping, assessment, development and measurement into a single, connected system. When these elements operate within one environment, leaders gain a consistent, evidence-based view of workforce capability. This highlights areas of risk and readiness as priorities shift.
Skills-aligned learning pathways ensure development efforts directly build the capabilities that matter most. When those skills are connected to clear, strategic goals, employees understand how their development ties to business priorities. Clear milestones and measurable indicators give leaders confidence that learning is translating into real-world performance where it is needed most.
AI-powered learning is becoming central to this approach. By analysing skills data, identifying gaps in real time and recommending targeted development aligned to roles and organisational priorities, AI enables a dynamic, continuous ‘skills supply chain’. Instead of relying on infrequent skills audits, organisations benefit from an adaptive system that evolves alongside the business.
This model supports skill development at scale. It educes reliance on costly external hiring,. This helps organisations to unlock business results by developing and redeploying existing talent through more strategic upskilling and reskilling.
Linking skills to business priorities
Skills visibility is most powerful when it is tightly linked to organisational priorities. When leaders understand which skills matter most and how their workforce measures against them, they can make informed decisions about where to invest and how to focus development.
This alignment enables organisations to build capabilities that will drive the biggest impact, accelerate AI adoption through capability-led learning pathways, prioritise roles critical to future growth and track workforce readiness with confidence.
A connected skills supply chain ensures these insights continuously inform both development and execution, creating a workforce that is responsive, targeted and future ready.
AI transformation depends on skills visibility
Technology may enable AI transformation, but people determine whether it drives meaningful results. Without a transparent understanding of workforce capability, even the most ambitious AI strategy risks becoming costly and ineffective. Skills visibility is the differentiator between organisations that keep pace with AI and those that lead.
Organisations that invest in understanding their people, their current capabilities, their growth potential and how their skills align to business needs will be best positioned to unlock AI’s full value. A strategy built without skills visibility is not just incomplete; it is fundamentally blind.
Simon Ritter, Deputy CTO at Azul, on how the Java ecosystem is adapting (quickly) to the needs of AI applications
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If you ask most people in IT which programming language is most commonly used for AI applications, they’ll almost all answer Python. Which raises the obvious questions: why, and why not Java instead?
Python is actually older than Java by over 4 years, so its AI popularity is not because it is newer than other alternatives. Python’s origins have nothing to do with AI (or even numerically intensive applications). It was originally developed as a high-level scripting language for writing system utilities and applications on the Amoeba distributed operating system (which I doubt most people have even heard of). Python is also a poor choice for numerically intensive operations, which is a lot of what AI and LLMs need to do.
The real reason for Python’s popularity is that the mathematicians working on AI found it easy to learn. It could serve as a simple interface to high-performance computing libraries, typically written in complex low-level languages such as C and C++. This initial popularity led to many industry-standard frameworks, such as PyTorch and TensorFlow, being written in Python.
Does this mean Java has no place in AI?
Far from it, as was demonstrated recently by Azul’s AI4J online conference. This brought together nine AI proponents, most of whom are Java Champions, to present various ways Java can be used in this area.
The general theme that came across was that “Java owns the enterprise data”. Given Java’s enduring popularity for server-side applications, this is a statement that’s hard to dispute. This underscores the importance of Java when considering Retrieval Augmented Generation (RAG). Training an LLM uses a wide array of data and can deliver a model that is easy to communicate with in natural language. To change this from being smart to helpful (another quote from the conference), you need to access enterprise data in real time. The most valuable data for an organisation is contained in systems like those for ERP and CRM, as well as a plethora of databases. Most of that data is managed by Java-based applications.
What we end up with is the usual layered IT architecture, each layer providing an abstraction of the one below it. As was explained in one session, the LLM sits at the bottom, with vector search (or naïve RAG) above that and Graph RAG above that.
Java delivers powerful technologies
Java can deliver powerful technologies that fit well into this critical space. Instead of searching flat text files, a RAG system can use tools like Neo4j and Cyrock.AI to model complex relationships among concepts, people, and documents. This allows the AI to fetch deep, interconnected context that standard vector databases might miss. Enterprise Java application platforms like Jakarta EE and Spring are rapidly adapting to the needs of AI systems; Josh Long of Pivotal and James Ward of Amazon provided details of how Spring and AWS Bedrock can be used in this way.
Another significant takeaway from the AI4J sessions was the use of predictive, or analytical AI rather than the high-profile generative AI (or GenAI). One session quoted a report from Deloitte on the State of AI that said that “30% of GenAI projects will be abandoned due to a lack of clear business value”. Predictive AI has many benefits for an enterprise, not least of which is its deterministic quality. This means it does not hallucinate (which you really don’t want when making mission-critical, enterprise-wide decisions).
AI with context
Context for AI was another recurring theme in these sessions. Better coordination of input to an LLM-based AI system is critical to success. Providing that bridge from smart to helpful is what enterprise users need the most.
This, in turn, led to the conclusion that one of the most important aspects of any AI system is performance. In its over thirty-year history, Java has gone from a platform teased for being slow to one that, using techniques like just-in-time (JIT) compilation, can even outperform native C and C++ code.
The AI4J sessions discussed a number of ways that Java delivers optimum performance:
Project Panama for easier Java integration with lower-level C++ frameworks such as CUDA and ONNX Runtime.
Virtual threads, capable of delivering massively greater scalability for applications that spend a lot of time blocking, which is what AI applications do as they wait for tokens to be processed.
Project Babylon, which is exploring ways to target specific hardware architectures at runtime. Moving beyond JIT, this allows code to be targeted at GPUs or even FPGAs as and when necessary.
Azul’s Prime JVM that delivers lower latency and higher throughput through different garbage collection (GC) and JIT implementations. All whilst maintaining full adherence to the Java SE specification, making it a drop-in replacement for other JVMs. No code changes or recompilations required.
Overall, the AI4J conference provided a fascinating insight into how the Java ecosystem is adapting (quickly) to the needs of AI applications.
About Simon Ritter
Simon has been in the IT business since 1984 and holds a Bachelor of Science degree in Physics from Brunel University in the UK. Simon joined Sun Microsystems in 1996 and started working with Java technology from JDK 1.0; he has spent time working in both Java development and consultancy. Having moved to Oracle as part of the Sun acquisition, he managed the Java Evangelism team for the core Java platform. Now at Azul, he continues to help people understand Java as well as Azul’s JVM technologies and products. Simon has twice been awarded Java Rockstar status at JavaOne and is a Java Champion. He represents Azul on the Java SE Expert Group, OpenJDK Vulnerability Group and Adoptium Steering Committee. He is also the author of OpenJDK Migration for Dummies.
About Azul
Azul is the trusted leader in enterprise Java for today’s AI and cloud-first world. Its open source-based Java platform empowers organizations to optimize the entire Java lifecycle to accelerate performance, strengthen security, reduce licensing and cloud costs, and boost developer productivity. Azul powers mission-critical systems for 36% of the Fortune 100, 50% of the Forbes Top Ten World’s Most Valuable Brands, and the world’s top 10 financial trading companies. Learn more at azul.com and follow @azulsystems.
Oz Nicco-Annan, CFO at InfraPartners – the prefabricated data centre solutions specialist – on why developers should align deployment with evolving demand, while also adapting flexibly to keep pace with AI
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Behind the excitement of AI infrastructure, from hyperscale data centres to GPU compute mega-campuses, lies a growing concern. While long-term demand for AI is widely expected to grow, the shape and timing of that demand is still taking form. The market is shaped by rapid chip cycles, fluctuating pricing and shifting workload demands, and the old ‘build it and they will come’ approach has resurfaced. Only now, the stakes are far higher and there is an expectation to get more certain on when ‘they will come’.
Financial analysts are beginning to flag the tension between rapid AI infrastructure expansion and the pace at which demand is materialising. Some reports suggest AI infrastructure spend is outpacing realistic demand projections, and there is an increasing focus on how quickly demand can convert into committed, revenue-generating workloads.
Yet projections do point to strong long-term growth. While questions are emerging about who bears the risk if utilisation levels fall short, the answer is not to halt or slow the development of vital infrastructure. Instead, utilising more flexible and phased deployment models will enable operators to better align capacity with usage and meet AI ambitions. Therefore, infrastructure can be deployed, adapted and expanded in line with realistic, evolving workload requirements.
The demand complexities
There is a widely held assumption that demand for AI compute will be effectively unlimited. That if capacity is built, it will inevitably be used. This assumption is likely because projections point to sustained growth in training and inference workloads, with data centre capacity demand expected to grow by around 20–25% annually through 2030.
In reality, AI demand is complex and still evolving. Enterprise adoption remains uneven, many organisations are still in pilot (or experimentation phases) and not all workloads require large-scale, always-on infrastructure. Recent research shows that only around 20% of companies have scaled AI capabilities across their organisations, highlighting the gap between investment and real, sustained usage. At the same time, AI interest is undeniably accelerating, driven by rapid advances in generative models and increasing enterprise confidence. Most importantly, there is clear risk of over-cautious investment that could leave markets underprepared and unable to keep pace with national and commercial AI ambitions.
Speed versus commercial discipline
Historically, large data centre developments were underpinned by long-term agreements with hyperscalers or enterprise tenants before construction began. These commitments provided a clear route to secure revenue, reducing exposure to market volatility. Across the last few years, however, projects are increasingly moving forward while long-term customer commitments and workload requirements are still taking shape.
This shift in commercial models is partly driven by the speed of the market. The pressure to deliver capacity quickly has intensified, particularly as new entrants such as GPU-as-a-service providers compete alongside established hyperscalers. In this fast-paced environment, the ability to deploy infrastructure rapidly can outweigh the discipline of securing customers in advance. But this comes with risk that needs addressing.
Without committed users, developers are exposed to two key challenges. First, the utilisation risk; the possibility that capacity cannot be filled. Yet this is only likely if the facility is designed around specific architectures or hardware configurations that do not align with actual customer requirements. Where data centres were once built to operate for 10 to 15 years with minimal change, operators are now dealing with hardware refresh cycles closer to three to five years. The right technology for today’s infrastructure may not be fit for tomorrow.
The second challenge is slower-than-expected customer adoption. Capacity may ultimately be utilised, but not at the pace required to support the investment case. This issue can extend timelines, delaying revenue generation and placing pressure on project economics. Delayed customer adoption, whether due to economic conditions, regulatory constraints or shifts in AI development priorities, can leave newly built facilities underutilised for extended periods.
Rethinking deployment models
How does the industry respond to these risks? In short, we must rethink how infrastructure gets deployed. Instead of committing to large, monolithic builds, there is a growing case for more upgradeable and phased approaches. Upgradeable data centres, built offsite with advanced manufacturing processes, allow operators to align capacity deployment more closely with confirmed demand. This technique reduces the risk of overbuilding; organisations can start small and add capacity as required, rather than building for a future that may not play out in the way expected. Delayed customer adoption suddenly becomes less of a concern. Critically, upgrades can also be performed while the rest of the site remains operational, avoiding prolonged downtime and revenue loss.
As AI hardware evolves with increasing power densities and shifting cooling requirements, this upgradability also allows infrastructure to adapt and remain relevant as technology changes unfold. AI is evolving much too fast for rigid designs and, instead, flexibility should be built in to ensure this infrastructure can cater for the needs of tomorrow. In other words, we should be manufacturing building blocks that have the capability to evolve and scale, therefore reducing risk around utilisation. Rather than designing and building an AI factory that works only for the hardware we know, developers should be looking at power and structural systems that can scale to higher densities, or support a transition to new cooling methods, without the need for a full re-build or retro fit.
From a financial perspective, this level of upgradability means the model can shift from large upfront capital commitments towards more incremental investment. Developers can then scale in line with both technological developments and customer demand.
Demand-led infrastructure
How effectively capacity is matched to real, sustained demand will be crucial in the coming years. The risk of building without guaranteed users is not new, but the scale at which it is now occurring is. The industry must quickly learn the art of balancing speed with discipline.
Developers should align deployment with evolving demand, while also adapting flexibly. Understanding who is committed to using these facilities is critical to determining the sustainability of the AI boom. But it’s also key that we design facilities not just for today’s technology, but for tomorrow’s unknowns, ensuring they can evolve at the same pace as AI itself.
Cien Solon, CEO & Co-Founder of LaunchLemonade, on why the future of financial services will be shaped by those who can balance AI innovation with responsibility
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Every industry is racing to implement artificial intelligence. In financial services and other heavily regulated sectors, that race has been cautious. From automated underwriting and fraud detection to hyper-personalised customer experiences, AI offers FinTech companies unprecedented opportunities to innovate and scale. Yet alongside this promise comes the growing weight of regulation.
Different approaches to AI governance are already taking shape. In Europe, the EU is introducing a structured, risk-tiered framework that sets clear obligations depending on how AI is used. The UK is pursuing a more flexible, principles-based approach, allowing individual regulators to apply guidance tailored to their sectors. The US has a more decentralised model, blending federal direction with a growing patchwork of state-level rules. For FinTech founders operating across borders, this diversity can feel complex and, at times, overwhelming.
With these frameworks emerging, many fintech founders are beginning to hesitate. Questions around compliance costs, legal complexity and potential liability are causing some to reconsider or delay AI adoption altogether. For smaller fintech firms and scale-ups, the concern feels especially acute. Is the risk worth the reward?
It is. And founders who treat regulation as a strategic advantage, rather than a barrier, will be the ones who pull ahead.
Regulation as a Trust Multiplier
In financial services, trust is everything. Customers entrust FinTech platforms with their most sensitive data and critical financial decisions. Investors and partners demand reliability, transparency and accountability. Regulators expect firms to operate with integrity and control.
Robust AI governance can enhance trust across all of these stakeholders. FinTech companies that proactively adopt transparent and responsible AI practices signal maturity beyond their size. They demonstrate operational discipline alongside technical capability.
This matters especially now, when AI systems can appear opaque or unpredictable. Clearly communicating how AI is used, what decisions it informs, what data it relies on, and how risks are mitigated allows fintechs to differentiate themselves in a crowded market.
Compliance, when done well, builds credibility. And credibility, in financial services, is a powerful competitive advantage.
The Myth of ‘Compliance Paralysis‘
One of the most common misconceptions among FinTech SMEs is that AI regulation requires building entirely new compliance infrastructures from scratch. This belief often leads to what I call “compliance paralysis,” a reluctance to act because the problem feels too big to start.
In reality, much of AI governance maps directly onto frameworks that fintech companies already use. Risk management, model validation, data protection, auditability. These are familiar disciplines. AI extends them into new domains, but the foundations are already there.
Take model risk management. The steps a compliance team follows to validate a human-led underwriting process can be adapted to validate an AI-driven one. The logic is the same. The documentation requirements are the same. What changes is the technology performing the task, and the speed at which it operates.
I see this with our own clients. Regulated businesses assume they need to start from zero when it comes to AI governance. When we walk them through what they already have in place, the gap between current practices and regulatory expectations is almost always smaller than they feared.
The Agility Advantage of FinTech SMEs
Large banks may have dedicated compliance teams and significant legal budgets, but they also carry the weight of legacy systems. Retrofitting AI governance into complex, decades-old infrastructure is both costly and time-consuming.
Fintech SMEs have the advantage of agility. They can design and implement responsible AI practices from the ground up, embedding governance directly into their products and processes from day one.
This is exactly how we built LaunchLemonade. Every AI agent on our platform operates within a governance framework by default. Audit trails, data handling rules, transparency requirements. They are baked into the architecture, because bolting them on later is always harder and more expensive.
This compliant approach delivers real advantages. Long-term costs stay lower because governance is structural rather than retrofitted. Adapting to evolving regulatory requirements becomes faster because the foundations are already solid. And AI-enabled products reach the market ready to meet governance standards, rather than needing months of remediation after launch.
Regulation, in this sense, levels the playing field. Larger incumbents may have more resources, but smaller firms can move faster and design smarter. Those that embrace this will outpace competitors who delay.
Practical Steps for Leaders
For finance leaders looking to navigate AI regulation without overwhelming their teams, the key is to focus on pragmatic, high-impact actions.
Start by classifying AI risk early. A chatbot handling customer queries carries a very different risk profile from an algorithm making credit decisions. Understanding where your use cases sit on the risk spectrum allows you to prioritise governance efforts where they actually matter.
Be transparent by default. Inform users when AI is being used, explain its role in decision-making, and provide avenues for human review where appropriate. Clear communication builds trust and reduces regulatory friction.
Leverage what you already have. Map AI governance onto your existing risk and compliance processes rather than building a parallel system. This reduces duplication and accelerates implementation.
Build lightweight governance structures. You do not need a large compliance team to implement effective oversight. Documenting model assumptions, maintaining audit trails and assigning internal accountability can go a long way. At LaunchLemonade, we see regulated SMEs achieve meaningful governance with lean teams precisely because they focus on these fundamentals.
And know when to bring in expert input. Legal and regulatory expertise is valuable, but it does not need to be engaged at every step. Focus external support on high-risk use cases or areas of genuine uncertainty.
The Cost of Standing Still
If financial SMEs retreat from AI because of regulatory anxiety, innovation will not stop. It will simply concentrate in the hands of large incumbents and major technology companies with the resources to navigate complex compliance landscapes.
That concentration has broader implications. Competition reduces. Innovation cycles slow. Diversity in financial products and services narrows. Opportunities to improve financial inclusion and accessibility are missed.
Fintech and finserve has historically been a driver of disruption, challenging traditional models and expanding access to financial services. AI represents the next wave of that transformation. If smaller players step back now, they risk ceding that ground entirely.
Regulation as an Enabler
The conversation around AI regulation in financial services needs a shift in perspective. Regulation creates the guardrails that allow new technologies to scale safely and sustainably. It builds the trust required for widespread adoption. And it ensures that innovation benefits broadly, across industries and communities, rather than being concentrated among those who can afford to figure it out alone.
For fintech and finserve founders, the real question is how to innovate responsibly and strategically. Those who embrace that question will find that regulation is part of the answer.
AI is too important an opportunity for FinTech to ignore. Regulatory frameworks may seem daunting, but they are neither insurmountable nor inherently restrictive. When approached thoughtfully, they strengthen trust, streamline operations and unlock new avenues for growth.
Financial SMEs are uniquely positioned to lead in this space. Their agility, focus and ability to embed governance from the outset give them a distinct edge over larger, slower-moving competitors. The future of financial services will be shaped by those who can balance innovation with responsibility. The firms that recognise this will define the age of AI, rather than simply surviving it.
Cien Solon is the CEO and Founder of LaunchLemonade, a secure governed platform of AI agents for regulated industries. An experienced AI transformation leader, Cien has been building AI-powered solutions since 2018 and working with generative AI since 2022. Her mission is to ensure that smaller businesses in regulated sectors can adopt AI confidently, without the enterprise price tag. LaunchLemonade was included in Entrepreneur UK’s Top 100 Startups to Watch, and Cien was shortlisted in the Technology category of the 2025 Investec Early-Stage Entrepreneur of the Year Awards.
Sam Hill, Investment Analyst at TDK Ventures tells us why electrical transformers must evolve to enable them to deal with the power demands of the AI age.
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The electrical transformer is one of the 19th century’s most enduring engineering achievements. Designed to step voltage up or down, conventional iron-core transformers have been the backbone of our power grids for over a century. As a testament to their ingenuity, the technology has remained largely unchanged in its fundamentals, consistently facilitating power delivery to cities, factories and data centres alike.
However, as the demands on the grid fundamentally shift, so too must the transformer. The boom in demand for energy infrastructure – driven by renewables, BESS proliferation, electrification, and more recently, the massive requirements of AI compute – has placed the conventional transformer at the centre of an emerging bottleneck.
Inherently passive, these devices cannot adapt to the bidirectional power flows of distributed solar, nor can they respond to real-time fault conditions on a dynamic grid. Modern applications, such as data centres and megawatt-scale charging facilities, demand significantly higher power densities and efficiencies, creating a growing mismatch with legacy infrastructure and necessitating eventual replacement of the installed base. The U.S. grid alone hosts an estimated 60 to 80 million distribution transformers today; well over half of these units are more than 35 years old and are rapidly approaching the end of their design life.
Despite burgeoning demand, supply is failing to scale. Lead times for manufacturing, fabrication and delivery now exceed 24 months, while prices in certain categories have risen as much as ninefold. Even as lead times extend, incumbent manufacturers have remained reluctant to expand capacity, concerned with geopolitical and policy uncertainty, questions over the sustainability of demand growth and structural incentives to maintain constrained supply. Ultimately, the conventional transformer is becoming the grid’s most consequential bottleneck.
Solid state transformers for expanded capability
The solid-state transformer (SST) is emerging to solve this bottleneck. While a conventional transformer relies on copper windings around a laminated ferrous metal core to inductively transfer energy, an SST performs the same voltage conversion using high-frequency power semiconductors. Often based on silicon carbide, these semiconductors switch thousands of times per second, resulting in a device that is smaller and lighter, yet far more efficient and robust.
Power flow is actively controlled by software at all times. This enables SSTs to function as dynamic, programmable power platforms rather than passive components. The SST can accept and deliver both AC and DC inputs and outputs across a range of voltages, each reconfigurable within the same device. This level of control means SSTs can absorb the functions of several conventional components – step-down transformers, UPS systems, protection switchgear and power factor correction banks – into a single integrated platform.
Because SSTs are built from semiconductor components rather than wound copper and electrical steel, their manufacturing lead times align with electronics supply chains instead of the volatile commodity metal markets. Consequently, their cost trajectory follows semiconductor learning curves rather than commodity price cycles. Furthermore, modular SST architectures can handle failures through redundancy and be repaired in minutes through hot swapping of individual power stages, bringing significant reliability benefits to critical infrastructure.
SSTs on the critical path for data centres
The most immediate and commercially urgent application for SSTs is the AI data centre. Power architectures underpinning today’s hyperscale facilities were originally designed for rack densities measured in single-digit kilowatts. However, NVIDIA’s latest GPU clusters demand hundreds of kilowatts per rack — a trajectory pointing toward megawatt-scale densities within the decade.
The industry has rapidly coalesced around DC bus architectures as the solution, such as the 800 VDC architecture proposed by NVIDIA. These designs bring medium-voltage power closer to the rack, reducing copper runs, cutting resistive losses and improving end-to-end efficiency margins.
In this shift to DC, SSTs are becoming a key building block. Only a solid-state platform can directly convert medium-voltage grid power to 800V DC while simultaneously integrating backup energy storage. This eliminates the need for UPS rooms, grey-space switchgears and step-down transformer banks that currently consume a significant share of a data centre’s physical footprint.
These efficiency gains, often spanning multiple percentage points, directly expand the “compute-per-megawatt” available within a fixed power envelope – a metric that now drives decisions from board-level components through to the siting of entire campuses. Furthermore, SSTs accelerate deployment speeds in the race to bring compute capacity online, as they benefit from both structurally shorter lead times and the consolidation of multiple pieces of equipment in the powertrain into a single unit.
Not if but when
Historically, the industry has questioned whether the technology has been ready. That question has now largely been answered. Power electronics, particularly silicon carbide devices, have matured dramatically over the past decade, driven largely by the EV and solar industries’ demand for higher voltage, high-efficiency inverters. These sectors have cultivated a new generation of power electronics talent – engineers with deep experience in translating power electronics technology advances into commercially viable products. SSTs will be deployed in 2026.
Simultaneously, the supply crunch in the conventional transformer market is creating a durable market opening. The massive AI infrastructure build-out is creating a class of motivated customers willing to support first-wave commercial deployments at scale. With DC architectures now squarely on the industry roadmap, SSTs have moved onto the critical path for AI. As these products take shape and real data is generated in pilots, customers can quantify a compelling business case, demonstrating that the cost of this new technology is justified.
The benefits of SSTs, first realised in data centres, will soon extend to a wider array of transformer applications. For high-power EV charging sites, where construction timelines and physical footprints often constrain site development, SSTs offer higher power density and shorter build cycles than conventional electrical infrastructure. On the distribution grid, replacing aging transformers with SSTs would introduce capabilities such as active voltage control, automatic fault isolation, phase balancing, and reactive power support. This would enable a more distributed, dynamic, and software-defined grid while enabling utilities to extract substantially more kilowatt-hours from existing poles and wires. As early adopters such as data centres drive SSTs down the cost curve, these mass-market utility applications will become increasingly viable.
In short, the commercial moment for SSTs has arrived. In recognition of this, Amperesand (a TDK Ventures portfolio company) raised an $80 million Series A in late 2025. The company is preparing to deploy 30 MW of medium-voltage SSTs with hyperscale and critical power customers in 2026, ahead of a projected volume ramp in 2027. Their value proposition is stark: an 80% reduction in electrical footprint, a 50% cut in installation labour and a 10x acceleration in time-to-power compared to conventional methods. Meanwhile, challengers like Heron Power and DG Matrix have collectively raised >$200m to date and incumbents across the ecosystem, including heavyweights like Infineon, Delta, GE Vernova and Eaton, are actively developing solutions to meet the clear market signal for SSTs.
Solid-state transformers are one of the essential building blocks needed to overcome the power wall AI is currently facing. The companies that combine technical performance with scalable manufacturing and system-level integration will define the category. Those that arrive at scale first will shape the power architecture of the AI era.
Now the hype is settling, 2026 is the year organisations must prove AI will deliver ROI. Matt Fuller, Co-founder and Vice President of AI/ML Products at Starburst, explains why measurement is only meaningful if organisations embed AI into core business processes and workflows.
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Over the past 18 months, many organisations have experimented with AI through pilots and isolated initiatives. While those experiments have generated excitement and much speculation, they have also created a measurement challenge. The question is no longer whether AI works, but whether it will deliver the measurable return on the significant investments companies have made in data infrastructure, skills and systems.
Today, AI’s impact is often assessed through activity metrics – usage rates, prompts generated, or time saved – rather than whether it improves business performance. Yet, activity does not equal impact. If organisations want to measure AI’s real contribution, they must anchor it to business outcomes that define competitive position: win rate, customer retention, time-to-market, risk exposure, cost per case, or revenue growth. Therefore, measuring AI meaningfully requires a shift in thinking – from treating AI as a standalone tool to embedding it directly into how the business operates.
Redesigning workflows for the AI era
The main reason AI initiatives fail to scale and, subsequently, prove business value is that they are introduced into existing processes. AI tends to be added as a “bolt-on” step in a legacy workflow rather than being part of the redesigned process.
To unlock value, organisations must therefore rethink and redesign end-to-end workflows around AI rather than inserting it after the fact. I believe that the real opportunity lies in improving decision-making across the whole process – from data architecture to insight generation to operation execution.
When AI is embedded in business operations from the start, its impact becomes measurable through outcomes such as faster product launches, improved forecasting accuracy, reduced operational losses, or stronger customer retention. However, redesigning workflows in this way quickly exposes another challenge: ensuring reliable access to trusted enterprise data.
Building the data foundation for scalable AI
As I’ve outlined, AI pilots often rely on fragmented, project-based datasets, where the data often sit outside of the organisation’s data architecture. While those datasets may be useful for experimentation, they rarely provide the reliability or governance required for enterprise-wide deployment, compromising scalability.
To embed AI into core business processes, organisations must therefore adopt a ‘data product’ mindset. This means creating curated, domain-owned datasets with clear ownership, quality standards and built-in governance. The data products then become reusable assets capable of supporting analytics, operational systems and AI models across the organisation.
It is only when AI operates on trusted data products, instead of one-off extracts, that it can be reliably integrated into operational workflows, scaled across the enterprise and measured meaningfully. At that point, AI stops being an experimental capability and becomes a business asset.
But getting to that point requires another shift to happen. Data must be treated as a governed, enterprise-wide strategic asset rather than a by-product of IT systems or a collection of disconnected silos. The shift here isn’t about better AI models; it’s about building a unified data foundation that enables AI to drive durable competitive advantage.
Scaling up security in the AI era
As organisations start embedding AI into decision-making processes, data governance becomes critical. Scaling AI without robust upstream governance structures introduces significant legal, operational and reputational risks. Before AI influences business-critical decisions, it is imperative that organisations can ensure clear data ownership, enforceable access controls, lineage visibility, and compliance with regional and regulatory requirements.
Importantly, governance cannot simply exist as policy documentation. It must be technically enforced across the entire data estate so that organisations can confidently scale AI while maintaining control over how and what data is accessed and used. Without that level of governance, AI risks amplifying existing data fragmentation and compliance challenges rather than delivering enterprise value.
People first, every time
AI adoption also raises the important question of the relationship between human expertise and machine intelligence. Much of an organisation’s competitive advantage exists not in data, but in tribal knowledge held by humans – judgment, context and business understanding built over years of activity.
AI systems are only as strong as the data and the context they are given. While organisations should progressively codify that institutional knowledge into governed data products, metadata and documented business rules, we are still far from a point where AI can fully replicate that depth of human expertise.
Today, AI is enhancing human capabilities rather than replacing them – surfacing insights, accelerating analysis and providing decision support – while experts remain in the loop to validate, refine, apply judgment and ensure decisions reflect the broader business context.
As institutional knowledge becomes more structured and accessible, AI’s role will naturally expand. But in the near term, as I’ve outlined, the most successful organisations will be those that use AI to augment their workforce rather than attempt to automate expertise prematurely.
From experimentation to enterprise value
Organisations are now moving beyond AI experimentation, shifting the focus from novelty to measurable business impact. Achieving true AI business value requires embedding AI into how the business actually operates – supported by trusted data foundations, strong governance and workflows redesigned around better decision-making. It is my view, then, that AI should ultimately be judged not by how often it’s used, but by whether it delivers meaningful business results.
ByMatt Fuller, Co-founder and Vice President of AI/ML Products at Starburst.
CTOs have piled into digital AI. The next advantage lies in connecting it to the real world, argues Nick Thompson, co‑founder and CEO of OLO Robotics
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Spend any time with CTOs and the chat is almost always about AI. Copilots, chatbots, analytics pipelines, document understanding – the digital side of the business is getting plenty of attention. But in most of those conversations the AI stops at the edge of the screen while the warehouse, factory floor and inspection bay still run much as they were a decade ago.
Physical automation has always existed. But it has sat behind a wall of specialist expertise, capital and long lead times that most IT and software teams could not see past. That wall is now coming down. The question for technology leaders is whether they treat that change as part of their AI strategy, or leave it as somebody else’s problem.
From niche tech to mainstream platform
My background is in software rather than robotics. After two decades building software teams and technology businesses, I started to look more closely at the intersection of AI and physical automation. What I found was familiar: a specialist domain starting to look like a platform that generalist development teams can use.
Cloud computing is the obvious precedent. Before cloud, running infrastructure meant capital expenditure, specialist skills and lengthy procurement cycles. The arrival of platforms such as AWS, Azure and Google Cloud abstracted that complexity into services that a small team could consume in hours rather than months. The systems did not become simpler; they became accessible through a different model.
Robotics is moving in the same direction. ROS2, the standard framework for programming industrial robots, is powerful and open source but difficult to learn. Making progress has meant understanding a large stack of concepts and tools, which has kept robotics in the hands of specialist roboticists who are in short supply. For many organisations, that has been enough to keep robots on the ‘future plans’ slide.
But recently, platforms have emerged on top of ROS2 that provide browser‑based environments, integrated simulation and SDKs in common languages. Instead of assembling their own mix of simulators, dashboards and custom scripts, development teams can work in one place and treat robots as just another class of endpoint in the architecture. These capabilities are already in production; what is lagging is recognising them as part of mainstream AI and automation strategy.
The robot team you already employ
As perception and planning increasingly rely on AI models, the bottleneck in robotics is shifting away from low‑level control towards data management, orchestration and integration – areas where existing software and IT teams are already experienced.
A developer can now log into a web‑based environment, spin up a simulated industrial robot and experiment with behaviours before any hardware is ordered. Large language models can generate ROS2 code from natural‑language descriptions. Developers review that code, run it in simulation and refine it as they would any other component. The code sits in the same repositories, goes through the same review processes and is deployed through the same pipelines as other software.
This changes who can own automation strategy. Rather than building a separate robotics function, organisations can draw on engineers who already understand their systems and data, and treat robots as part of the same stack rather than as isolated projects.
Rethinking how you buy robots
Traditionally, each robotics deployment has been treated as a bespoke project. You hire specialist roboticists, engage systems integrators, commit capital to hardware and integration, then discover over time whether the automation meets expectations. It is a familiar process for anyone who remembers pre‑cloud infrastructure projects.
A platform‑based approach alters both the economics and the sequence of decisions. Development starts in simulation. Use cases are validated before hardware is purchased. Organisations can run small pilots, discard the ones that do not deliver and scale up the ones that do. Robots become standardised endpoints running defined behaviours, rather than one‑off builds.
Consumption models are evolving too. Some providers now offer robot‑as‑a‑service, where customers pay for hours of operation instead of owning the asset outright. Combined with sim‑first development, this changes the risk profile of automation projects in a way that mirrors the move from owned hardware to cloud computing.
For IT leaders who lived through cloud procurement debates – build versus buy, capex versus opex, open versus proprietary – these patterns should be recognisable. Organisations that engaged with cloud early, in a measured way, gained advantages that compounded over time. The same potential exists in physical automation.
The ‘last mile’ of your AI projects
The highest‑profile robotics deployments tend to be in warehousing and logistics, where autonomous mobile robots move goods between zones and robotic arms pick from shelves. But for many organisations, the more immediate opportunities sit at the boundary between digital programmes and the physical environment.
For predictive maintenance, for example, many manufacturers have invested in sensors, data pipelines and AI models to forecast equipment failures. In practice, these initiatives often culminate in dashboards and alerts on control‑room screens. When an alert sounds, a technician still walks the floor, inspects the asset and performs a standard intervention. Detection has been automated; the response remains manual.
Robots can handle parts of that response. A mobile platform equipped with a camera and basic tooling can be dispatched to a machine, collect visual or sensor data and carry out a simple inspection or reset. The workflow from model output to physical action can be orchestrated by the same software teams that built the predictive maintenance system.
A similar story appears in warehouse operations. Many facilities now use AI‑driven demand forecasting and sophisticated warehouse management systems to decide what to pick, when and to where. Execution can still often be manual however, with staff walking long distances to carry out those decisions. Autonomous mobile robots and robotic picking systems can take the instructions those systems already generate and turn them into physical movement on the warehouse floor.
Organisations have digitised much of the data layer through sensors, telemetry and analytics; but the connection to consistent physical action is still developing. In sectors facing acute labour shortages such as warehouses, ports, construction, brownfield manufacturing and field service, combining human expertise with robotic assistance is becoming a practical response to current constraints.
A strategic choice, not a technical one
Every significant technology shift creates a period in which early adopters can build advantages that are difficult to replicate later. Cloud, mobile and data platforms all followed that pattern. Organisations that engaged early, took time to understand new models and built internal capability now operate differently as a result.
Physical automation is entering that phase. The factors that kept robotics at arm’s length from mainstream IT – the need for scarce specialist skills, the capital intensity of hardware purchases, the complexity of bespoke integration – are being reduced by open frameworks, platform layers, simulation environments and new consumption models.
For CTOs, the question is not whether physical systems will become part of IT strategy, but when and on what terms. If competitors are quicker to connect their AI investments to physical systems, closing the loop from predictive maintenance alerts to automated inspection, or from demand forecasts to in‑facility logistics, they will accumulate operational advantages over time.
The practical move is to treat physical automation the way you would treat any emerging technology. Understand the procurement models. Run a contained pilot that links one existing digital programme to a simple physical workflow. Build internal capability in the teams who already know your systems. If you are still treating robotics as a one‑off patch at the edge of operations, that is now a choice, not a technical inevitability.
Nick Thompson is co‑founder and CEO of Sheffield-based OLO Robotics.
Following the launch of its Core Modernisation Playbook, Publicis Sapient’s Dave Murphy, Head of Financial Services – EMEA & APAC, explores why so many banks struggle to modernise despite investing heavily in AI. The challenge is no longer knowing what to transform, but how to execute transformation safely
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Most banks have spent the last decade applying AI where its impact is most visible, in chatbots, fraud scoring and personalised product recommendations. The results have been real, and in places significant, however, they have also been contained. AI has been working at the edges of the organisation, improving the surfaces that customers and compliance teams interact with, while the actual machinery of the bank carries on untouched. The core systems, the batch processing logic, the settlement rules written in COBOL three decades ago, have continued to run exactly as they always have, insulated from the transformation happening around them.
Meeting the AI Execution Challenge
That insulation is starting to break down. For CTOs weighing where AI investment should go next, the implications are considerable. The question is no longer whether banks need to modernise. Every major institution already knows it needs real-time payments, AI-ready operations, API-enabled architectures and cleaner data foundations. The challenge is execution. How do you modernise decades of critical systems without introducing unacceptable operational risk?
That is where AI is beginning to change the equation. Rather than focusing solely on customer experience or analytics, it is being applied to the engineering process itself. The discovery, extraction, transformation and validation work that has traditionally been the slowest, riskiest and most expensive phase of core modernisation is becoming increasingly automated. This is where the greatest opportunity now exists.
The Core Modernisation Playbook
Publicis Sapient explores this shift in its recent Core Modernisation Playbook. At the centre of that approach is Sapient Slingshot, a platform designed to apply AI across the engineering lifecycle rather than simply the coding stage. Slingshot applies AI across the entire modernisation lifecycle, from understanding legacy systems through to transformation and continuous validation within a governed workflow. The objective is not simply to write code faster. It is to make large-scale modernisation more predictable, auditable and easier to execute.
One global bank recently used this approach to analyse three million lines of COBOL and produce verified functional specifications in eight weeks, work that would previously have taken well over a year and required scarce legacy expertise that is becoming increasingly difficult to find. What changed was not the strategy or destination. It was the execution model.
Core banking code written over thirty or forty years rarely comes with a user manual. Business rules become embedded directly in software, operational workarounds accumulate over time, and critical knowledge often exists only in the minds of engineers who built the systems. As those engineers retire, banks are not simply losing people. They are losing the institutional knowledge needed to modernise safely.
This reflects a broader shift in how modernisation is delivered. Instead of treating discovery, code generation and testing as separate phases owned by different teams, they become part of a connected execution model. Understanding legacy systems, building modern services and validating every change happen as part of the same continuous process. That reduces risk while improving speed, which is exactly what heavily regulated organisations require.
AI-Powered Business
AI-powered business rule extraction is a direct response to that problem. Rather than depending on interviews with legacy experts and time-consuming manual code walkthroughs, automated analysis can surface the logic embedded in legacy programs, cross-reference it against actual processing behaviour, and generate specifications that engineers who’ve never touched the original COBOL can read and act on. In the case above, specification accuracy came in at 95%, and the time needed to analyse individual batch feeds dropped from 35 days to five. That compression changes the shape of what a modernisation programme looks like in its early phases. Work that used to occupy teams for the better part of two years can be substantially completed in months, and what comes out the other end isn’t an informal summary but a set of audit-ready specifications and, in this instance, more than 200 implementation-ready backlog items that become the foundation for everything that follows.
Establishing Strong Foundations For AI
Foundations matter because the validation phase of modernisation is where most programmes break down. There’s no shortage of ambition or strategic clarity in banking transformation. Most major institutions have roadmaps, target architectures and detailed business cases. What consistently falls apart is proving, with sufficient rigour, that a rewritten system behaves identically to the one it’s replacing.
Core banking processes handle exceptions, regulatory edge cases and accumulated business logic in ways that are difficult to fully enumerate up front, let alone test exhaustively by hand. Traditional testing models depend heavily on manually created test cases, subject-matter-expert review and long validation cycles. Coverage gaps are hard to detect, particularly when the legacy behaviour they’re meant to validate against is poorly documented in the first place. Banks end up completing the development work and then getting stuck proving the new system does what it’s supposed to.
AI-driven testing changes that equation by generating and executing test cases at a scale manual teams can’t match, covering standard transactions, edge cases and downstream dependencies systematically rather than sampling them. That doesn’t remove the need for human judgement on what ‘correct’ looks like, but, it does remove the bottleneck of generating enough coverage to have confidence in the answer.
Modernisation Strategy
None of this means banks can modernise the way digital natives do. Core banking platforms sit inside dense ecosystems of payments networks, risk engines, regulatory reporting systems and third-party providers. A change in one system can create consequences dozens of steps downstream, and banks must modernise around uptime, auditability and customer trust in a way a retailer redesigning a checkout flow simply doesn’t. That’s precisely why execution has become the defining challenge. Every bank already knows what it wants to build. The competitive advantage now lies in how effectively it modernises the foundations that make those ambitions possible.
Sapient Slingshot – Deploying An Execution Engine
What AI applied to the engineering process offers is a way to close that gap without pretending the underlying complexity has gone away. Understanding what a legacy system actually does, generating modern code and architecture that preserves the business behaviour underneath it, and validating the result continuously rather than at the end of a multi-year build: these are the three things that have historically made core modernisation slow, expensive and prone to stalling.
Applying AI across the engineering lifecycle does not remove the need for governance, auditability or human judgement. It strengthens them by making documentation, traceability and validation part of the execution process rather than activities completed afterwards. This is where platforms such as Sapient Slingshot are emerging as execution engines for modernisation, enabling banks to modernise with greater speed, control and confidence rather than simply generating code more quickly.
For CTOs, the practical takeaway is less about any single tool and more about where in the lifecycle AI is being pointed. The customer-facing layer of banking has benefited from a decade of AI investment. The engine room is only just beginning. The banks that move first will not simply modernise faster. They will build the AI-ready foundations that determine how quickly every future innovation can be delivered.
The Core Modernisation Playbook
Get your copy of the latest White Paper from Publicis Sapient here
Nicholas Batten, Co-founder and CTO at Nuumad explains how a combination of probabilistic and deterministic AI can help pharmacies adapt for the future.
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As funding pressures on the NHS grow, community pharmacies are reaching a turning point. What used to focus mainly on dispensing medicines is evolving into a more patient facing healthcare network. But this shift is not guaranteed. Without the right technology in place, pharmacies may struggle to keep up with rising demand, regulatory requirements and increasingly complex patient needs.
The conversation around artificial intelligence (AI) has, to date, been dominated by hype. In healthcare particularly, this is problematic. Clinical environments are highly regulated systems where safety, traceability and accountability are non negotiables. The real opportunity, therefore, is not AI adoption, but understanding how intelligent systems, both probabilistic and deterministic, can be deployed responsibly to scale care without compromising compliance or cybersecurity.
AI as a force multiplier, not a replacement
One of the most persistent misconceptions is that AI will replace clinical expertise. In practice, the opposite is true. Across progressive pharmacy networks, intelligent systems are being deployed instead to augment healthcare professionals.
AI, when used appropriately, acts as a force multiplier. It can assist with triage, summarise patient inputs, support protocol adherence and reduce administrative burden. This allows pharmacists to focus their attention where it matters most: clinical judgement and patient interaction.
However, it is critical to recognise that not all intelligence in healthcare needs to be AI driven. In many cases, structured workflows, exact data matching and rules based logic can deliver similar operational gains with greater predictability, reducing the need for human checks. For technology leaders, this distinction is essential. The goal is to optimise system design as opposed to AI usage.
In clinical settings, augmentation is the defining principle, not automation. Human oversight remains central, but it is enhanced by systems that increase throughput, reduce error and standardise care delivery.
Deterministic systems vs probabilistic models
A key strategic decision when deploying intelligent infrastructure in healthcare is choosing where AI is appropriate and where it is not.
Probabilistic AI models, such as large language models, excel in handling unstructured data and generating flexible outputs, at the same time they also introduce uncertainty. Even with high accuracy, their non deterministic nature can create challenges around explainability, auditability and regulatory approval.
By contrast, deterministic logic systems operate on predefined rules and structured data. Every decision pathway is traceable, reproducible and auditable – delivering a clear advantage in highly regulated environments such as pharmacy consultations, especially when paired with an intuitive, best in class user experience.
This is not an argument against AI, but rather in favour of balance. In practice, the most effective architectures combine both approaches: AI at the edges to enhance usability and efficiency, and deterministic systems at the core to guarantee compliance and safety.
For example, AI can help organise patient inputs or highlight relevant information, while final decisions are handled by rule based systems aligned with clinical protocols. This hybrid approach enables organisations to benefit from AI while keeping risk under control.
Beyond individual technologies, the real transformation lies in how systems are orchestrated.
Modern consultation platforms have become dynamic orchestration layers that manage the entire patient journey. From pre-consultation risk assessment and structured symptom capture, to real-time clinical support and automated follow-up, these systems create a continuous, data-driven workflow.
This orchestration reduces fragmentation. Pharmacists no longer need to navigate multiple disconnected systems or manually reconcile information. Instead, they operate within a unified environment where clinical protocols, patient data and decision support are seamlessly integrated.
The impact is significant. Consultations become faster and more consistent, training time for new staff is reduced, and perhaps most importantly, the standard of care becomes repeatable across locations with more empowered healthcare professionals.
Scale can now come into play. Pharmacies can expand private services such as travel health, weight management or preventative care without proportionally increasing operational complexity or risk – avoiding the additional cost and resources typically required to manage and mitigate these challenges on an ongoing basis.
Structured data is the foundation of this model. By capturing patient information in a consistent, machine-readable format, pharmacies can unlock automation, reporting and continuous improvement, all while maintaining compliance with regulatory requirements.
Balancing innovation with regulatory responsibility
The UK regulatory landscape for AI in healthcare is still evolving, with frameworks being shaped by bodies such as the MHRA (Medicines & Healthcare Products Regulatory Agency), creating both opportunity and uncertainty. Organisations that move too slowly risk falling behind and those that move too quickly risk non compliance.
Navigating this requires a disciplined approach to innovation where every system, whether AI-powered or not, must be clinically validated, fully auditable and aligned with existing healthcare regulations. Transparency is critical as if a system cannot clearly explain how it reaches a decision, it is unlikely to meet the standards required for clinical deployment.
Equally, leaders must resist the temptation to prioritise cost or speed over safety. In healthcare, the consequences of failure extend beyond financial penalties and can impact clinicians trust, organisational reputation and mostly importantly patient outcomes.
Responsible digital transformation, therefore, is about making deliberate, informed decisions about where technology adds value and where it introduces risk.
The intelligent pharmacy era
Pharmacies are no longer just dispensing centres, they are becoming decentralised healthcare providers. Intelligent systems are enabling this shift, but success depends on how they are implemented.
AI has a role to play, particularly in enhancing efficiency and user experience. But it is only one component of a broader ecosystem that includes deterministic logic, structured data, secure infrastructure and workflow orchestration.
For technology leaders, the challenge now is to build systems that scale and can be trusted at the same time. The technology is already available. What matters now is leadership and choosing the right architectural approaches, embedding governance from day one and recognising that true innovation in healthcare is measured by reliability, safety, and impact.
Simon Hayward, GM & VP Sales International at Freshworks explains why AI needs to work effectively for mid-sized organisations to truly succeed.
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Determining the impact AI will have on the future of enterprises lies in the hands of the global mid-market sector. Sitting between SMEs and large corporations with revenues between £25m and £500m – mid-market businesses employ 40% of the global workforce and are vital growth engines to the global economy and determining the extent of AI’s global impact.
The path to successful AI adoption
Investors and board-level decision makers will spend much of 2026 identifying key target markets and the mid-market should be top of every list.
Identifying and assuring investors’ delivery of AI ROI is critical to not only the future of the technology but also the success of so many global businesses. Mid-market companies represent roughly a third of private-sector GDP and around 40% of global employment.
They sit at the heart of the global economy, yet they are often overlooked in AI discussions that focus primarily on hyper-scalers or the largest enterprises. These companies do not have the luxury of multi-year transformation programs or unlimited budgets. For them, AI must deliver tangible value quickly or it simply will not stick.
Speed, simplicity and real outcomes
Mid-sized organisations are under constant pressure. They compete with far larger players while working with fewer resources. Cost efficiency matters, meaning time to value is critical.
Software that takes months to deploy or requires swathes of consultants is a non-starter. AI that creates more complexity than it removes will fail, regardless of how sophisticated the underlying models may be.
For AI to succeed in the mid-market, it must be designed around how people actually work. It must integrate easily into existing systems, automate real tasks and deliver measurable outcomes in weeks, not years. Anything less becomes shelfware.
This is where the conversation around AI needs to mature. The future of AI adoption is not about who has the most advanced models. It is about who can turn intelligence into impact, at speed, for teams that are already stretched thin.
The companies powering the economy
The future impact of AI will not be determined by a handful of global giants. It will be shaped by the millions of companies powering the economy every day.
Businesses with revenues of around a billion dollars, not ten or one hundred billion, are using AI to help their teams work more effectively, serve customers better and grow more efficiently. When AI works for them, it scales across the economy.
What is especially encouraging is how traditional businesses are embracing this shift. Companies with decades of history are using AI to modernise core operations, automate routine work and improve both employee and customer experiences. These are not abstract experiments or innovation theatre. They are practical applications that free up investment, improve productivity and support sustainable growth.
This is where AI becomes truly transformative for the mid-market: not as a futuristic concept, but as a tool that helps people focus on higher-value work and helps businesses compete more effectively.
Reducing complexity, not adding to it
If AI is going to deliver meaningful economic impact, we need to meet companies where they are.
That means reducing complexity, not adding to it. It means building technology that is intuitive by design, not powerful but inaccessible. And it means focusing relentlessly on outcomes, not features.
The most successful AI deployments I see share a common trait. They are invisible when they work well. They remove friction instead of introducing it. They make teams faster, smarter and more effective without requiring a fundamental rewrite of how a business operates.
This approach is especially critical for the mid-market, where every investment must justify itself quickly and clearly.
The real test for AI
The mid-market does not need more hype. It needs AI that works. If AI can help these companies scale, compete and grow more efficiently, the economic impact will be great. Productivity gains will compound. Innovation will accelerate. Opportunity will spread more evenly across industries and regions.
The real test for AI is not whether it can impress in a demo. It is whether it can deliver value where it matters most. And that future will be defined by how well we serve the companies at the heart of the global economy.
92% of operators say AI demand is speeding up build timelines, but supply chain challenges are creating costly remediation risks
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New research from data centre infrastructure specialist Onnec has found that surging demand for AI is heaping pressure on to data centre operators to accelerate builds – increasing the risk of delays, higher costs and remediation after go-live.
The survey of 300 senior decision-makers at data centre operators in the UK, Ireland and Nordics found that 92% of operators say they are being forced to compress build timelines to keep pace with AI demand. But 75% say speed-to-market pressure is forcing design decisions before infrastructure requirements are fully understood.
As a result:
Safety and cost pressures are rising: 74% say the pressure to accelerate data centre builds is increasing health and safety risks on site, while 67% say their AI-ready build or retrofit costs have increased in the past 12 months. Among those reporting a rise, costs have increased by an average of 42%.
Quality control is being squeezed: 45% say compressed timelines have reduced time for testing, commissioning or quality assurance, while 43% say they have increased risk of quality issues or rework on completed builds. A further 43% say infrastructure has required upgrades or remediation after go-live.
Supply chain disruption is delaying delivery: 61% have had a project delayed by supply chain issues, while 79% agree that geopolitical instability will affect the cost or availability of data centre components.
“Operators are under enormous pressure to deliver AI capacity quickly, but speed and readiness are not the same thing,” comments Matt Salter, Global Head of Data Centres at Onnec. “A data centre can go live on time and still need optimisation for the AI workload it was built to support. Reworks, retrofits and remediation are increasingly common. And network infrastructure is often where problems show up first. No amount of compute or GPU power can compensate for infrastructure or cabling that wasn’t designed to keep pace with today’s AI demands.”
Supply chain bottlenecks are holding back delivery
Almost half (45%) of operators are leaning towards new builds to deliver AI infrastructure, but one of the biggest pressure points impacting new builds is the supply chain. While 87% of operators are confident they have the right supply chain ecosystem to support fast, reliable delivery, delays are still occurring across the whole data centre environment.
Among those who have delayed a project due to supply chain issues, operators reported issues sourcing GPUs and compute (53%), cooling systems (45%), specialist staff (45%), power distribution equipment (43%) and cabling (39%).
Meanwhile, 29% of operators are leaning towards retrofitting to expand AI infrastructure and maintain pace without sacrificing design quality. But retrofitting is rarely a simple fix. Key challenges include cooling limitations for high-density racks (35%), retrofitting live environments without disruption (33%) and insufficient power capacity (30%).
“The operators who come out ahead in the AI race won’t be the ones who moved fastest,” continues Salter. “They’ll be the ones who took a holistic approach to design from the outset, treating cabling, power, cooling and compute as one connected system rather than separate workstreams managed under deadline pressure. Without that, operators risk locking in performance constraints that are harder and more expensive to address after go-live.”
Jeremy Vianna, Vice President, Strategic Growth at Nearform explains how AI-native engineering (AINE) is rewriting software creation.
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Agile is no longer the differentiator it once was. In fact, it’s now table stakes, with nine in 10 organisations practicing it.
Agile originally emerged as a response to the rigid, adversarial Waterfall model, replacing years‑long requirement gathering and disappointing final releases with rapid iteration, continuous learning and tight alignment between business and technology. By the mid‑2010s, agile thinking spread beyond software into the broader enterprise, becoming a baseline practice, rather than a competitive differentiator – most teams now use it, differing only in execution quality.
Because Agile inherently keeps teams moving in the right direction through constant feedback, the real shift today isn’t about methodology, but about what AI‑native engineering introduces: a new step‑change in capability. Just as early adopters of Agile once dramatically outpaced those on Waterfall, organisations who adopt AI‑driven engineering practices can achieve a similar – but far greater – velocity advantage, amplified by machine‑speed iteration rather than human‑speed process.
The next competitive edge isn’t about how you run ceremonies, it’s about how you produce software – and that’s changing fast. AI-native engineering (AINE) is rewriting software creation. It’s not about using AI as a bolt-on, but instead as a new means of production for intelligent organisations.
The impact of this shift is already visible: early enterprise adopters of AINE report a 20% productivity lift across development and service functions, and some engineers adopting coding assistants noticed their productivity increase by 10-20%. That’s before you even account for agents and closed-loop learning.
What is AI-native engineering?
AI-native engineering is all about using AI tools to create AI solutions – resulting in systems that are built from the ground up, and designed to scale, with AI. The agents execute governed tasks, while the AI-native architecture compounds performance over time, ensuring the system keeps improving.
As an example, at Nearform, we recently embedded governed agents inside an AI-powered cross-product search for a global pharma client. This enabled usage to feed the adaptation of prompts, retrieval and policies. Naturally, relevance improved instantly. But the biggest impact was seen in the discovery timeline, which compressed from six weeks to just two, and the AWS infrastructure came up in minutes, instead of weeks.
But why is this relevant now? Because enterprises aren’t struggling with model accuracy anymore, they’re struggling with operationalising AI at scale. Most companies still haven’t been able to move pilots into measurable production value, only 26% have the capabilities to move beyond proof of concept, and 74% are still failing to realise tangible AI value. AINE is the missing operating model.
The new innovator’s dilemma
Unlike previous delivery models, AI-native engineering introduces compounding velocity. This means automated code generation and tests reduce release cycles from weeks to a matter of hours. Governed AI agents run multi-step workflows, keep context inside the system and reduce rework and handoffs. Meanwhile, continuous evaluation pushes improvements back into prompts, retrieval and policies.
Early AI-native engineering systems may look ‘worse’ on legacy control metrics, as they’re packed with unfamiliar governance paths. But AI-native startups, unburdened by process debt, adopt AI-native engineering from day one – driven by smaller, senior teams supported by streams of governed agents. This immediately results in higher velocity and less waste – and as tooling and methods mature, cost advantage and learning loops become an insurmountable advantage.
McKinsey’s State of AI report evidences this, showing how most organisations are still experimenting, while a minority of high performers – who are redesigning workflows – are realising outsized value.
Failing to adopt AI-native engineering risks the AI-edition of the innovator’s dilemma – protecting today’s governance and delivery model, while tomorrow’s competitors compound away from you.
AI-native engineering erodes yesterday’s moats
The old moats were legacy codebases, hard-won internal knowledge and proprietary data. AI-native engineering attacks each of these.
With AI-native engineering, automated refactoring and code generation significantly drop the cost of rebuild vs. maintain – eroding the value of legacy codebases. Similarly, copilots and agents encode decision history, meaning internal expertise becomes portable across teams, instead of being trapped inside handoffs. And the proprietary data advantage is narrowed by foundation models and synthetic data. In fact, foundation models already encode massive general knowledge, meaning smaller players aren’t starting from zero anymore.
We’ve also seen institutional knowledge become more portable. In the pharma use case above, copilots and agents codified decision history and evaluation criteria into the system, meaning context is able to travel with the work.
The barrier to high-performing systems is collapsing. It’s no longer about “who owns the most data” or what you’ve built – it’s about how fast you’re able to learn and improve.
The measurement problem nobody wants to admit
Currently, most CTOs trying to prove AI’s value are measuring the wrong things. Velocity, story points, lines of code, etc, are all proxies for human labour – friction points getting in the way of progress. When you move at machine speed, these measures collapse to zero. What actually matters is whether AI is solving the problem, not how fast a human would have solved it.
Agile teams often ask the business for time, trust and a year of dedicated resources, so they can ship iteratively, learn continuously and ultimately, deliver the most valuable outcome – without knowing upfront exactly what that will be. Finance however, works on annual planning and wants clear commitments on cost, scope and ROI at the outset, creating a long‑standing tension between agile delivery and fiscal predictability. Traditionally, software engineering could bridge this gap because it relied on decades of experience, solid estimation models and deterministic systems.
But AI-native engineering’s value is compounding. It’s not just seen in sprint velocity, it’s seen through the reduction of rework cycles, the acceleration of the second and third release after the first, and the rate of technical reduction debt over time. These are harder to measure in the short-term, and therefore often easier to dismiss – which is precisely why many organisations stay trapped in pilots. Not because the technology failed, but because large delivery models and metrics aren’t designed to capture compounding value, making it difficult to justify the next investment.
The CTOs realising true value aren’t necessarily those with the best agents, they’re the ones who started by instrumenting their workflows before introducing AI. This gives them a genuine baseline to measure against, meaning ROI conversations become more of a demonstration than a negotiation.
A pragmatic path forward
Start with a thin-slice in production: pick a workflow that has real P&L impact as a starting point. Then ship a governed agent and closed-loop evaluation and measure against the baseline.
Build the AI-native engineering backbone: introduce governance as code into the workflow, embed evaluation into CI/CD, and centralise shared memory to preserve context and knowledge across teams.
Reshape the talent mix: create senior-led pods that combine domain leaders with engineers who are fluent in agents, retrieval and testing automation.
Scale by compounding: each release becomes a reusable capability, which you can use to grow horizontally over time, into adjacent workflows.
AI-native engineering moves beyond bolting AI-features onto existing workflows, towards changing how you build. While Agile made delivery scalable for almost every organisation, AI-native engineering makes it compounding.
The organisations that internalise that now will set the pace for the next decade. The rest will be catching up… at human speed.
By Jeremy Vianna, Vice President, Strategic Growth, Nearform.
Robbie Tilleard, GM EMEA at Lorikeet, on why you don’t need a year, a team of twenty engineers, or a bespoke model trained on your data to deploy an AI in CX
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Revolut launched its AI financial assistant in April this year. Starling launched one three weeks before that. An in-app AI financial assistant has become table stakes for FinTechs overnight.
The good news is you don’t need a year to get there. All you need is a plan and the right data.
Eight Years or Eight Days
Revolut’s AI assistant took a year from announcement to launch, and Starling describes their assistant as the culmination of eight years of work. Both were building the infrastructure before it existed off-the-shelf.
FinTechs we work with deploy an AI assistant in weeks, not months. They don’t build from scratch. They buy infrastructure that’s already solved the hard parts… Knowing when to escalate, handling ambiguity without hallucinating, navigating the tone of a conversation where someone has had their card blocked and is not in the mood for corporate language.
Sound familiar? It should. Every technology shift in financial services has looked like this for a period. Early movers build bespoke, the rest wait for the case studies, and then someone works out you don’t need to build when you can buy much faster. Then it simply comes down to having the right data in place, connecting your systems, and putting in place a clear implementation plan. Financial services knows the playbook. The difference is that the loop has simply gotten a lot faster with AI.
Why Not Wait Longer Then?
Customer expectations have moved, shaped by AI interactions people have outside financial services (support queries answered in seconds, refunds processed mid-conversation), and the gap between ChatGPT responses and waiting for a queued ticket to a human agent is becoming visible in churn data. The FinTechs that move first will set the bar for what customers expect from anyone in the same category.
There is also a regulatory dimension. In the UK, the FCA moved Consumer Duty into more active supervision this year. The review published in March 2025 found that vulnerable customers continue to receive worse outcomes than other customers, particularly where firms have primarily digital customer journeys, and that most firms have underestimated the depth of outcomes monitoring required.
The reaction to AI in many compliance teams looks a lot like the reaction to autonomous vehicles. Every time a Waymo crashes, the genuine safety improvements (fewer accidents overall, faster hazard detection, no fatigue) get less coverage than the single incident.
Yet AI is going to help Consumer Duty over time, not hinder it. Autonomy with the right guardrails produces more consistent outcomes than human handling at scale. An AI system can understand a customer’s problem immediately, resolve it on the spot where it can, and route to the right internal team where it can’t, without queue times, without agent fatigue, without twenty different people interpreting the same policy differently. This ensures the core customer care team remains focused and can prioritise the cases with the most need. And this isn’t a far off future for consumers. The infrastructure to build it already exists off the shelf.
The European Dimension
There are additional points to consider if you operate in Europe. The EU AI Act’s full compliance requirements for high-risk AI sees financial services explicitly on the list.
Being compliant means documented risk management, transparency mechanisms, human oversight baked into the architecture and a traceable record for every decision that the system makes. The cost of getting this wrong is high: up to €35 million or 7% of global revenue for the most serious breaches.
Most organisations aren’t close to achieving requirements. More than likely they’ve rolled out AI as an experiment (that’s good!) but without a plan to get it scaled in production (not so good). Systems built purely to retrieve and respond weren’t designed with auditability or explainability in mind and it isn’t something you can bolt on later.
If you operate in Europe and have not mapped your current customer experience AI stack against these requirements, the time is now.
What You Actually Need
You don’t need a year, a team of twenty engineers, or a bespoke model trained on your data. You need focused use cases, infrastructure that handles the compliance layer, and a team willing to run a tight pilot with clear success criteria and a plan to scale.
You can move now. The companies that don’t are still lacing up.
Callum Pennington, CEO & Co-founder of HBHR, tells us why HR teams that don’t embrace AI risk being left behind.
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Across Europe, AI used in hiring, promotion, workforce management and performance evaluation, is being formally classified as ‘high-risk’ under the recent EU AI Act. HR is now on the regulatory front line. This year, most of the core rules for those systems will begin to apply, with further obligations phasing in through 2027.
That label is supposed to be a safeguard, but inside many HR teams it often lands more like a warning: if something is ‘high-risk’, it is safer to keep your hands off it altogether. Faced with new regulation, loud debate and limited capacity, many business leaders are quietly concluding that the safest option is to delay, restrict or avoid AI in HR altogether.
In my view, that instinct, however understandable, is now one of the biggest risks HR faces. Over-caution does not freeze risk, it freezes progress. The real choice for HR leaders is no longer ‘AI or no AI’. It is what kind of AI they use, under what controls, and with whom in charge.
High risk doesn’t mean ‘don’t touch‘
The EU AI Act organises systems by risk. At the top are prohibited practices, such as emotion recognition in workplaces or social scoring, which are simply banned. Below that sit the ‘high-risk’ systems, including many HR and people management tools, where stricter rules apply. These include documentation, human oversight, transparency and robust data governance.
On paper, that is a familiar pattern. Medical devices, credit scoring and critical infrastructure controls are all treated as high-risk too. The message isn’t ‘never use them’, it’s ‘treat them as important, design them carefully, and put them under proper governance’.
In HR, though, ‘high-risk’ can sound scary. If something goes wrong in a recruitment algorithm or workforce-planning tool, it is not hard to imagine the headlines. That fear is pushing some organisations towards a defensive posture, to park AI projects, ban tools outright, and stay in the comfort zone of manual decisions.
The problem is that ‘going manual’ isn’t the same as ‘being safe’. It simply hides risk better. Spreadsheets do not come with model cards or audit logs, but they can still embed bias, errors and inconsistency.
In many ways, this moment mirrors the shift from paper records to spreadsheets. At the time, some organisations worried that digital tools would introduce new risks. In reality, spreadsheets improved accuracy, visibility and accountability. AI represents a similar shift today — the difference being that well-designed systems can now surface risks earlier rather than burying them inside manual processes.
According to HBHRʼs own research drawing on a survey of 2,000 UK employees, ongoing manual payroll errors are already having an immense impact on the workforce. Twenty percent of workers say a single payslip error has caused them to miss a bill, while 18% report having to borrow money because of payroll mistakes.
A well designed, well documented AI system with human oversight is not the opposite of compliance. Increasingly, it is exactly how compliance will be demonstrated.
The hidden cost of over-caution
When organisations freeze on AI, they push more strain onto already stretched HR teams juggling legacy systems and manual workarounds. Modern tools are simply an expectation, with HBHRʼs research demonstrating that 85% of employees expect their employers to use up-to-date technology to minimise mistakes, with 72% saying that the technology their employer uses directly correlates to the confidence they feel in their pay being accurate and on time. Outdated legacy tools not only contribute to a lack of trust among employees, but create extra work and pain for employers.
You see it most clearly in high-volume processes, where recruiters spend days sifting through CVs, whilst payroll teams are forced to manually reconcile inputs, hoping nothing has been mistyped along the way. At the same time, boardrooms are being told that AI is a once-in-a-generation opportunity, and that countries like the UK face a huge economic gap if they fail to develop the right skills and adopt the right tools. HR simply cannot sit that conversation out and still claim to be a strategic partner.
Over-caution creates its own risk: higher error rates in core people processes, slower responses to regulatory change, and HR teams so buried in admin that they have no capacity left for culture, capability or workforce planning. These errors and barriers are not without consequence – 61% of employees surveyed by HBHR would look for a new job after six months of repeated payroll errors and delays.
Moving beyond ‘AI vs humans‘
Public debate often frames AI as a direct threat to HR roles, as algorithms that replace recruiters and chatbots that replace HR advisers. This narrative is just not true. Most HR teams I meet are not short of work, rather theyʼre short of capacity. The opportunity is a symbiotic HR function, where each does what it is best at.
In practical terms, AI is very good at repetitive, pattern-recognition tasks at scale. It can scan thousands of CVs for clearly defined, job-relevant criteria and hand recruiters a shortlist that is actually manageable. It can monitor payroll and pension data for anomalies, payments that do not match any live employee, or contributions that look out of line with policy, and flag them long before they become front-page stories. It can power assistants, like our own HRGenie at HBHR, that answers routine questions about holiday, pay and policies on demand, instead of sending employees into ticket queues.
People, by contrast, are good at context, empathy and ethical judgement. They are the ones who should decide whether a flagged pattern is a genuine risk or a perfectly reasonable exception, whether a candidate is the right fit for a team, whether a performance signal points to misconduct, burnout or a problem with the role itself.
The most effective HR functions I see are not those that automate everything they can, nor those that reject automation outright. They are the ones that deliberately design people-led, AI-supported processes, with humans making the calls that actually affect jobs, pay and progression.
From admin to engine
Across organisations, three clear camps are emerging in the approach to AI: those that use it with clear intent by starting with real operational problems, those that experiment widely but lack a coherent strategy and those that reject it outright as too risky. The third, overly cautious group is already beginning to fall behind, not because they lack talent, but because they lack the tools to scale it.
Everyone talks about wanting ‘strategic HR’. But strategy requires headroom and trustworthy data. If HR leaders are still spending most of their week reconciling numbers between three systems, or chasing down the source of basic discrepancies, it is almost impossible to play that strategic role.
The combination of the EU AI Act and accelerating workplace change makes this an inflection point. HR can either retreat into manual, reactive processes in the name of caution, or step forward and shape a people-led, AI-enabled function that is more resilient, more compliant and more human than what came before.
That does not mean turning HR into a testing ground for experimental tools. It means getting the foundations right: unified HR and payroll systems rather than fragmented stacks of disconnected tools, clear governance for AI, and HR leaders who are confident asking hard questions about how technology works, not just what the sales slide promises.
Handled thoughtfully, AI will not replace the ‘people’ side of HR. If anything, it will finally give HR teams the time, visibility and capacity to focus on it properly.
In an era where regulations are tightening, skills are shifting and expectations are rising, an overly cautious approach to AI in HR is no longer the safest option. Standing still may ultimately prove far riskier than moving forward thoughtfully.
Cirata CEO Stephen Kelly tells us why having fully centralised data is the key to creating seamless AI access for companies.
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MIT reported that up to 95% of enterprise AI projects fail. Businesses are racing to adopt the technology and rushing execution in the process, significantly eroding ROI and potentially leaving thousands of businesses at risk of regulatory exposure.
Despite the urgency, there lies an invisible burden which is undermining progress. It is called AI debt and it may be the single greatest obstacle standing between AI ambitions and commercial reality.
AI debt is the accumulated result of incomplete digital transformation. It is every decision which meant legacy infrastructure was never fully retired, or the fragmented data siloes that were never unified. Any new platforms layered on top of such large and complex data sets that haven’t been organised properly, are creating complexity rather than clarity.
On their own, these shortcuts may have seemed pragmatic but collectively, they are now stalling innovation.
The risk of unfinished business
Recent analysis from McKinsey highlights the scale of the missed opportunity. Despite AI tools becoming commonplace today, 63% of organisations reported that they are still experimenting or piloting early-stage AI projects. This shows that most organisations are yet to embed AI deeply enough into their workflows and processes to capture its full value, estimated globally at between $2.6 trillion and $4.4 trillion.
The number one reason for this is years of bolt-on systems that have created tangled IT estates that slow decision-making and make rapid innovation nearly impossible. Running legacy and modern environments side by side inflates maintenance costs and introduces operational confusion which can result in failed implementations. These poorly executed migrations waste capital and introduce security and compliance risk, particularly under regulations such as General Data Protection Regulation (GDPR) and Digital Operational Resilience Act (DORA).
These consequences are measurable. Projects are delayed by months, which has a knock-on effect for budgets. Overall, the whole process inevitably grinds to a standstill because the data required to fuel AI models remains locked away in silos. Estimates suggest that between 50 and 70 per cent of enterprise data remains unconnected and inaccessible for advanced analytics.
The rise of AI debt
The push towards autonomous systems capable of independent decision-making is amplifying this risk of failure. While a majority of organisations plan to deploy AI agents in the near term, only a fraction have centralised their data or ensured their infrastructure can handle the projected surge in workloads.
The statistics are sobering. A recent report from Cisco found that fewer than one in five companies have fully centralised their data for seamless AI access. Over 60 per cent expect workloads to increase by more than 30 per cent within the next few years. Less than a third feel fully prepared to secure agentic AI systems against emerging threats.
Even the most digitally advanced firms are grappling with spiralling compute costs and persistent talent shortages in cybersecurity and AI engineering. In the same way that technical debt slowed software development in the 1990s and 2000s, AI infrastructure debt threatens to stall the current wave of transformation before it delivers meaningful returns.
At its core, AI debt is a data problem. AI systems amplify whatever they are trained on. If the data is incomplete or contextually degraded, the outputs will be flawed, often in ways that appear plausible but lack integrity. This phenomenon, sometimes described as AI slop, is not merely a technical nuisance but a commercial and reputational risk.
This occurs when organisations migrate or modernise without preserving metadata, lineage and governance and so its meaning is lost along with trust. In regulated industries, that erosion has legal implications. In competitive markets, it has revenue implications.
The path forward requires paying down the debt.
Paying off AI debt
At Cirata, we always advise that the best way to eliminate AI debt is to address fragmentation at its source. This means employing a system that can create a unified, interoperable data foundation that supports AI at scale. By decoupling data orchestration from underlying infrastructure, organisations can move, replicate and integrate data seamlessly across on-premises, hybrid and multi-cloud environments without disrupting production systems.
This approach delivers several strategic advantages. Automated data flows across clouds and platforms ensure models are trained and updated with the latest data. Businesses should always look to leverage open standards, such as Apache Iceberg, to prevent vendor lock-in and preserve long-term flexibility.
IT leaders should explore solutions to help them make sense of their data. By centralising governance and eliminating brittle integrations, organisations can feel confident they’ll be on the right side of AI project success. Most importantly, they can break the cycle of making short-term compromises that accumulate into long-term risk.
No algorithm will compensate for structural weakness
The promise of AI remains immense. Autonomous systems and generative models will continue to reshape industries. But no algorithm can compensate for a weak foundation. Just as a building requires structural integrity before additional floors are added, AI requires a unified and trusted data infrastructure before it can deliver sustained value. The organisations that thrive in 2026 and beyond will not be those that launched the most pilots. They will be those that had the discipline to eliminate their AI debt first.
Errol Rodericks, Director of product marketing EMEA and LATAM at Denodo, on why the future of insurance will be defined by whether humans and AI agents have the right data, in the right form, to make the right decisions
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AI adoption across the UK insurance sector continues to accelerate, yet the impact is falling short of the industry’s expectations. The narrative is now shifting beyond systems that analyse data and generate recommendations towards agentic AI. Capable of taking action, whether that means initiating claims processes, identifying potential fraud in real time, refining underwriting decisions or triggering next-best actions. Many see this as the next major leap forward for insurance, and they may be correct, but not for the reasons most people think.
While AI development certainly doesn’t lack pace, most of the insurance industry remains constrained by a more fundamental issue. AI cannot make trusted business decisions until it first understands the business. That understanding depends on trusted, relevant data being delivered in the right context and at the right time. Without it, insurers will struggle to give AI systems meaningful autonomy, however advanced the models become.
This is not because insurers lack data or technology. Over the past decade, the industry has invested heavily in data lakes, advanced analytics and AI tools, and integration and data engineering pipelines. Yet despite these investments, many organisations continue to struggle to move AI beyond proof-of-concept projects and into day-to-day operations.
Meeting the Challenge
The challenge is not data availability. It is ensuring data is trusted, relevant and readily accessible when decisions need to be made.
Few industries experience this challenge quite like the insurance industry. Decision-making depends on information drawn from multiple policy, claims and customer systems, alongside external data sources such as telematics, weather and credit data. When that information is fragmented or delayed, even the most advanced AI systems are left working with an incomplete picture, making it difficult for AI to understand the wider business context behind every decision.
Agentic AI does not solve this problem. It exposes it.
Access Alone Is Not Enough
Many organisations respond by building a shared data foundation consisting of a unified layer where humans and AI agents can access the same information. While this is directionally right, it is incomplete. The challenge is not that organisations lack a shared data layer. They struggle to deliver the right version of data for each decision, at the moment it matters.
Insurance operates on multiple, decision-specific views of data, each with distinct requirements:
Claims decisions depend on real-time, enriched incident data.
Underwriting relies on forward-looking risk models and external signals.
Fraud detection requires cross-entity patterns and behavioural analysis.
Customer servicing depends on a simplified, current policyholder context.
These are not variations of the same dataset; they are purpose-built representations of data, shaped by different latency, governance, and semantic needs, which becomes even more critical with agentic AI. Different agents operate at different points in the decision lifecycle, and require different data, in different forms, at different times.
A shared data layer can improve access to information, but access alone does not guarantee better decisions. Context transforms data into business understanding, and business understanding is what enables trusted decisions.
Turning Data Into Action
This is where many AI strategies stall. Most architectures are designed to store, process, and analyse data, but not to activate it at the point of decision. There is a fundamental gap between data being available and data being usable within real-time workflows.
Agentic AI operates directly in this gap. Without access to live, governed, and contextually aligned data, agents operate with partial understanding, and their outputs become unreliable. This is why many AI initiatives remain stuck in experimentation.
To move forward, insurers need to rethink how data is delivered. Not as raw datasets or reports but as data products. A reusable, governed, and outcome-aligned data asset designed to support a specific decision or workflow is what’s needed. Instead of exposing raw data, insurers should deliver contextualised, decision-ready views, with embedded governance and policy controls, consistent business semantics, and real-time access to internal and external sources.
For example:
A claims data product unifying FNOL, policy data, repair estimates, and external signals.
A fraud data product combining claims history, network relationships, and behavioural indicators.
An underwriting data product integrating internal risk data with third-party enrichment.
These are not static datasets. They are decision-ready data assets, designed to deliver the right information, in the right context, for a specific business outcome.
Why Data Quality Matters Now More Than Ever
For agentic AI to deliver value, data must be live, governed at access, semantically consistent, and traceable. This is where a logical data layer becomes critical, not just as an integration approach, but as a way to connect distributed data in real time, apply governance dynamically, and deliver consistent, business-ready views across systems. This enables both humans and AI agents to act with confidence, without introducing further fragmentation.
The insurers that lead in 2026 will not be those with the most advanced models. They will be the ones that connect AI directly to business outcomes. That means starting with the outcome, such as reducing claims cycle time, improving fraud detection, increasing underwriting precision, or enhancing customer experience, and working backwards to define the decisions, data and systems required to support them.
This is how AI moves from experimentation to operational impact.
Where AI Initiatives Succeed or Fail
The next phase of AI in insurance will not be defined by advances in model performance. It will be determined by how well AI understands the business it is supporting. That requires trusted, relevant data, but it also needs the business context that allows AI to interpret that data correctly and make decisions insurers can trust.
Agentic AI accelerates this realisation. It makes clear that data must be trusted, contextual, available at the moment of decision, and aligned to outcomes. The goal is not simply to give AI more information. It is to help AI understand how the business works well enough to make confident, consistent decisions. Those who solve this will scale AI successfully, and those who do not will continue to pilot without transformation.
The future of insurance will not be defined by whether humans and AI agents share the same data. It will be defined by whether they have the right data, in the right form, to make the right decisions. That requires a shift from shared data to decision-ready data, from access to activation, and from experimentation to measurable outcomes. The real inflection point for AI in insurance will come when organisations enable AI to understand the business well enough to make decisions that customers, employees, and regulators can trust.
Hugh Scantlebury, CEO and Founder of Aqilla , on why the finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight
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At first glance, finance and accounting appear to be ideal environments for AI integration. The work is structured, rules-driven and built on numerical data, which AI can automate and process at scale and speed. As such, it’s ideal for much of the repetitive work — such as invoice capture, reconciliations, reporting, and anomaly detection. These tasks still consume so much time and reduce space and capacity for strategic and creative thinking.
But as confidence in AI grows across the sector, and as the technology is integrated into accounting and finance software, is there a risk that organisations once fearful of the technology may go to the other extreme? What might happen if they lean so deeply into AI that finance and accounting professionals become distant and removed from the numbers?
These questions matter because the decisions that sit behind the numbers are rarely driven by pure logic. Financial strategy is shaped by risk appetite, leadership judgement, organisational priorities and sometimes even internal politics.
So many entrepreneurial success stories include a fateful risk, gamble or moment of inspiration that defies established financial wisdom. A founder deciding whether to invest in growth or a charity balancing financial sustainability with its mission does not rely on numbers alone. That means a surprisingly high number of commercial decisions have a human dimension that AI cannot, and arguably should not, replace.
Balancing AI and Human Strengths
So how can organisations maintain the human intuition and instinct that sits behind so many corporate transactions while embracing AI? The answer is to use the technology to remove repetition rather than people from core financial processes. In practical terms, that means using automation to reduce cognitive load so people can focus on interpretation, creativity and decision-making — the human parts of finance and accounting.
That’s a sensible approach because poorly implemented automation accelerates errors and obscures decision-making processes. It can also cut humans out of the process exactly at the point where instincts and experience are most needed. In that situation, AI-enabled systems simply become faster at making poor decisions. And those poor decisions end up costing time as well as money.
Once that happens, the space, time and resources that finance and accounting leaders are trying to create for more strategic work will rapidly shrink. By contrast, when humans actively guide AI systems, review outputs and set boundaries, automation becomes a powerful extension of human capability rather than a substitute for it.
The Limits of Automation in Decision-Making
In finance and accounting, numbers often create a sense of objectivity and certainty. But financial reporting still involves interpretation, context and judgement. A technically correct output is not always the same thing as the right commercial decision for a business, its employees or its long-term strategy.
As such, it’s important that accounting and finance professionals at every level do not lose a grip on their data when engaging with AI and automation. Aside from losing an understanding of the systems and processes behind the outputs, there’s still an ethical responsibility to deploy AI in a way that preserves human oversight, authority and compliance in financial reporting. That’s because, for the first time, we’re asking technology, in the form of AI, to provide an opinion on our data — not just deliver the logic and the numbers.
For that reason, finance leaders still need visibility into how AI-generated outputs are reached, the ability to challenge them when necessary, and a clear understanding of the underlying data behind the results. That’s important because AI output is ultimately based on prediction, and prediction is not the same as established and quantifiable truth.
Keeping Score
One possible middle ground is confidence scoring and validation workflows. Rather than unquestioningly trusting every AI-generated output, organisations can introduce processes that flag lower-confidence results for human review before action is taken. That creates a more balanced relationship between automation and oversight, while also giving finance teams clearer visibility into how reliable or complete AI-generated outputs actually are. The goal should be confidence in AI-supported workflows, not unquestioning reliance.
That visibility, however, should not sit solely with grads and junior finance staff. They need to understand the manual calculations and processes sitting behind automated systems so they can properly challenge the results. But the same principle applies at senior levels too. Experience and seniority should not create distance from the underlying logic behind the numbers. If anything, AI makes that visibility even more important.
Otherwise, organisations risk creating the worst of both worlds: juniors who can’t challenge AI outputs because they haven’t learned the manual processes, and complacent seniors who know the manual systems and assume AI is following them. It means organisations can end up with more information at their fingertips than ever before, while simultaneously becoming more detached from the underlying data and logic behind it.
That risk becomes even more significant at senior levels, where financial decisions often carry wider operational, commercial and strategic consequences. The more detached leaders become from the logic behind the outputs, the greater the impact when reliability or credibility issues emerge within the data.
Conclusion
Productivity gains from AI and automation have the potential to create more space for higher-value thinking rather than remove people from the process. The information AI surfaces can ultimately help senior leaders make more creative and strategic decisions by revealing connections, patterns and insights that may previously have remained hidden within the data.
Leaders have always relied on summaries, dashboards, and reporting layers to help them make decisions. But AI dramatically widens the gap between decision-makers and the operational reality beneath the numbers. The danger is not simply inaccurate data. It’s overconfidence. When systems appear highly intelligent and highly efficient, organisations can gradually stop questioning how conclusions are reached in the first place.
Ultimately, organisations may wish to focus on people-led automation rather than handing complete control to systems. The goal should be confidence in AI-supported workflows, not unswerving reliance on automated outputs — with AI acting as an extension of human capability rather than a replacement for judgement.
The finance teams that benefit most from AI will likely be the ones that strike the right balance between automation and oversight. Hand over the repetition and the manual processing, but don’t lose visibility into the data itself. Because if organisations surrender that understanding completely, all the entrepreneurial instinct, creativity and commercial judgement in the world may no longer be enough to compensate for the decisions being made underneath them.
Louis Landry, Chief Technology Officer at Teradata explains why it’s an exciting time for enterprises who are leveraging agents to transform their processes.
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Technological advances such as agentic AI have completely changed the way enterprises work for the better, but they have also posed several challenges. Businesses are not struggling with AI because the technology isn’t ready, but because the infrastructure beneath it isn’t.
Several demonstrations will have you believe that AI implementation is just another straightforward process, but this is not the case at all. Such presentations are impressive because the data is already polished, the queries are direct, and the answers are predictable, while the overall environment is controlled. But this is not the enterprise reality.
The typical enterprise runs multiple different SaaS applications, each with its own platform, data models and methods for showcasing the same business concepts. Not only that but they’re layered on top of millions of relationships and contextual rules that no demo was ever designed to handle. As a result of the complex state some enterprises’ systems are in, most of their AI adoption projects end up stalling or failing. This was also confirmed by last year’s MIT report which stated that 95% of businesses don’t see a return on their AI investment despite spending $30 to $40 billion.
So what are so many enterprises doing wrong and how can they revert it?
What’s hindering enterprises’ AI journey
Most AI models are impressive in isolation, but an enterprise environment doesn’t operate in siloes. Businesses have layered systems, interdependencies and nuanced rules that AI models will need to be able to support, otherwise they’ll fall short. Without the ability to act autonomously, they will not be able to provide meaningful change to the business.
With this in mind, a successful AI deployment needs proper orchestration. The 5% of enterprises that are winning this race coordinate their AI agents to own different areas within the business and come together through an integrated foundation instead of depending on a single model for everything. Successful enterprises see AI as a strategic differentiator, grounded in integrated, proprietary datasets that competitors can’t duplicate. They also ensure their systems are following the business objectives and operational limitations, as well as compliance requirements and competitive pressures.
AI autonomy in four levels
In terms of what successful enterprises have in common, they are all in what I refer to as the “AI autonomy journey” where they usually go through four different levels to build a robust foundation that transforms agentic AI deployments to capable and trusted advisors. Each business will achieve different levels of autonomy depending on how technologically advanced they are, their complexity and business value.
Level 1
Looking into these levels in more detail, the first stage is where most businesses sit today. Their AI systems are taught to respond to basic interactions in natural language and collect data, though humans are still needed to validate the results.
Level 2
The next level sees AI tools taking a more trusted analyst position. They start to comprehend context and intricacies while understanding connections between datasets. Such systems are able to recognise business terminologies and manage scenarios independently.
Here’s an example to help you visualise the two levels. An enterprise in the banking sector wants to understand its loan repayment rates across different customer segments and product types. A level one system will be able to retrieve and share the repayment figures with the exact criteria, while a level two system will not only gather the numbers, but will also flag which customer profiles are showing signs of risk and recommend next steps for the analyst. This stage goes beyond simply reporting figures, as it is able to translate complex financial patterns into clear and actionable intelligence.
Level 3
Then we move on to level three, which is a significant turning point for an enterprise’s AI autonomy level. At this stage, the technology is able to make connections across your business and recommend actions with insights, including retention strategies, adjusting contract terms or raising potential risks.
Level 4
And finally, at level four, we see AI stop advising and instead start acting. For example, if you prompt it to optimise customer retention while keeping margins at a certain point the system will autonomously identify at-risk customers, deploy the right strategies and track the results while adjusting its tactics in real-time. With that said, employees are still in control of defining the goals and setting the risk tolerance levels and compliance requirements.
The above levels reveal a clear progression. If level one is “prompt and prove” and level two is “prompt and trust,” then level three is “prompt, understand and suggest” and level four is “define, deploy, adapt”. Through this autonomous journey, the system gradually becomes an intelligent business analyst that actively improves how decisions are made, enabling the business to operate at a scale that simply wasn’t possible before.
The road to competitive advantage
As enterprises start exploring what their AI autonomy journey looks like, we will also see a gap forming between businesses at different stages. Companies moving on to higher levels of AI autonomy will gain real competitive advantage much faster than others. Having said that, achieving this isn’t as simple as a matter of deploying more powerful AI models. It requires a robust knowledge base that AI systems can actually depend on, otherwise they become unreliable and decisions get misaligned. This is where context engineering comes into play as a critical tool helping autonomous agents become more accurate and useful, and shaping how AI systems interact with the broader business. Context engineering also enables agents to perform complex tasks without worrying about errors coming up in the process.
It’s an exciting time for enterprises who are leveraging agents to transform their processes. The ones who are successful in their adoption will be those who understand early on that AI is only as powerful as the foundation it sits on. This means that it’s crucial to get the datasets, context and orchestration right from the beginning, to be able to progress with your AI autonomy journey and set yourself apart from competition.
Sascha Giese, Tech Evangelist at SolarWinds talks to us about how to manage the transition from AI experimentation to delivery.
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When Johnson & Johnson revealed a shift in its AI strategy last year, it signalled the end of a period of experimentation for the pharmaceutical giant and the beginning of a more disciplined, results-driven era.
At its peak, J&J employees were pursuing nearly 900 individual AI-related use cases. But as the WSJ reported at the time, this was a position that was simply unsustainable. Instead, the company decided to change tack and home in on high-value generative AI use cases in areas such as drug discovery and supply chains.
For many in the tech sector, this was a high-profile example of a business deciding to call time on pilots and trials in favour of a more structured approach where AI finally has to earn its keep.
Fast forward to the beginning of 2026, and the State of AI in the Enterprise report by the Deloitte AI Institute suggests that many companies are also following a similar path, with business leaders becoming impatient and wanting to see a return on their investment.
AI is shifting from a one-off investment to an ongoing operational expense
And it’s easy to see why. Amid the current AI boom, a recent report by Mavvrik, a US-based IT financial management platform, found that 80% of enterprises missed their AI infrastructure cost forecasts by more than 25%. It claimed that AI costs are “crushing margins” and that AI-related overheads are harder to manage than cloud costs because AI introduces new variables where “minor changes in usage can spike spend by 100x”.
It’s a good point. Unlike traditional software, which tends to follow predictable instructions, AI systems explore multiple possible paths to reach a solution. Sometimes that could mean resolving an issue in five minutes. At other times, it might mean AI systems retrying, looping and testing alternatives for hours, leading to additional expense.
But there are other unforeseen costs as well. The dash to embed AI can often expose shortcomings in existing IT infrastructure. Legacy systems and on-prem environments, for example, that might have been perfectly suited to a pre-AI world may now lack the necessary processing capacity required.
What rightsizing AI adoption really looks like
As a result, this may force organisations to bring forward plans to upgrade their technology. But at what cost? It’s not simply a case of writing a cheque and flicking a switch. In practice, it means being clear about the task AI is meant to improve, and defining measurable indicators and a baseline against which to judge impact.
But rolling out AI isn’t just about the technology. One of the lessons we’ve all learned over the last couple of years is that if workflows are unclear or the quality of data is poor, then AI will expose those shortcomings. Without a proper assessment of whether the company is ready for AI, such a project could quickly turn out to be unsuccessful, becoming a time and money sink.
Equally, if AI is embedded into structured environments – with defined responsibilities and feedback loops – then we also know that AI can genuinely improve outcomes.
For instance, a recent report by SolarWinds sought to understand how generative AI (GenAI) has been incorporated into ITSM (IT Service Management) workflows. It focused on those areas within helpdesks designed to reduce manual effort, such as automatically suggesting ticket responses, sourcing relevant knowledge base articles and generating incident summaries.
The report wanted to discover whether AI was genuinely making life easier for IT teams or merely adding another layer of complexity. The findings showed helpdesks saw a significant decrease in average incident resolution time after enabling the GenAI features.
Savings and Benefits
On its own, that saving of just under five hours per ticket – or just under 20% of the time spent per support request – is impressive. But it’s only when you multiply that efficiency gain across the hundreds, if not thousands, of tickets generated each year that the true scale of AI’s impact becomes clear.
While the savings clearly point to GenAI’s benefits, the report also highlighted a common thread around that all-important pivot from pilot to operations. Instead of viewing GenAI as a test or side project, those teams that integrated AI tools into their daily service desk workflows appeared to have better outcomes.
“These results show what’s achievable when AI adoption is combined with effective change management and a focus on process improvements,” said the report. “They also act as a benchmark for other organisations evaluating the potential impact of similar tools,” it said.
Proper safeguards are key
But perhaps the biggest lesson learned from the last couple of years is that if we’re to truly maximise ROI, then we need to look at the bigger picture and develop AI systems in a much more methodical way. In effect, we need to develop AI by design. What does that mean? Well, in terms of privacy and security, it means establishing clear rules around not just the use of data but also how AI tools act and behave.
We also need to look beyond bias checks during model training to ensure that fairness is woven into AI right at the start, while ensuring that humans have the final say. And to build confidence in a new generation of tools, we need a proper paper trail to ensure that AI decisions can be traced and analysed so that, in the event something goes wrong, people can understand not simply what happened but why. It’s a good practice to ask an AI vendor which models are in use, where the training data came from, and what happens to the data users feed into the AI.
It’s too soon to say definitively whether Johnson & Johnson’s pivot signalled the end of AI’s pilot phase and the beginning of a more disciplined era. What we do know, though, is that the organisations most likely to succeed are the ones that are able to successfully manage the transition from AI experimentation to delivery while keeping a keen eye on outcomes and cost.
Sean Evers, VP of Sales & Partner at Pipedrive explains why using AI effectively involves redesigning workflows around business problems, whilst maintaining a focus on the human experience.
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AI appears to be everywhere, but clear results and a quantifiable ROI are not so common.
Many businesses are learning that simply adopting the latest tools doesn’t guarantee efficiency, innovation or growth. It’s a lesson that must be re-learned from time to time as the latest new tech paradigm comes upon the business world faster than the wisdom to make best use of them. And to be fair, faster than new technology itself becomes mature.
Too often in the past two years AI initiatives have been slower than desired in showing value because they’ve been treated as backend tech upgrades rather than strategic, human-centred transformations. An organisation is not a server rack, able to work at double the output when a new blade is inserted. Working with the grain of the business will always lead to better outcomes as the people and processes mix better with the changing variables being introduced.
Businesses can move beyond reflexive actions brought on by AI hype by embedding new technologies into problem-shaped workflows, creating meaningful impact through strategy, culture and human-centred implementation. AI tools are designed to solve specific challenges, not every challenge. We’re not at AGI yet (artificial general intelligence). Use tools wisely in limited areas, train teams well, and ensure a clearly communicated strategy supports team efforts. Then, all of a sudden, those investments in new tech will really move the needle on what’s been carefully defined as critically important.
Redesigning workflows
There is no best-in-class AI ‘Swiss army knife’. Even the most cutting-edge tech can’t deliver value unless workflows are redesigned around real business problems. AI succeeds when aligned to clear strategy, not when applied to broken processes like a sticking plaster. Firms positioning AI purely as an IT project will often encounter resistance, poor uptake and disappointing outcomes. The tech cycle has been this way forever, likely since fire and the wheel. Success always depends on employee understanding, willingness and trust in both their leadership and the ability of the tech to deliver on vendors’ promises.
Pipedrive’s ‘The evolving role of AI in sales workload management report’, highlighted how artificial intelligence has been reshaping sales roles and optimising workload distribution. In some areas, like in the sales function, the findings reveal that AI has become a co-pilot for professionals, enhancing efficiency and helping them focus on high-value activities:
One takeaway from the report was that AI users spend more time on strategic activities compared to non-users.
AI usage was still higher among sales managers (41%) than among salespeople (31%), indicating a need for more accessible AI tools and tailored training programmes.
Focus on what matters
AI centred on the human experience and business problems is what delivers real results. Thus, so-called soft skills and processes like corporate transparency, workplace empathy and ethical AI governance in practice are not really ‘soft’ considerations. Only a hard-headed, machine-minded person would fail to consider them as the essential enablers of organisational uptake and progress.
When employees are informed participants in change, AI becomes empowering rather than disruptive. That shift does not happen by accident. It requires leaders to articulate what problem AI is solving, for whom, and how success will be measured. Now, in many SMEs, the temptation is to deploy AI features because competitors are doing so, or because vendors promise transformational gains. But transformation without direction is simply noise.
Take a typical sales team in a growing business. Introducing AI-driven forecasting or automated lead scoring will not improve performance if the underlying pipeline stages are unclear or data hygiene is poor. In that scenario, AI merely accelerates existing inefficiencies. However, when leadership first defines what a qualified lead actually looks like, standardises processes and sets clear accountability, AI can enhance decision-making and free up time for higher-value conversations and really make a difference.
This is where cautiously redesigning workflows becomes critical. AI should be introduced at friction points like repetitive admin, inconsistent reporting, or slow handovers between teams. This is better than when added as a layer across every function. A targeted deployment creates quick wins, builds confidence and demonstrates measurable returns. Over time, those incremental gains compound and any cultural and technological lessons can be carefully applied in a virtuous cycle.
Managing the transition
The role of leadership is equally important. When AI is positioned as a cost-cutting mechanism employees will view it with suspicion. When it is brought in as a productivity partner to reduce manual tasks and allow individuals to focus on creative, relational or strategic work then adoption improves. Communication must be clear about what will change and what will not. Ambiguity will always breed a very understandable resistance.
Ethical considerations also need to move from policy into well-understood daily practice. For SMEs in particular, governance can feel like a burden reserved for larger enterprises who can ‘do it properly’. In reality, lightweight but explicit guardrails are often enough. Define what data AI tools can access. Establish human review points for high-impact decisions. Be transparent with customers about how AI is being used in interactions. These measures build trust internally and externally, which protects the long-term value of relationships and contracts.
There is also a mindset shift required. AI is not a one-off implementation. At the current rate of change it appears that it will be an evolving capability for many years. Teams will need space to repeatedly test, refine and learn. That may mean starting with a single department, gathering feedback and iterating before scaling. It may mean accepting that some pilots will fail. The objective is not perfection at launch, but steady alignment between technology and business requirements.
Being an SME can be advantageous
For SMEs, this disciplined approach can actually be a competitive advantage. Larger organisations often struggle with legacy systems and complex approval chains. Smaller businesses can be more agile, provided they resist the urge to chase every new feature release. Clarity of purpose becomes their differentiator.
Ultimately, fitting AI to the problem is about respecting the fundamentals of good management. Define the objective. Align people and process. Introduce technology where it adds measurable value. Review and refine. The companies seeing real returns from AI in 2026 are not those with the most tools, but those with the clearest strategy.
AI will continue to evolve rapidly. New capabilities will emerge, and expectations will rise. But the principle remains constant: technology works best when it amplifies well-designed human systems. Organisations that remember this will find AI soon stops being overwhelming when enabled as a controlled, practical tool for growth.
Paul Done, Field CTO at MongoDB talks to us about why people who think ‘vibe coding’ is all just lazy, AI generated code, are missing the valuable contribution AI can make to high-quality coding.
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Very few phrases in software have spread as quickly, or been as misunderstood, as “vibe coding”. For some, it signals the democratisation of software development, or the practise of causal programming for throw-away applications. But to its loudest critics, vibe coding represents a shortcut culture: developers tossing four‑line prompts into an AI model, then flooding production systems with fragile code. Many engineers also dislike the term because it implies coding without really understanding the code itself or blindly accepting AI output. In turn, some see vibe coding as trivialising the real effort and expertise required in software development.
I’ve been in the industry long enough to have heard similar complaints before: a new technology trend or tool that will make developers lazy, hollow out engineering skills, and ultimately leave organisations exposed. But that framing misses the point, because it has become a catch-all to describe two very different practises, with very different implications for developer teams and enterprises.
AI-driven coding is not vibe coding
Vibe coding often distracts and undermines the very real and positive impact of adding AI to the programming workflow. The leaps in the past few months alone have meant that AI agents have rapidly transformed how software is developed. Rather than manually writing code line by line, developers can now assign complex tasks to AI agents in plain English and have them independently research solutions, write code, debug errors, and deploy systems. These agents are capable of handling long, multi-step workflows – for example, setting up infrastructure, integrating tools, testing functionality, and documenting the results – often completing in minutes what previously took days.
As a result, programming is shifting from direct coding to orchestrating and supervising AI-driven processes, where developers focus more on defining problems, guiding agents, and reviewing outcomes, rather than implementing every technical detail themselves.
This style of development still requires a comprehensive understanding of engineering principles. As a result, it should not be confused or conflated with vibe coding, where code may also get generated rapidly, but lacks proper reviews, and is deployed with limited consideration for scalability, security or ownership. When critics warn about insecure “slop” code entering production, they are often pointing to failures of process and governance. That is a legitimate concern. But it is a concern about how teams operate, not about the inherent properties of AI-assisted development.
Even long-standing sceptics are beginning to acknowledge this distinction. Figures such as Linus Torvalds, Donald Knuth, and Robert C. Martin have recognised that AI can play a constructive role when it sits within disciplined engineering practice. The conversation is shifting away from whether AI should be used at all, and towards how it should be governed.
AI accelerates existing coding practises
Teams that have always kept strong review standards, testing rigour and capable leadership will not have those practises washed away if AI enters part of their workflow. For these teams, AI helps to increase the output without lowering standards. This is because it amplifies the culture already there.
The same principle applies to security. Secure software depends on threat modelling, dependency management, access controls and continuous monitoring. If those disciplines are weak, vulnerabilities will surface regardless of whether the first draft was written by a human or generated by a model. AI changes velocity, not responsibility.
For CIOs and technology leaders, this has practical implications. Prohibiting AI tools is unlikely to succeed and may drive their use underground – and given the advances in AI-driven coding, it may even amount to a serious competitive disadvantage. The more effective response is to strengthen the controls around them. Clear coding standards, automated testing, AI-driven code review, policy-driven code scanning, and runtime observability must become the default.
As AI accelerates the pace of software development, traditional human-centric code review workflows will struggle to keep up. When code can be generated much faster than it can be manually reviewed, human reviewers quickly become the bottleneck. The emerging model is one where AI systems enforce standards, review and validate code continuously, and flag risks at machine speed, while human engineers focus on defining requirements, architecture and higher-level oversight, rather than line-by-line review. AI belongs inside a structured software development lifecycle with guardrails, traceability and accountability.
A higher baseline for good code
With these in place, AI can raise the bar for what “good” looks like in our industry. Junior developers can receive immediate feedback on idiomatic patterns, security pitfalls and performance trade-offs. Senior engineers can offload repetitive tasks and focus on architectural coherence and long-term design. Test generation, documentation updates, refactoring and coding standards enforcement can become continuous activities rather than deferred clean-up exercises.
In many organisations, technical debt accumulates because improvement work competes with delivery deadlines. AI is reducing that tension by lowering the cost of maintaining standards. It can flag inconsistencies, suggest improvements and reinforce agreed conventions across large codebases.
This is particularly relevant for modern, data-intensive applications where distributed architectures and complex data models leave little margin for error. AI can support good engineering principles by improving validation, surfacing edge cases and increasing visibility into how systems evolve.
The debate around vibe coding muddies the waters about AI in our industry – the question is not whether AI will be part of software development. It already is. The differentiator will be how seriously organisations treat governance, architecture and long-term maintainability in an era of accelerated output.
Markus Nispel, Head of AI Engineering & EMEA CTO at Extreme Networks explains why your strategy and budget aren’t holding up your AI network initiatives – your execution is.
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The pilot phase is over. A third of companies have now scaled AI into production. For everybody else, the holdup isn’t strategy or budget – it’s execution.
Many AI initiatives struggle to deliver meaningful ROI for familiar reasons: missed business objectives and clearly defined KPIs, limited AI expertise, processes that haven’t been redesigned for AI, gaps in AI and data governance, and persistent challenges around data availability and siloes, quality, and preparation.
At the same time, supporting AI at scale requires infrastructure that can keep pace with continuous data flows, shifting workloads, and real-time decision-making. Traditional networks were built for stable, predictable workloads – not the speed, volume, and variability that AI-driven systems demand.
As AI moves from experimentation into production, networks must handle continuous data flows, adapt instantly to shifting demand, and operate at a scale that manual intervention simply cannot match. Meeting these demands requires a shift in network design and operation. This is where AI-powered, autonomous networking becomes essential.
Autonomous networking in practice
Autonomous networking allows systems to detect disruptions early, adjust traffic flows and configuration dynamically, and continuously optimise performance in real time, rather than reacting after issues occur.
This is made possible through multi-agent systems. Individual agents manage narrow tasks such as bandwidth monitoring, anomaly detection, and routing optimisation. Others sit above them, orchestrating them, interpreting user and business needs, and pulling the right capabilities together.
The system also works within defined governance frameworks. Humans stay involved in critical decisions, with full visibility, transparency and explainability into system activity, planning, reasoning and decision-making. Every automated action is logged and can be traced, verified and audited if necessary.
Trust, as in real life, is built through repeated positive outcomes. When networks keep making the right decisions, teams gain confidence, reduce oversight and can focus on higher-value work rather than constant troubleshooting.
This transition is already underway. Recent data reveals that 88% of organisations already rely on multiple AI-powered tools for networking and security operations. For stretched-thin teams, autonomous networking has grown into a necessity. Meanwhile, the strain is evident, with 92% of leaders reporting that AI places heavier demands on bandwidth and computing resources.
In this environment, network performance becomes a defining factor in whether AI systems can operate reliably and deliver measurable value.
Security through intelligent boundaries
Scaling AI also changes the security landscape. Networks are no longer connecting only users and devices; they are supporting a growing number of non-human identities. Agents across finance, marketing, engineering and operations interact with systems and data, often without human involvement.
Traditional security models were not designed for this level of autonomy. Access can no longer be broad or static. It must be tightly scoped, context-aware, and continuously verified. Every action must be visible and traceable.
But confidence is climbing. In fact, 93% of executives now say AI-powered networking reduces security risk rather than increasing it. That shift matters. It shows organisations are moving past scepticism and starting to take control.
That doesn’t mean we should be giving agents free rein. It means tightening the boundaries around what they’re allowed to do and enforcing strict agent governance. Access can’t be broad or permanent. It has to be specific, context-aware, and continuously verified. Every action should be traceable through a clear audit trail as AI for networking is implemented.
Where fabric architecture makes the difference
Networking for AI will not work without visibility. Real-time traffic monitoring and system connection mapping make enforcement possible. From there, segmentation becomes essential to contain risk and prevent unintended access or lateral movement – especially in the agentic era we have entered.
Fabric architecture enables this level of control at scale. By creating a unified network environment, it ensures that policies are applied consistently across all users, devices, and workloads.
This allows organisations to automatically isolate sensitive systems and tightly control how entities interact. If an AI agent behaves unexpectedly or is compromised, its access can be contained immediately, preventing wider impact.
Organisations that get this right bring identity, access control, visibility and threat detection together into a single, cohesive system. Autonomy can then operate within clearly defined boundaries, giving IT teams the confidence to guide, verify and trust every action the network takes.
Real-world application
The impact of this shift is visible across industries. Take retail in 2026. AI allows hyper-personalised shopping through cameras, IoT sensors, mobile apps and edge devices. Inventory data, customer preferences and behavioural patterns flow nonstop. Shelf labels, RFID systems and automated checkouts all require connectivity.
Healthcare follows similar patterns. AI supports diagnostics and robotic-assisted procedures, with patient data moving continuously across EMR systems, monitoring devices and analysis platforms. Without well-designed enterprise networks and access controls, those same systems can become exposure points rather than protection.
Manufacturing uses AI to predict equipment failures, optimise production lines and coordinate autonomous robots as sensors and machinery continuously communicate.
Across industries, without unified enterprise networks, stringent access control and defined human checkpoints, a single misconfigured AI agent could compromise sensitive data, disrupt operations or even endanger lives.
From experimentation to production
The era of experimentation is over. Organisations that operationalise AI effectively move faster, operate more securely, and respond to change with greater agility. Achieving this requires networks that can anticipate demand, enforce boundaries, and adapt continuously without compromising performance.
When that foundation is in place, AI can deliver on its promise – reducing operational friction, minimising disruption, and enabling teams to focus on innovation rather than maintenance.
Satish Thiagarajan, founder of Brysa, a Salesforce and data consultancy based in the UK, explains why high quality data is the foundation for using AI successfully.
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AI is everywhere in business right now, and for good reason. It offers the potential for better visibility across operations, fewer surprises, and more efficient use of resources. Most businesses have run a pilot to test that. Fewer have made it past one.
The stall point is consistent. Projects start well, attract investment, generate interest, and then stop delivering. The technology rarely gets the blame internally, but it tends to take it publicly. The real problem is almost always the data behind it.
Why data decides whether AI works
Business data systems weren’t designed to tell the whole story. They were each added over time to solve specific problems. One for finance, one for sales, another for HR, and a few more for marketing, support, and operations. Individually, they do the job for which they were intended, but they don’t really connect. And that’s fine, until you try to apply AI.
AI doesn’t think in terms of individual systems. It looks for patterns across everything, from customers and revenue to pipeline activity and service history. When that information is split across systems that don’t line up, those patterns break down, and what you’re left with is data that doesn’t quite agree with itself: records that don’t match, account data that conflicts with billing history, customer activity that looks different depending on where you check.
At that point, the problem isn’t a lack of data. It’s that the data doesn’t hold together. And if the data doesn’t hold together, the output won’t either.
What “AI-ready” actually looks like
Before AI can do anything useful, the data has to make sense on its own. Without that, AI is, effectively, guessing.
Context matters just as much. Data only becomes useful when it carries the relationships behind it. A customer record on its own doesn’t say much. Connect it to purchases, support history, and engagement activity, and you start to see why things happened, and that’s what AI needs to work with.
Timing forms another pressure point. Business moves quickly, but the data often doesn’t. If updates are delayed, or stitched together after the fact, AI will always be working from an outdated version of events. When data flows in real time, it reflects what’s actually happening, not what happened last week.
Then there’s continuity. When past performance connects directly to current activity, AI has something to learn from. Without that link, every decision starts from scratch. To get to that point, data needs to be consolidated in one place, and that’s where a CRM comes in.
Bringing it together in one place
Used properly, a CRM becomes more than a system for managing contacts. It acts as a central hub for accounts, opportunities, cases, campaigns, and service activity. AI doesn’t need perfectly clean data, but it does need to understand how work actually happens, and a CRM gives it that. Business data is constantly changing, and it needs to scale across teams, regions, and functions. Role-based access, audit trails, and clear permissions aren’t just nice-to-haves, they keep data usable as it grows.
Building a usable data foundation
The goal when faced with fragmented systems isn’t to replace everything, but to connect what’s already there. Customer data, comprising account histories, contact records, and interaction activity, is standardised and can be used across sales, marketing, and service, reducing duplication and avoiding inconsistency.
Workflows matter as well. If data quality depends on someone fixing issues later, it won’t hold. When validation happens at the point of entry, and updates flow automatically across systems, accuracy is built in, rather than an afterthought. When the data is reliable and connected, AI can support things like demand forecasting, lead prioritisation, or churn prediction, and good governance ties it all together.
AI doesn’t fail because the models aren’t capable. When the underlying data is fragmented, AI reflects that fragmentation. When the data is coherent and connected, AI has something solid to work with. That’s the difference between systems that generate outputs and systems that actually support decisions.
Satish Thiagarajan is the founder of Brysa, a Salesforce and data consultancy based in the UK. His company advises media, industrial, and services clients on using Data Cloud and Agentforce to turn signals into action. His work focuses on closing the loop between insight and execution in sales, marketing, and service.
U.S. Merit Systems Protection Board: Driving Operational Exellence
Interface revisits the important work of the U.S. Merit Systems Protection Board – an independent, quasi-judicial agency in the Executive branch that serves as the guardian of Federal merit systems. CIOCraig Thomas, explains the strategy behind the completion of Phase II of its case management journey, application stabilisation challenges and the deliberate rollout of secure artificial intelligence tools.
“At MSPB we field IT that is necessary, useful, cost-effective, manageable and scalable. If the technology doesn’t add value, doesn’t help us meet our mandate, I don’t want it… We see the active deployment phase as an incubator for support methodology, data and reporting strategy, and road-mapping the next five years of your application – use the active deployment phase, don’t just firefight.”
Morgan Street Holdings: Cybersecurity for Strategic Operations
Morgan Street Holdings is a privately owned investment and operating company whose portfolio includes global supply chain specialist HAVI, marketing and sourcing firm tms, consumer products brand Stanley, and hospitality and vending business Continental Services. Collectively, the organisation employs more 10,000+ people globally across more than 50 countries. Business Information Security Officer, Douglas Darden, talks building cyber resilience to enable the business and deliver cybersecurity for strategic operations.
“It’s not in us as a cyber team to just say no. Give us the opportunity to conduct a risk assessment and the cybersecurity function can become a business enabler.”
Also in this issue, we hear from Confluent on why AI is changing what data privacy means; learn from Kore.ai on how unmonitored AI agents are becoming enterprise AI’s biggest risk; and Xsolla explain why the real AI debate in video games isn’t about jobs, it’s about creativity.
Craig Gravina, CTO at Semarchy, on why successful AI at scale requites the transparency and trust only proper governance foundations can deliver
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Across the globe, organisations are aggressively approving AI budgets, hiring teams, and deploying models, yet the promised returns remain elusive for many. AI investments continue to grow, but measurable business value is not keeping pace with the level of spend.
The numbers tell a striking story. Semarchy’s 2026 research of C-suite executives reveals that 99% of UK organisations claim AI readiness – with 69% saying they’re completely ready. Yet 56% making significant investments cite data management as their top challenge, while only 33% are prioritizing it for investment. This 23-point gap reveals the fundamental issue: organisations are building AI applications whilst underfunding the data infrastructure those applications require.
Globally, fewer than 2% of enterprises successfully avoid data quality problems, even though 74% planned to increase AI spending in 2025.
This isn’t a failure of technology talent or algorithms; it’s a governance breakdown. The path to faster AI ROI doesn’t run through better models, but through better governance and transparency, embedded at the center of every AI initiative from the outset.
The Hidden Cost of Ungoverned AI
When AI operates without governance, the primary risk shifts from technical to reputational. Whereas technical failures are recoverable, reputational damage is far harder to restore. Consider consequential AI decisions: credit denials, medical recommendations, hiring shortlists. When these go wrong publicly, the fallout extends far beyond the technical team. When regulators or customers ask how they make their decisions, organisations without governance have no credible answers.
The regulatory pressure only amplifies this risk. Global frameworks such as the EU AI Act, emerging UK regulations, and sector-specific rules increasingly require explainability and traceability. Companies that cannot show how AI reached its conclusions risk fines, restrictions, and forced remediation.
Public misfires, such as biased outputs or visible system breakdowns like Grok AI’s high-profile failure on X last year, can rapidly erode customer trust and brand equity. For consumer tech brands especially, reputational cost can exceed technical cost exponentially. Internally, unexplained or unreliable AI outputs erode confidence, resulting in disengaged business users, stalled initiatives, and data leaders losing credibility.
These aren’t exceptional cases. They’re the predictable result of deploying AI without governance infrastructure.
The Governance Misconception
Many organisations still treat AI governance as a compliance checkpoint, bolted on just before deployment to satisfy legal or risk requirements. This narrow interpretation is exactly what makes governance feel like an obstacle. Organisations feel forced into a false choice: move fast and bypass governance or be compliant and accept delays.
In this scenario, projects slow to a crawl. Data teams spend months preparing ‘clean’ datasets specifically for AI, only to sit in approval queues. By the time governance clears, business conditions have changed or stakeholders have moved on to new priorities.
The answer isn’t less governance; it’s different governance. Governance should be intrinsic to how data is defined, managed, and delivered. When governance “travels with the data,” models consume information that already carries quality checks, lineage, access controls, and semantic context already present. AI initiatives don’t wait for approval gates – they consume governed data from the start. There’s nothing to retrofit or remediate later, because the controls were in place from day one.
This shift from governance-as-gate to governance-as-infrastructure is the key divide between organisations that struggle to realise AI ROI and those that achieve it.
Transparency as a Strategic Asset
Leadership often frames transparency as a compliance requirement, but in AI, it’s just as critical for development speed. When data lineage is clear, AI teams don’t have to spend weeks figuring out where data came from, how it was transformed, or whether they can trust it. They can focus on building and refining models instead of investigating data provenance, shifting effort from detective work to value creation.
Transparency also removes technical friction. With explicit semantic context, models interpret data correctly without layers of custom preprocessing or extensive feature engineering. Consistent access controls across data sources prevent last minute security reviews from stalling projects at critical milestones.
Transparency enables continuous improvement cycles. When you can see exactly which data influenced a decision, you can diagnose errors with precision rather than guesswork. You can identify which specific data sources introduce noise or bias into model outputs, and measure whether data quality improvements affect AI performance in production, creating feedback loops.
Organisations that try to reconstruct this visibility only during audits pay for it repeatedly in delays, extended debugging, and failed deployments. By contrast, those that build transparency from the start move faster through development, testing, deployment, monitoring, and iteration. Transparency then becomes a competitive advantage rather than compliance burden.
What Real AI Success Looks Like
Real AI success isn’t a single breakthrough model. It’s the ability to deliver value repeatedly and at scale. Successful organisations move from proof of concept to production without major rework or last-minute governance scrambles.
In these organisations, AI consumes the same governed data as everyone else. Agents and models access data through shared interfaces and policies, alongside business users and operational systems. There’s no special ‘AI data prep’ phase, no separate pipelines to maintain, and no governance gap to close before go‑live. AI is simply another consumer of trusted data.
Explainability comes by default. Because lineage, transformations, and quality metrics are already present in the data, teams can trace any decision without forensic effort. When governance is built into data flows, AI teams can retrain on fresh data without re-running compliance reviews, keeping models current as business conditions change. Iteration cycles shorten from months to weeks or days.
This foundation also enables decentralised experimentation with centralised trust. Teams can launch AI initiatives against shared, governed data products without creating shadow pipelines.
Getting There: Three Practical Shifts
Reaching this future state doesn’t require a multi-year overhaul or new tech stack. It needs a focused effort in how data is delivered to every consumer, including AI, with governance built in rather than added later.
First, stop treating AI data preparation as a separate workstream. If AI teams need specially cleaned and packaged data, you’ve created handoffs that introduce delay and governance risk. Instead, provide data that all consumers can trust and use immediately.
Second, embed semantic context with the data. AI needs business context and meaning, not just schemas. What does ‘customer’ mean in this specific context – prospect, active user, former customer? What business rules apply, and what relationships matter for decision making? This semantic layer enables AI to interpret data correctly without custom workarounds.
Third, make lineage and quality observable by default. When lineage, quality scores, and transformation history are always available, debugging, compliance, and continuous improvement happen without emergency efforts.
Yet our research reveals the challenge: whilst 56% of UK organisations cite data as their top AI challenge, only 33% are prioritising investment in it. This misalignment between stated priorities and actual investment is precisely why AI initiatives continue to struggle, regardless of confidence levels.
The ROI Equation
The companies that achieve AI ROI faster aren’t the ones with the biggest budgets or the most sophisticated models – they’re the ones that eliminated the friction between data and AI consumption through proper governance infrastructure.
When governance is intrinsic, projects don’t wait for approval gates. With built-in transparency, debugging is fast, and audits are painless. The result? Shorter time to value, lower risk of public failure, reduced regulatory exposure, and AI investments that deliver measurable impact. These aren’t abstract benefits – they show up in project timelines, deployment success rates, and business KPIs.
The technology is ready. The question is whether your data governance is ready to support AI at scale. Success requires the transparency and trust that only proper governance foundations can provide.
Peter Pugh-Jones, EMEA Field Chief Data Officer at Confluent, on why the challenge for companies will not be building AI systems but standing behind the decisions those systems make
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For years conversations about data privacy have centred on conventional dangers, like breaches from outside, or leaks from within. The assumption has been that the biggest risk lies in information either being inadequately protected or improperly handled.
As we move deeper into the AI era, that picture is shifting. A more complex privacy challenge that we’re now faced with comes from how data is used once it sits inside an organisation.
AI is a crucial part of everyday business operations. It automates processes and shapes decisions. Data is no longer simply stored or shared; it’s interpreted and acted on at incredible speed, in ways that are difficult to trace or explain.
Decision-makers recognise the importance of accessing this data. 80% of business decision makers say without up-to-date data, businesses can’t be fully confident in decisions.
As such, the systems and rules that dictate how AI is used have never been more important. Business leaders need to be able to understand and explain how automated decisions are reached and the data that informed it.
Governance has to sit at the centre of the privacy conversation — not as a compliance exercise, but as the framework for the responsible application of AI. Without that foundation, organisations are likely to expose themselves to regulatory and reputation risk that they’re simply not equipped to avoid.
Privacy Now Depends On Explanation
Governments worldwide recognise the need for these AI frameworks. Europe’s AI Act has drawn the most attention, but equivalent regulatory approaches are appearing in markets from South Korea and the UK.
The result is that companies deploying AI systems are held to higher standards than ever when it comes to demonstrating that they use AI in a lawful, appropriate manner. As such, many businesses are discovering a gap between deploying AI and understanding it.
Implementing a model that produces useful outputs is relatively straightforward. Explaining how those outputs were created and which data shaped them is much harder. Where that clarity is missing, privacy becomes an operational problem.
Organisations may need to pause or redesign systems already in production because they cannot explain how those systems reached their conclusions. Some companies may even find themselves withdrawing products already released into the market.
So: what challenges are these organisations facing?
The Segmentation Challenge AI Introduces
One significant obstacle is segmentation. Companies need to understand who their systems interact with and which rules apply to each, and this isn’t always clear.
Businesses have historically segmented audiences by geography, demographics or behaviours. AI creates a more demanding requirement in that the systems using it must recognise when different safeguards should apply to different groups of people.
Regulation is pushing this further especially around children and vulnerable users. Content recommendations and automated responses that are acceptable for one group can become inappropriate for another.
The difficulty is that many AI systems are trained on broad datasets gathered across the internet. That breadth makes it harder to understand whether information came from a reliable source, or whether it should appear in a particular context. Without stronger segmentation, organisations risk losing control over how AI behaves in real interactions — and if that AI behaves inappropriately, it’s likely to lead to a breach of regulation.
Why Governance Cannot Be An Afterthought
Another mistake appears repeatedly in businesses deploying AI and modern data platforms. Governance is treated as something that can be dealt with later once systems are already running, with isolated but exciting pilots running well on smaller data sets often leading businesses to try and run before they can walk.
The challenge is even sharper in real time environments where data moves constantly between applications and teams. When governance is not designed into those flows from the beginning it quickly becomes a bottleneck.
In practice, the most effective point to govern data is when it first enters the business. Once information is already moving through systems, feeding models and generating outputs introducing controls becomes far more complex.
Many companies only realise months later that they’re lacking certain controls needed to manage risk. If they were never properly built in, it’s an expensive, time-consuming, incredibly nuanced task to put the genie back in the bottle.
The Shared Responsibility For AI Decisions
As with any cybersecurity approach, the human link can also be the weakest in the chain.
Responsibility for AI risk is often treated as if it sits neatly with one team. In many companies the assumption is that specialists are handling it, whether that is data scientists, a compliance function or a single executive role. The underlying thought is: “this is not my problem — someone else is dealing with it.”
That assumption creates problems. AI systems are trained on vast datasets and their behaviour is not always easy to predict without close scrutiny. When governance is treated as a delegated task, important questions can slip through the cracks unanswered.
Decisions informed by AI still sit with the business using the technology and ultimately with its leadership. Without senior oversight and a clear understanding of how systems are built, trained and governed privacy risks tend to surface long after those systems have become part of everyday operations.
The Question Organisations Should Be Asking
Organisations have the capability to build powerful AI systems. The harder question is whether those systems can stand up to scrutiny.
That means understanding where data originates, recognising differences between users and putting governance in place before automation begins to scale. It’s not enough to just co-opt an impressive new platform and give it access to your data.
In an AI driven environment privacy does not sit somewhere at the end of the process. It shapes how systems are designed, deployed and managed from the beginning.
In the years ahead, the challenge for companies will not be building AI systems. The challenge will be standing behind the decisions those systems make.
Chris Tredwell, Chief Operating Officer and Charis Thomas, Chief Product Officer at Aqilla, on why the question is no longer whether to adopt AI, but whether processes, governance structures and training pathways are ready for the workforce
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Have you ever got into an old car with a Gen-Zer? If they were driving, chances are you wouldn’t have got very far. A recent survey has found that 39% of 14–29-year-olds couldn’t identify an ignition key. Proof, if it were needed, that once technology advances, old ideas are quickly forgotten. This isn’t just happening in cars. The internet and social media have produced their own native generations – people who have never known a world without those technologies.
The same pattern is starting to emerge with AI. That means Gen Z and Millennials are about to experience a similar shift. The first wave of true AI natives will soon enter the workforce – a cohort that has never known a world without AI.
AI- The New Normal
People’s reactions will largely depend on their experience with AI. But one thing is certain: these graduate and entry-level employees won’t need to be convinced of its value. They’ve already seen what it can do, so if it’s missing, disbelief – or frustration – is likely to follow. It’s a bit like broadband. Here in the UK, it’s simply the standard we all expect. We don’t stop to think about how that connectivity reshaped our lives, helped us work from home or allowed us to stream high-definition media.
Many organisations are still in the early or experimental phases of AI adoption. They might be using the technology to automate basic email inbox management and take meeting minutes. Meanwhile, those further ahead of the curve are exploring more advanced tools and assessing where automation can be safely deployed, particularly for reporting and analysis.
But AI natives won’t see these use cases as experimental. In fact, they probably wouldn’t even refer to them as use cases. It’s just normal, like using a search engine rather than visiting a library to carry out research.
Prompting New Behaviour
Perhaps the biggest difference, however, is where organisations may integrate AI into their existing workflows, AI natives are more likely to structure work around it from the outset.
For them, work tends to start within an AI system, defining the objective clearly, setting constraints, and effectively “briefing” it, before iterating quickly and refining outputs as they go. For AI natives, this kind of prompt-based mindset isn’t a specialist skill; it’s simply how they approach tasks.
This is a fundamental shift. For AI natives, the question isn’t “Should we use AI here?” It’s “Why can’t I use it for this piece of work?” When their expectations collide with more cautious, process-led environments, friction is almost inevitable. Not because one approach is right and the other is wrong, but because both sides are starting from completely different assumptions.
Skills Transfer and Mentoring
But how does the need for AI natives to understand and work through basic manual processes coexist with intuitive prompt-based thinking? Should AI use come with experience-based restrictions in the finance sector? For example, do your three years first, and then you can use the tools.
It’s probably not what AI natives want to hear, but there is logic behind the approach. Learning the manual processes behind automation will enable new recruits to apply the necessary checks and balances to system outputs — putting them in a position to verify data rather than passively accept it.
Without taking this step, there’s a real risk that people will lose the ability to question the outputs they’re working with. That, in turn, has implications for how people are taught. Whether in an educational setting or on the job, that training needs to help AI natives understand the logic behind the systems they’ll be working with.
Lurking in the Shadows
If AI natives encounter friction when trying to use the technology, there’s a risk they’ll seek informal workarounds. Organisations have seen similar patterns before with personal devices or early cloud adoption. With AI, the risks are more focused on data, traceability, and accountability than on system access and security, though these remain important considerations.
Rather than restricting AI use, organisations are already beginning to reshape it by building oversight into how systems are used. That might mean making the AI’s working assumptions more visible and requiring humans to validate outputs. It might also require AI systems to signal their confidence in those outputs and to request manual checks. Over time, this reduces risk and creates an environment where people can work confidently with AI without losing sight of who is responsible.
This approach also challenges a common narrative. Much of the current discussion around AI focuses on job displacement, but the reality is more nuanced. The issue isn’t a simple replacement of human intuition and experience, but how those qualities evolve alongside increasingly capable systems.
Rather than removing the need for people, this managed shift reinforces it. Greater emphasis is placed on human-in-the-loop models, in which individuals with a deep understanding of AI can interrogate, challenge, and interpret system outputs.
A different starting point
So, what happens next? AI tool adoption, for sure. But it goes far deeper than that. Getting ready for AI natives means shifting to a different starting point – and learning to “live in the prompt”.
As AI natives begin entering the workforce and eventually move into leadership roles, the expectation won’t be that AI is introduced; it will be that it is already there. That shift reshapes how people think, learn, and approach tasks from the outset. It will also change how tasks are conceived, carried out and reviewed. The ability to configure, interrogate and challenge systems will become as important as the ability to interpret their outputs.
For organisations, the question is no longer whether to adopt AI, but whether their processes, governance structures and training pathways are ready for a workforce that already assumes it – and will expect to work that way from day one.
Adnan Patka, Enterprise Manager – Blockchain, AI and Web3 at OVHcloud, on why the convergence of blockchain and AI heralds a brighter future for both
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Anthropic’s Mythos team recently assessed various LLM technologies, scoring ChatGPT against Gemini, Grok, Claude and others. The metrics included trustworthiness, but also user deception, sycophancy, encouragement of user delusion and co-operation with human misuse.
Clearly, there’s no doubt that AI can be an enormously positive transformational force: the development of AlphaFold, whose founders won the Nobel Prize, showed us that very clearly. But when AI is being assessed for encouraging ‘user delusion’, it’s clear that it still has a PR problem.
In fact, AI has more than just a PR problem: research from McKinsey highlighted that although almost two thirds (64%) of decision-makers reported that AI was accelerating innovation within their organisation, less than half (39%) could actually report positive financial impact on the business as a result.
The AI Trust Gap
AI doesn’t exist in a vacuum, and the interplay of AI with other technologies is beginning to come into focus. We see that blockchain has great potential to complement AI, providing the transparency and trustworthiness that it needs. In a recent study of Web3 professionals, almost three-quarters (70%) said that blockchain had the potential to fill AI’s trust gaps.
But where else are these issues coming from?
It’s overly glib to say that people and businesses don’t always trust AI. What our research told us was that 32% of Web3 experts believe that privacy is AI’s main challenge. Historically, AI systems have been shown to be biased, and data has often been collected by AI systems for training or other uses – sometimes without user permission. One in seven (14%) of the study also reported that they believed AI needed more transparency to be trustworthy.
Blockchain’s Potential for Supporting AI
Blockchain is well-placed to help tackle these issues. Blockchain was originally created as a database with clear traceable links between items, without the need for a single governing authority.
For example, consider an AI system which optimises food supply chains, allowing a supermarket to maintain lower prices. In an environment where people may distrust AI, they may suspect that the system is simply buying lower-quality stock and shipping it more affordably, potentially meaning shorter shelf-lives and less tasty tomatoes! However, blockchain systems can help to track the provenance of these items, proving where the tomatoes were grown, when, by who, and the subsequent shipping and treatment of them, all in a publicly accessible ledger.
Blockchain systems also automatically check the validity of transactions and link them together in a way that makes fraud very difficult. Changing one transaction generally requires changing the entire ‘course of history’ or having significant control (specifically, controlling over half of the nodes) of an entire blockchain, which is usually unfeasible in most large, public blockchains today.
Delivering Benefits with Blockchain
There are also a number of cases where blockchain can bring functionality benefits to AI systems. For example, as we saw, privacy is a significant concern for AI users, but blockchain systems are able to authenticate securely without exposing user credentials, through self-sovereign identity. This is especially useful where AI systems need to interface with other tools that might not be as trustworthy as the core system.
Blockchain principles can also support data exchange in federated learning systems, where data is shared between different machine learning platforms. Blockchain can establish the validity of this data while still preserving its integrity and user privacy. This means that AI can be trained on data that is consistent and robust – without breaking trust.
There are a huge number of applications for this kind of technology, but unfortunately, it’s not all plain sailing.
Blockchain’s Challenges
The industry is undergoing a huge change today. Interest in stablecoins – a crypto asset which is attached to the value of a more stable asset like a traditional (fiat) currency or other physical commodity – has quadrupled since October 2025. Large, well-established organisations are considering what private and public blockchains can do for them, which could ultimately have a positive and stabilising impact on the regular bear and bull cycle that the crypto market experiences.
However, some more negative parts of blockchain’s legacy persist. Almost two thirds (61%) of our research sample said that concerns about blockchain’s credibility were having an impact on its integration into the enterprise ecosystem. This includes blockchain’s links with the more unsavoury side of cryptocurrency, including cybercriminal activity. Over half (54%) simply said that there wasn’t enough understanding about blockchain’s capabilities and what it could really do, holding IT decision-makers back from making the most of it.
At the same time, this isn’t a one-way street. AI’s capabilities – in particular, automated, agentic AI – can carry out blockchain activities without needing user input. For example, AI can issue transactions or analyse data and summarise it for monitoring systems. There is tremendous potential for AI to support many different parts of the blockchain ecosystem, summarising trends, automatically carrying out tasks, or executing smart contracts without human intervention.
A Mutually Beneficial Relationship
Blockchain and AI have enormous potential to support each other. There are some organisations already starting to integrate blockchain into AI and vice versa, but it will still be a number of years before the relationship is mature. That said, two-thirds of the Web3 experts we surveyed (67%) believed that it’d take roughly one to four years, with only eight percent saying that it might take five to six years.
Indeed, given the enormous benefits that blockchain can bring to AI, not to mention the rapidly increasing acceptance of blockchain technologies across the financial sector in particular, the convergence of these two technologies is almost inevitable. If we can push past the challenges, the future is bright for both blockchain and AI.
Adam Gale, Field CTO for AI & Cybersecurity at NetApp, on why cybersecurity in the AI era will depend on two capabilities: detecting abnormal behaviour as early as possible and ensuring trusted data can always be restored
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How can we not talk about cyberattacks when talking about AI? Barely a week passes without another story on AI-generated scams, deepfake voice fraud or ransomware becoming more sophisticated. Security teams are starting to see the effects in their own environments too. In many organisations, the first sign is simply the number of alerts appearing in security dashboards each morning.
Generative AI tools are lowering the technical barriers that once limited cybercrime. Cybercriminals can use it to generate convincing phishing emails in almost any language, code malware variants in minutes, or automate the process of scanning infrastructure for vulnerabilities. In fact, cyberattacks are now being generated at industrial scale. The good news is that AI can be an equally powerful tool for organisations in defending against this deluge.
When threats multiply faster than defences
It’s important to understand how quickly – and how far – Ransomware-as-a-Service has come in recent years. Large language models (LLMs) can now generate convincing phishing campaigns tailored to specific roles or individuals. Threat actors are already using them to scrape publicly available data and craft highly personalised emails that mimic internal communications. Malware authors are experimenting with AI to produce polymorphic code that changes its behaviour between executions, making signature-based detection far less effective.
Voice cloning is another rapidly emerging risk. In several reported cases, help desks and IT support teams have been persuaded to reset credentials after receiving calls from voices that sounded identical to employees or senior executives. What previously required careful social engineering can now be automated and scaled.
Many of these incidents succeed because cybersecurity platforms were built around a fundamental assumption: threats evolve incrementally and can therefore be detected by identifying known patterns. These include signature-based detection, rule-based alerting and static threat indicators. However, the problem is that this model breaks down quickly when cybercriminals can generate new variations faster than those signatures can be updated.
Additionally, monitoring tools generate huge volumes of warnings, many of which turn out to be harmless, but IT security teams still need to investigate them. When hundreds of alerts appear each day, this creates a risk of alert fatigue which may see genuine threats hidden in a sea of false alarms. As a result, malicious actors are now operating at machine scale while many defensive processes still rely on human triage, and this mismatch is becoming one of the defining problems in modern cybersecurity.
Fighting AI with AI
On the bright side, all that AI-powered automation can also be used for defence – not just offence. For example, AI can strengthen cybersecurity by examining activity across systems and data and identifying behaviour that deviates from normal operations.
Instead of relying only on known hacking techniques, AI-driven systems can monitor how data is accessed and modified across environments. If behaviour changes unexpectedly, systems can raise alerts or trigger automated responses. Detecting anomalies early matters even more as breaches become more automated, because the earlier suspicious activity is identified, the easier it is to contain the impact.
Automation also reduces the burden on security teams. Analysts spend less time reviewing routine alerts and focus more on investigating genuine threats. As organisations increasingly rely on AI systems, protecting the data those systems depend on becomes central to cybersecurity strategy.
This is especially crucial today, as modern enterprise data environments are highly distributed. Data is constantly moving between on-premises systems, cloud platforms, analytics pipelines and AI training environments. In turn, every transfer, replication process or API connection creates another potential attack surface.
This also means that storage plays an elevated role in supporting cyber resilience. Capabilities such as storage-level anomaly detection, autonomous ransomware protection and immutable snapshots allow organisations to identify suspicious data activity and preserve trusted recovery points. If systems are compromised, clean versions of data can be restored quickly without paying ransoms or rebuilding environments from scratch.
Building Confidence in AI Environments
AI is rapidly industrialising cybercrime. Malicious actors can now generate new techniques, test them and deploy them at a pace that traditional security operations struggle to match. Therefore, defence strategies must adapt accordingly. That means shifting from signature-based detection to behavioural analysis and from manual investigation to automated response. In other words, defending against AI-powered threats will increasingly require AI-powered security.
Ultimately, cybersecurity in the AI era will depend on two capabilities: detecting abnormal behaviour as early as possible and ensuring trusted data can always be restored. In a world where attackers can generate new threats in minutes, organisations that cannot protect and recover their data quickly will struggle to trust their systems at all – including the AI tools they increasingly rely on.
Tom Parsons, Commercial Director at Good Energy, on why the next phase of AI infrastructure will not be judged solely on speed or capacity. It will also be judged on how well it integrates with a constrained, transitioning energy system
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Artificial intelligence is reshaping the UK’s economy. Less visible, but equally significant and important in the national debate, is the potential pressure it is placing on the energy system that underpins it. As AI adoption accelerates across sectors such as finance, healthcare and logistics, electricity demand from data centres is rising at a pace that few anticipated at a time when grid capacity is already under strain.
Across the UK and Europe, data‑centre operators are encountering long connection queues, constrained local networks and growing scrutiny over the real‑world carbon impact of powering AI‑driven growth. Against this backdrop, the way electricity use is measured and reported is coming under closer examination. It is no longer enough to ask whether data centres are important for economic growth, but also how we can build and run them in a way that doesn’t drastically increase demand and indirectly impact household and commercial energy prices.
There are already tangible early signs of how this could play out on the ground. In parts of the UK where clusters of large data centres are being developed, local stakeholders and consumer groups have warned that the scale and concentration of new electricity demand risk placing significant strain on local grid capacity. Addressing that strain will require substantial network upgrades, and without clear rules on how those costs are allocated, there is growing concern that households and smaller businesses could ultimately face higher network charges.
Data Centres
These issues have now reached Westminster. In February, MPs launched a formal parliamentary inquiry into the impact of data centres on the UK energy system, explicitly examining their effect on electricity demand, grid congestion and energy bills, and questioning whether the pace of approvals is compatible with wider energy affordability and net‑zero objectives. The inquiry reflects growing political concern that, without a clearer plan for how data centres are powered and integrated into the energy system, the rapid expansion of data‑intensive infrastructure risks increasing system costs and distorting local energy markets.
The challenge, however, is not that data centres are inherently incompatible with a cleaner, more affordable energy system, but that the way many currently procure and account for power has not kept pace with the scale and intensity of their demand. Many of the tools needed to manage this already exist and are widely used across homes and businesses, from smart meters and time‑of‑use tariffs to demand‑side response and on‑site generation. These measures help shift consumption away from peak periods, reduce pressure on the grid and better align electricity use with renewable supply.
Renewable Energy
While applying these approaches at data-centre scale is more complex, doing so more systematically could help to reduce pressure on the grid and improve how large digital loads interact with a renewables-led system. If paired with stronger requirements around additional clean generation and long-term power procurement, data centres could play a more constructive role in supporting new renewable capacity – rather than simply adding to overall system demand.
When powered by genuinely 100% renewable electricity that is matched on an hourly basis, data centres can move beyond offsetting their impact and instead play a more constructive role — supporting investment in new clean generation, improving system flexibility and supporting progress towards a greener, more resilient UK grid.
Hourly matching aligns electricity demand with renewable generation on an hour‑by‑hour basis, rather than retrospectively over a year. In practical terms, it provides a clearer view of when consumption coincides with clean power and when it does not. The result is not a perfect system, but a more honest one — offering greater transparency about emissions and the limits of current renewable supply.
AI Infrastructure
For AI infrastructure, that visibility is increasingly important. Data centres operate continuously and often have limited flexibility to shift demand. Without clearer insight into when clean power is available, growth risks hard‑wiring higher emissions into local grids already under pressure. Time‑based data can help operators identify where on‑site generation, storage, or modest load‑shifting could meaningfully reduce reliance on carbon‑intensive electricity.
This shift is already influencing how some operators think about scaling. Rather than focusing solely on annual renewable coverage, there is growing attention on pairing large‑scale on‑site solar and storage with more granular measurement of how power is used. In some large deployments, substantial on‑site solar and storage is now being paired with granular measurement, allowing operators to see – often for the first time – when clean power is genuinely meeting demand.
Time‑based matching is also starting to change the conversation around carbon claims. Annual averages can suggest progress that does not always align with the physical realities of the grid. Hourly data, by contrast, makes those constraints visible. It highlights where further investment is needed and where system‑wide challenges – such as storage, flexibility and network capacity – remain unresolved. Hourly matching does not eliminate the challenges of powering energy‑intensive infrastructure, but it does impose the transparency the system needs.
Energy Reporting
Some UK suppliers and platforms are now making this level of data available to business customers, reflecting a broader move towards more granular energy reporting. While approaches vary, the underlying direction of travel is clear: greater transparency, better alignment with how the grid actually operates, and fewer assumptions built into sustainability claims.
As AI continues to scale, scrutiny from regulators, investors and local communities is likely to increase – particularly where trust in sustainability claims is already fragile. Data‑centre growth that cannot demonstrate how it interacts with the energy system – not just on paper, but in practice – will face tougher questions.
The next phase of AI infrastructure will not be judged solely on speed or capacity. It will also be judged on how well it integrates with a constrained, transitioning energy system. Understanding not just how much renewable energy is procured, but when it is actually used, is becoming an increasingly important part of that equation.
Andrea Babayan, Demand Growth Strategist at Ipsotek (an Eviden business), on why the real competitive advantage will not belong to the hub with the most advanced analytics stack but the one that can demonstrate proportionality, resilience and clarity of purpose
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Transportation hubs are not simply adopting new technologies. They are reconfiguring how they operate. Digital identity systems, AI-driven video analytics, environmental sensors, drones and upgraded communications platforms are converging into core processes.
What was once framed as incremental innovation is now embedded in passenger throughput, dispatch coordination, platform management and perimeter oversight. Modernisation is no longer an upgrade cycle. It is a redesign of the operating model.
The more difficult question is not what these systems can detect, but whether institutions are evolving with equal deliberation.
Identity Border Systems as Throughput Infrastructure
Border modernisation makes the shift explicit. Europe’s Entry/Exit System (EES) illustrates how biometric identity is no longer merely a security control. It is a flow determinant. When enrolment, verification and exception handling are integrated into primary processing lanes, identity becomes inseparable from capacity planning and operational resilience.
The expansion of touchless identity verification in US airports reflects the same structural move. Identity confirmation now shapes staffing equations, lane geometry and peak-period modelling.
When these systems perform, throughput improves. When they stall, queues lengthen – and confidence declines.
Not all identity applications carry equivalent risk. Verification within controlled checkpoints differs materially from open-ended identification in public space. Proportionality must therefore be embedded at design stage. Governance is not a constraint imposed after deployment; it defines the parameters within which identity systems can scale without undermining trust.
In jurisdictions such as the European Union, where the AI Act establishes a formal framework for high-risk systems, institutional readiness must include formal risk assessment, auditability and transparent oversight mechanisms as foundational design requirements – not retrospective safeguards.
Border digitisation is not merely technological enhancement. It is throughput engineering under regulatory and operational accountability.
Sensor Convergence and Institutional Responsibility
Beyond borders, transport networks are integrating AI-powered video analytics with environmental sensors, access control systems and communications infrastructure. The objective is coordinated situational awareness – not visibility for its own sake.
Crowd density analysis can inform service adjustments. Platform anomalies can trigger structured escalation. Perimeter events can be validated through multiple inputs before operational response.
This convergence strengthens detection capability. It also expands institutional responsibility. As data streams intersect, purpose limitation, retention discipline, interoperability standards and escalation protocols must be clear. Integration increases capability – and scrutiny.
Governance is not a brake on innovation. It is the framework that allows it to scale responsibly. Alert volume creates noise. Decision quality creates advantage.
The Operational Decision Core
Modernisation is increasingly coalescing into centralised operational environments – decision cores that synthesise data across security, passenger flow, maintenance, access control and communications.
This direction aligns with the formalisation of collaborative operational models such as the Airport Operations Centre (APOC), which positions airport coordination as a structured, cross-stakeholder decision environment rather than a collection of siloed control rooms.
What was once compartmentalised becomes interconnected. The value lies not in dashboards, but in how insights shape staffing allocation, capital prioritisation, service recovery and commercial performance. Yet greater visibility does not eliminate boundaries. Cross-functional insight should improve coordination, not dilute accountability. Rich operational data must remain tied to defined purposes.
As AI systems transition from pilot to operational dependency, resilience becomes decisive. Designing for stress is therefore as important as designing for efficiency. Compliance cannot be retrofitted. Interoperability cannot be improvised. Institutional maturity cannot be assumed.
What Comes Next
Transportation hubs are becoming decision environments embedded within physical infrastructure. Cameras function as sensors; identity shapes throughput; and analytics informs operational judgement. But the defining difference over the next decade will not be technological sophistication; it will be institutional discipline.
In my view, the real competitive advantage will not belong to the hub with the most advanced analytics stack. It will belong to the one that can demonstrate proportionality, resilience and clarity of purpose – consistently, transparently and under pressure. Smarter systems are inevitable. Smarter institutions are a choice.
How Sonata Software is driving CPL Aromas’s technology transformation
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When CPL Aromas needed a software partner with deep technical expertise to guide its transformation, it chose Sonata Software. Theirs is a relationship that’s been going strong for eight years and counting – since Alfred Muthunathan, CIO at CPL, joined the team. “He was looking to transform the IT landscape,” says Uttam Hazari, Head of UKI & Europe at Sonata Software. “But before he could embark on that journey, it was important to ensure that the foundation was stable first.”
The goal was very simple: to ensure CPL could continually strengthen its system performance and improve reliability across its global operations. “It was growing at a rapid pace,” says Hazari. “However, the systems and data weren’t well integrated. It was important for us to work collaboratively with CPL to not only analyse the processes, but also stabilise the systems and the applications, and allow the company to regain its confidence.”
This early engagement reflects Sonata’s Frontier Firm approach – combining deep engineering expertise with business-aligned transformation to create resilient digital foundations.
A transformational relationship
Alongside the ERP support, Sonata partnered with CPL Aromas to develop its first B2B customer portal, enabling users to securely access orders and shipment information, thereby improving transparency and reducing manual interactions. “The early successes formed the foundation for a long-term digital transformation partnership built on trust, responsiveness, and delivery excellence,” Hazari adds.
This momentum reflects Sonata’s broader approach to building long-term, value-driven digital partnerships.
“Sonata Software is committed to building strategic partnerships that help organisations accelerate their digital transformation journeys. Today, our clients are looking for initiatives that move faster, reduce risk, and deliver clear, measurable business impact. As a Microsoft Frontier Partner, we bring deep expertise in translating advanced Cloud and AI capabilities into tangible outcomes. Our expansion across the UK and Europe marks an exciting phase of growth for Sonata, and our transformation journey with CPL Aromas is a key pillar of that strategy. We are privileged to have CPL Aromas as both a valued customer and a trusted partner.” – Anthony Lange, Chief Revenue Officer at Sonata Software
What began as a simple collaboration has evolved into a multi-phase, enterprise-wide transformation journey over the past eight years. A couple of years in, Sonata’s role expanded. “As part of that, we helped CPL rebuild and transform their core infrastructure,” says Hazari. “We also helped to implement Azure Site Recovery, stabilise IMS operations, strengthen identity and endpoint security, and establish modern monitoring with Azure Sentinel. And this is just the beginning.”
From ERP to infrastructure management to full enterprise modernisation, Sonata has been with CPL every step of the way, including the end-to-end global migration from the legacy ERP platform to Microsoft Dynamics 365 across multiple legal entities. “As we speak, the engagement has matured into a data-driven, AI-first innovation spanning across Dynamics 365 and the customer engagement platform, RPA automation, and agentic development,” says Hazari.
AI-first
Developing an AI-first approach is at the core of CPL’s current and ongoing focus, and it’s an area where Sonata has played a major role. “Sonata has been working very closely with CPL Aromas in its AI-first evolution – not just technically, but strategically as well,” says Hazari.
One of the key contributions to this has been building a modern digital core through the Dynamics 365 ERP program, enabling real-time visibility, standardised processes, and scalable operations. “These things are a prerequisite for effective AI deployment,” says Hazari. “Without that, you can’t just bring in AI to do the magic. Data is the most important ingredient. If the data isn’t right, the AI won’t be either. Then came automation at scale through RPA initiatives, which reduced the manual workload and increased process efficiency.”
More recently, Sonata has been working with CPL to pioneer its agentic AI adoption. “One of the recently deployed agents is a custom planner agent, which is designed to automate one of CPL’s most complex manufacturing processes,” Hazari explains. “This represents a major leap towards AI-driven decision intelligence across operations. Together, these initiatives have established the technology, data, and automation foundation for CPL Aromas’ AI-first strategy.”
These capabilities exemplify how Frontier Firms operationalise AI – embedding intelligence into core processes to drive real-time, autonomous decision-making.
The four pillars
Sonata is now helping CPL deliver value across four strategic dimensions. These consist of:
Operational resilience and security
Standardisation of global processes and transparency
Enterprise-wide data platform
Agentic AI-driven decision-making
“Bringing these initiatives together is transforming CPL into a digitally agile and AI-ready enterprise,” says Hazari. “That’s the value we’ve been able to deliver.”
The goal is for this relationship to continue for many more years. Sonata will continue to contribute towards CPL’s incredible growth, and as this collaboration progresses, it will be centred on CPL’s AI-first operating model.
“Scaling agentic AI beyond the initial successes is very important,” says Hazari. “That’s across all business functions, whether it’s finance, quality control, demand forecasting, supply chain optimisation, and even customer engagement. The idea is to help CPL in democratising AI across business functions, to reach a state where they’re able to create their own workflows and generate their own agents.”
As well as this, it’s about continuous automation and expansion to further reduce manual effort wherever possible from current processes. “Lastly, we’re working on enhancing digital sustainability,” says Hazari. “This includes ensuring that systems remain scalable, secure, and ready for emerging AI technologies. Technology is changing; AI is changing very rapidly. It’s extremely important for Sonata, as the strategic partner, to make sure we’re able to keep CPL Aromas one step ahead in this journey.”
Appian: Why AI is Putting Better Business Within Every Organisation’s Reach
Our cover story highlights how AI is putting better business within everyone’s reach.Mark Talbot, Director – CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. “Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day.”
CPL Aromas: How a Leading Fragrance House is Using AI to Amplify Creativity
In the world of retail, a leading fragrance house uses AI to amplify creativity. Alfred Muthunathan, CIO at CPL Aromas, explains how the family-owned business is using AI as a strategic capability to support creativity and accelerate innovation. “We didn’t bolt AI onto our systems; we redesigned the organisation, so AI is native to how we operate… Our new system takes away the workload from perfumers and has allowed us to create something that always keeps the nuances of our industry at its core.”
Vibrant Capital: Scaling AI on Main Street
Shadman Zafar, Founder & CEO of Vibrant Capital, is building a CIO-led model for enterprise transformation. Vibrant Capital is an operator-led investment and company-building platform focused on scaling AI in the real economy. “We don’t spray investments across hundreds of AI startups. We curate a portfolio with purpose – selecting companies that solve the real mission-critical problems CIOs face in scaling AI adoption.”
Also in this issue, we learn about the supply chain transformation journey at Swiss sportswear brand On, unpack the latest AI readiness research from Snowflake and hear from Hitachi Vantara about the importance of strong data foundations for the best utilisation of AI.
Snowflake’s UK research reveals that while many continue to invest in artificial intelligence (AI), the country’s businesses are in the early stages of finding productivity gains at scale
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UK productivity remains a longstanding economic challenge, with policymakers consistently positioning AI as a key lever for growth and competitiveness.
Snowflake’s findings show AI investment and experimentation amongst businesses though impact so far is varied. 45% of UK organisations say AI is delivering early gains or in specific use cases, though 23% are already seeing it delivering productivity improvements at scale. Buoyed by this thought, most organisations expect AI investment to increase over the next 12 to 24 months, with just 1% planning to decrease spend. Businesses continue to back thetechnology as a strategic priority, demonstrating confidence that productivity breakthroughs will come.
The UK AI Report
Conducted by YouGov on behalf of Snowflake, the report surveyed 500 senior decision-makers including CEOs & CFOs from large UK organisations, spanning key industries including public sector, manufacturing and financial services. These industries are seen as being vital to the Government’s AI and productivity ambitions. Its 2025 AI Opportunities Action Plan aims to boost the UK economy by £47 billion annually, estimating that widespread AI adoption could increase national productivity by up to 1.5% each year.
Dr Fabian Stephany, Economist & Departmental Research Lecturer at the Oxford Internet Institute (OII), University of Oxford commented: “I am encouraged to see early evidence that AI is beginning to generate measurable productivity gains for UK firms. Since the introduction of generative AI, many observers have been asking when these gains would materialise, and the findings suggest that this moment may now be arriving. This is consistent with what research would predict: technological breakthroughs rarely translate immediately into productivity improvements, as organisations need time to adapt their workflows, governance structures and capabilities.“
From AI Promise to Productivity Reality
The data suggests that while belief in AI’s potential is strong, execution at scale is proving more complex. Findings indicate that the primary obstacles to AI-led productivity are not technological. Instead, organisations point to structural and operational barriers as factors slowing the move from pilots to enterprise-wide transformation.
Top barriers include a lack of skilled workforce, poor data quality, organisational silos and unclear leadership or strategic direction. Technology itself ranks below many of these internal challenges, cited by just 19% of respondents. Responsibility for AI governance is also often fragmented across executive, technology, data and business leaders. While executive leadership typically holds responsibility for investment, there is no clear governance owner, limiting accountability and slowing decision-making.
This suggests that many UK organisations are pursuing AI at a measured pace, building progress through smaller, targeted use cases while strengthening internal structures, with broader productivity gains likely to follow as foundations mature.
In debates about national productivity, AI is positioned as a growth engine. For business leaders, these gains will manifest themselves at both their top and bottom line with cost reduction as a clear goal. Nearly half (44%) say that cost reduction matters most as a key measure of success, while 26% say the same of revenue growth.
The Executive Confidence Gap
The research also reveals cautious confidence on AI deployments among senior leaders. Only 24% of organisations say AI initiatives are identified and prioritised using a rigorous framework aligned to business objectives. Meanwhile, 40% expect AI to take two years or more to materially improve productivity.
Around 60% say ethics and safety concerns influence their decisions to adopt and scale AI. This reflects a responsible approach to deployment, particularly in regulated and risk-sensitive sectors.
Jennifer Belissent, Principal Data Strategist, Snowflake, said: “UK organisations clearly believe in AI’s long-term potential, and continued investment runs parallel to this belief. This research shows, however, that belief alone is not enough. Productivity gains require clear ownership, strong data foundations and alignment between AI initiatives and measurable business objectives. AI has the potential to be a real driver of UK productivity and economic growth. But unlocking that potential depends on getting the fundamentals right – governance, data and clear accountability.”
A Varied Industry Picture
The research highlights clear differences in AI maturity and confidence across key UK industries, although productivity gains remain uneven.
Financial services is more advanced on governance and strategic alignment, but regulatory and reputational concerns are slowing the move from structure to scale.
Manufacturing shows strong belief in AI’s long-term productivity potential, yet expects slower returns due to skills gaps and integration challenges.
Retail lags on confidence and delivery, with AI often confined to isolated use cases amid persistent data quality issues and fragmented ownership.
In the public sector, organisations are the most risk-aware and governance-led, but also anticipate longer timelines before productivity gains are realised.
53% cite safety & reliability of AI outputs as the top concern affecting confidence in AI.
Two thirds (66%) say ethics and safety significantly shape adoption decisions.
52% say AI will not materially improve productivity for at least two years.
While this cautious approach prioritises trust and accountability, it may mean productivity gains take longer to come to fruition.
Turning Point for UK Enterprise AI
Across industries, many are still realising how best to drive AI productivity at scale and the skills needed to make this a reality. While levels of governance maturity and risk appetite differ, the journey to broader productivity gains is shared.
Dr Stephany added: “The report’s finding that skills shortages are a key barrier to adoption strongly resonates with findings from my research group (SkillScale) at the Oxford Internet Institute, University of Oxford. AI systems are only as powerful as the people who develop, maintain, apply and govern them. In SkillScale’s research, we find that workers with AI-related skills command a wage premium of around 23% in the UK, have higher chances of finding a job, and are more likely to receive additional job benefits. These patterns reflect the strong and growing demand for talent capable of working with artificial intelligence. Expanding access to AI skills and training will therefore be critical if organisations want to sustain and scale these productivity gains and ensure that the benefits of AI are broadly shared.”
Jennifer Belissent concluded: “The research paints a clear picture. The foundations for AI success in the UK are in place. Organisations are investing, experimenting and strengthening governance frameworks. However, to close the gap between ambition and measurable productivity gains, businesses need stronger alignment, clearer ownership and more robust data foundations. If AI is to play the transformative role policymakers and business leaders expect, the focus must now shift from experimentation to disciplined execution.”
Methodology
The research was conducted by YouGov on behalf of Snowflake among 500 senior decision-makers from large UK organisations with 250 or more employees across manufacturing, financial services, retail, the public sector and other industries. Fieldwork was conducted in January 2026.
Snowflake is the platform for the AI era, making it easy for enterprises to innovate faster and get more value from data. More than 13,300 customers around the globe, including hundreds of the world’s largest companies, use Snowflake’s AI Data Cloud to build, use and share data, applications and AI. With Snowflake, data and AI are transformative for everyone.
Andrew Power, Head of UK&I at Tricentis, on why the right approach to AI can deliver the foundation for more resilient, predictable systems
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Artificial intelligence is reshaping software delivery in financial services. Code that once took teams weeks to develop can now be generated and deployed in a matter of hours. This isn’t just about faster delivery; it changes the fundamentals of how software is built and how it behaves in production.
Financial institutions have moved quickly to integrate AI across core systems, from customer operations to anti-money laundering (AML) and software development to capture efficiency and innovation gains. UK parliamentary evidence shows adoption is already widespread, with the majority of firms using AI, and more planning to follow.
But as adoption spreads and becomes more embedded within key systems, so does exposure. Risk is no longer confined to individual defects, but shaped by how quickly those defects can spread across interconnected environments.
AI has removed the limits on how quickly software can be created, but not on how confidently it can be trusted, and financial institutions can now generate and deploy code faster than they can safely validate it.
This creates a new paradox: AI is both accelerating the pace of software change and increasing the speed and scale at which failures can materialise.
Machine-Speed Failure
AI-driven development shortens the distance between change and consequence. Software updates can move through the pipeline from creation to production with significantly less friction. However, this also reduces the time available to identify, flag and contain any issues before they have an impact.
AI-driven software changes don’t just move fast, they scale fast. Unlike traditional failures, these are systemic risks. A single misstep in an AI-generated update can propagate unpredictably.
For financial services, this is especially significant when key systems are deeply interconnected, spanning complex layers of infrastructure, integrations, and third-party services. Even a minor defect can propagate quickly across systems, amplifying its impact.
What would once have been contained can now escalate, cascading across systems and causing wider disruption that affects customers, operations and, in some cases, market activity. In financial services, this is not just a technical issue but a business risk with direct implications for customer trust, regulatory compliance and financial stability. The challenge is no longer simply identifying defects but maintaining confidence in what is being deployed.
This risk is already being felt across the sector. Institutions are accelerating delivery to meet customer expectations and competitive pressures, but often without corresponding advances in validation. Tricentis’ research shows 68% of financial services organisations anticipate outages or serious incidents due to poor software quality.
Regulatory Pressure for AI is Increasing
The issue is also drawing attention from regulators. Earlier this year, the UK Treasury Committee warned that current approaches to AI in financial services are inadequate and could expose customers and the wider system to “serious harm”, highlighting the need for stronger guardrails, clearer accountability and more robust oversight to deploy it safely.
Traditional resilience frameworks were never designed for systems evolving in real time, and AI can no longer be treated as a marginal technology risk. It must become central to how organisations manage and assess resilience.
This marks a shift from software quality being an engineering concern to a board-level issue of operational resilience. If machine-speed change is the new operational hazard, then failure to address it becomes a strategic issue rather than a technical one. With that in mind, financial leaders must acknowledge AI’s dual role as both a driver of risk and a mechanism for preventing it.
AI as Both a Safeguard & Source of Risk
AI also offers the most effective and scalable way to manage the risks it introduces. Advanced AI-driven validation, continuous monitoring and risk-prioritised testing can identify issues earlier than any manual process, helping reduce the likelihood they reach production.
In effect, the same AI that accelerates software creation must now be applied to validation and governance – operating at the same speed and scale.
The same capabilities that facilitate rapid software production can be applied to validation and governance, continuously evaluating system behaviour, detecting anomalies and prioritising testing based on potential business impact, rather than volume. This allows organisations to move beyond rigid approaches and towards more adaptive, responsive quality models that more accurately reflect the way AI behaves.
Instead of relying on standard periodic testing cycles, systems can be validated on an ongoing basis. This enables earlier intervention before issues escalate.
AI can also help organisations better understand the complexity of their own systems. By analysing dependencies across applications and infrastructure, it becomes possible to identify which processes are most critical and where failures would have the greatest impact.
From Acceleration to Control
There is a clear mismatch in how financial organisations approach AI. While many are leveraging AI to accelerate development, far fewer are evolving their validation and governance to keep pace, and it’s in this gap that risk emerges.
This is the “confidence gap”, where organisations can create software faster than they can safely deploy it.
To address this imbalance, firms must treat software quality as a core component of their AI strategy. Development and validation must move forward together. Governance must adapt to continuous, AI-driven change. This requires a move from static testing and coverage metrics to continuous, risk-based validation, where software is assessed in real time based on potential business impact.
If AI is the engine driving software creation, validation must act as the braking system – built in, not bolted on at the end. At machine speed, gaps in control become points of failure. The aim is not to slow innovation, but to ensure it progresses in a way that is sustainable and safe. When validation keeps pace with development, firms can move quickly and competitively, whilst maintaining control over how risk is introduced and managed.
This is a change we are seeing across large enterprises adopting AI-driven quality approaches, where validation, monitoring and governance are increasingly orchestrated together rather than treated as separate processes.
Preventing the Next Outage
The financial sector has already seen how quickly failures can escalate in complex, interconnected environments. In March, an IT error at Lloyds Banking Group exposed the private financial information of nearly half a million customers, prompting the bank to issue £139,000 in compensation.
Such incidents aren’t isolated: over the last two years, more than 33 days of unplanned banking outages have been reported to Parliament, underlining the scale of the issue.
As AI increases the velocity of change, it also raises the stakes for getting it wrong. But the irony is that it also provides the tools needed to prevent these failures from happening in the first place. AI is both contributing to the risk of outages and becoming the most effective way to prevent them.
By applying AI to continuous validation, monitoring and risk detection, organisations can spot issues earlier, understand their potential impact and intervene before disruption occurs. This shifts the focus from reacting to outages to preventing them, and it’s where the paradox becomes constructive. AI doesn’t have to be a source of instability.
With the right approach, it can become the foundation for more resilient, predictable systems. Those that fail risk trading innovation for instability. In the AI era, speed without confidence is simply another form of risk.
By Dvir Hoffman, CEO at CommBox on why organisations that will lead in the next phase of digital transformation are those that treat AI not as a feature, but as a production capability
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Enterprise AI has entered a new phase. The experimentation cycle that defined the past few years, full of proofs of concept, innovation labs, and sandbox deployments, is giving way to a harder question: how do we operationalise AI at scale, safely and measurably?
Nowhere is this tension more visible than in customer service voice environments. Voice remains the most complex, emotionally nuanced and operationally demanding channel. It is also where AI has the potential to unlock some of the greatest value.
Recent research from McKinsey highlights that organisations embedding generative AI directly into customer operations are seeing productivity improvements of 30 to 45 percent when deployment is integrated into workflows rather than layered on top. The distinction is critical. AI succeeds not because it sounds intelligent, but because it is embedded into systems, governance and business metrics.
For technology leaders, the path from pilot to production is less about enthusiasm for AI and more about discipline in execution.
Why AI Voice Often Stalls Before Scale
The majority of AI initiatives do not fail because the technology underperforms. They stall because the surrounding enterprise architecture is not aligned.
In controlled pilots, AI voice agents can demonstrate impressive conversational capability. They answer FAQs, interpret intent and simulate human dialogue convincingly. But production environments are not defined by conversation quality alone. They are defined by operational depth.
When a customer calls a healthcare provider, an insurer or a retailer, the AI must do more than talk. It must: Authenticate identity securely. Retrieve and update records in real time. Execute transactions, escalate appropriately and comply with regulatory frameworks. Without direct integration into CRM systems, billing platforms, policy databases or electronic health records, AI remains superficial.
This is where many organisations hit friction. Production requires orchestration across telephony infrastructure, data platforms, compliance frameworks and human workflows.
Governance becomes another inflection point. Voice interactions carry legal and reputational weight, particularly in regulated sectors. Disclosure requirements, audit trails, escalation protocols and data protection controls cannot be retrofitted after deployment. The World Economic Forum’s 2024 work on responsible AI underscores that governance must be embedded into AI systems by design, particularly where customer trust and compliance are at stake.
When governance is treated as an afterthought, scaling slows dramatically.
There is also a measurement problem. Too many pilots are judged by narrow metrics such as intent recognition accuracy or conversation duration. Production environments are judged by business impact: containment rates, reduction in average handling time, cost-to-serve, regulatory adherence and customer satisfaction. If AI is not connected to those outcomes from the outset, executive momentum fades.
The shift from pilot to production requires organisations to think less about model performance and more about operational alignment.
Automation Without Eroding Trust
A common concern among executives is whether AI voice will erode customer trust. The answer depends entirely on how it is deployed.
Voice remains deeply human. Customers call when they want clarity, reassurance or resolution. In emotionally charged situations, such as reporting an accident, disputing a claim, or querying medical results, the experience must feel competent and controlled.
The most effective AI voice deployments do not attempt to automate everything. They focus on high-volume, structured interactions where resolution paths are clear and compliance rules can be embedded confidently. Appointment scheduling, policy updates, payment processing and order tracking are examples where end-to-end automation can meaningfully reduce friction.
In live enterprise environments, we are seeing organisations safely automate a significant proportion of inbound calls when the AI agent has direct access to real-time data and defined escalation thresholds. Customers benefit from immediate resolution, while human agents are freed to handle complex and emotionally sensitive cases.
Equally important is what happens when escalation occurs. AI should not disappear at the point of handoff. Instead, it should transfer context, summarise the conversation and provide agents with relevant data and next-best-action prompts. This augmentation model aligns with Gartner’s 2024 analysis of customer service technology trends, which emphasises that the greatest gains come from combining automation with agent enablement rather than pursuing full replacement.
Trust is reinforced when customers feel they are being served efficiently and responsibly. That requires transparency about when AI is involved, clear pathways to human support and systems that operate within strict compliance guardrails.
In regulated industries, explainability is no longer optional. Enterprises must be able to demonstrate how decisions were made, how data was handled and how customers can escalate concerns. When these safeguards are engineered into the platform, AI voice becomes a tool for strengthening trust rather than compromising it.
Where Enterprise Leaders Should Focus
As AI investment accelerates, CIOs and CDOs face pressure to deliver measurable value while maintaining governance standards. The lesson from organisations successfully scaling AI voice is that integration and oversight matter more than experimentation.
AI should be treated as infrastructure. That means prioritising deep integration with core enterprise systems from the outset. An AI voice agent that cannot execute transactions securely or access accurate, real-time data will struggle to deliver meaningful business outcomes.
Governance must also be operational, not theoretical. Clear escalation pathways, role-based permissions, audit capabilities and sector-specific compliance frameworks need to be embedded within the technology layer. When risk management is part of the architecture, deployment accelerates rather than slows.
Finally, measurement must be tied directly to business performance. Containment rates, resolution times, operational cost reductions and customer satisfaction metrics should be defined before rollout. McKinsey’s 2024 research reinforces that organisations capturing the most value from generative AI are those embedding it deeply into workflows with explicit performance targets.¹ AI that operates alongside business KPIs, rather than parallel to them, is far more likely to achieve sustained executive backing.
The broader transformation taking place across enterprise technology is not about AI replacing human capability. It is about rearchitecting customer engagement so that automation, data and people operate in synchrony.
Meeting AI Voice Demands
Voice remains one of the most demanding channels to modernise precisely because it sits at the intersection of emotion, compliance and operational complexity. Yet that is also why it offers such strategic value. When AI voice is integrated into core systems, governed rigorously and measured against real business outcomes, it moves from being an innovation experiment to becoming a structural advantage.
The organisations that will lead in this next phase of digital transformation are those that treat AI not as a feature, but as a production capability. Moving from pilot to production is not simply a technical milestone. It is a cultural one, signalling that AI is no longer an experiment on the edge of the enterprise, but a trusted component at its core.
Lee Nolan, GM UK&I at Hitachi Vantara, on why AI will not be defined by the sophistication of the models being deployed but the strength, consistency and reliability of the data that sits behind them
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Spend five minutes in any boardroom and AI will come up. Strategies are being signed off, budgets are being released and pilots are already underway, giving the impression that momentum is building at pace. Yet beneath that surface is a more uncomfortable reality, one that is becoming harder to ignore as organisations move beyond experimentation and into delivery.
Most organisations are trying to build AI on foundations that were never designed for it. The ambition is clear and well-funded, but the underlying data infrastructure has not kept up. That gap between intent and readiness is now becoming visible, particularly as organisations look to scale beyond isolated use cases and deliver outcomes that are consistent and commercially meaningful.
Recent research into UK businesses reinforces this point. While adoption continues to move forward, only a small number of organisations are genuinely set up to support AI at scale. The issue is not tools or talent, but the condition of the data at the core of the business.
AI Exposes What Businesses Would Rather Ignore
AI is often described as a layer that can be applied to existing systems to unlock value. In practice it does the opposite. It brings complexity into sharp focus, exposing inconsistencies and inefficiencies that may have been tolerated for years.
That is why data quality has moved to the centre of the conversation. Around 67% of UK organisations now cite it as the primary driver of AI success. The shift reflects a growing awareness that no level of investment in AI can compensate for weak or unreliable inputs.
For many organisations, data has evolved without a consistent approach to governance. In fact, Gartner estimates that 80% of organisations attempting to scale digital initiatives will fail due to weaknesses in data and analytics governance. Systems have been added over time, ownership is unclear and definitions vary. The result is a fragmented environment where the same metric can mean different things across the business. When AI is introduced, it does not resolve those inconsistencies, it amplifies them.
The Hype Cycle is Giving Way to Reality
Over the past year, there has been a shift in how organisations approach AI. The early phase was driven by urgency, with businesses keen to move quickly and demonstrate progress. That momentum remains, but it is now being balanced by a more realistic perspective.
There is a greater focus on outcomes, with more scrutiny on how AI is delivering value. Many organisations have realised that quick wins are harder to achieve when the underlying data is not fit for purpose, and that scaling AI requires a level of operational discipline that cannot be bypassed.
What was initially framed as a technology challenge is now understood as a business challenge, spanning processes, ownership and governance as much as platforms and tools.
Confidence is High but Capability is Uneven
Confidence across organisations remains high, but it often does not reflect reality.
While many businesses consider their data infrastructure to be mature, progress is often uneven. Some teams may be working with well governed data, while others are still reliant on manual processes and disconnected systems. This creates a situation where parts of the business are ready to move forward, while others are not, a challenge reflected in wider industry research from McKinsey & Company, which highlights siloed data as one of the biggest barriers to scaling AI.
That inconsistency is where problems begin. AI depends on trust in the data If that trust is not consistent across the organisation, outputs become unreliable and adoption slows. From the outside, many organisations appear ready, but internally the foundations are still being stabilised.
Control vs Convenience
There is also a growing emphasis on control, particularly around where data is stored and how it is managed. Data sovereignty is now playing a central role in decision making, with around 85% of UK organisations saying it directly influences how they deploy AI.
This reflects a broader recognition that data is both a critical asset and a potential point of risk. As organisations become more reliant on it, they are also becoming more deliberate in how it is governed and protected.
At the same time, the expectation that everything should be built in-house is fading. Many organisations are turning to external partners to accelerate progress, while retaining control over their data and strategic direction. This balance allows them to move faster without losing oversight.
Vague AI Strategies
One of the most persistent challenges is the lack of clarity around what AI is meant to deliver. Too many initiatives begin with broad ambition and little definition of success, resulting in activity that is difficult to measure and even harder to scale. This is reflected in wider industry trends, with Gartner noting that only 53% of AI projects make it from prototype into production.
The organisations making progress are far more structured. They define clear objectives, establish measurable outcomes and maintain a focus on value throughout. In the UK, a growing majority are now putting formal KPIs in place for their AI initiatives.
This shifts AI from being an experiment to something that can be managed, evaluated and improved over time.
AI Will not Wait for Organisations to Catch Up
AI will continue to evolve at pace, and the pressure on organisations to keep up is unlikely to diminish. What is changing is the nature of the conversation. It is becoming less about what AI can do in theory, and more about what organisations are actually capable of delivering.
Most are still in the process of building the foundations required to support AI effectively. That is not a failure, but it does define the scale of the challenge ahead.
Because ultimately, AI will not be defined by the sophistication of the models being deployed. It will be defined by the strength, consistency and reliability of the data that sits behind them.
Mark Talbot, Director, CS AI Initiatives at Appian, reasons that as organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it
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Many organisations have long treated improvement as something that arrives as a top-down effort, not something built with the people doing the work. Specialists designed new processes, discussed them in formal forums, and introduced them through large change programmes that often felt detached from daily work. For most employees, ‘transformation’ meant being asked to follow new rules, rather than designing better ways of working.
AI is starting to reverse that pattern. Instead of concentrating control and decision rights in a small, central group, modern AI tools give more agency to the people closest to the work. They can see what is not working, imagine better approaches, and use AI to help redesign and improve the processes they rely on every day. This shift – which can be described as the democratisation of AI – changes who participates in improving the business. However, it is worth remembering that this shift only works at scale when AI is embedded within a platform that maintains governance, visibility and control.
Process Improvement in the Hands of Many
Until recently, fixing a broken process often meant filing tickets, waiting for a slot on an IT roadmap, or hoping that a specialist team would eventually address the issue. Creating applications, building automations or redesigning workflows were seen as highly technical tasks. For most employees, waste and inefficiency were things to work around, not things they had the tools or authority to change.
That obstacle is now deteriorating, as long as organizations don’t lose sight of the fact that governance remains essential, particularly in highly regulated environments
AI agents, generative AI and conversational interfaces allow people across the business to shape how work is structured. Within this model, someone in operations can describe an outcome in plain language and have an AI system propose and embed the steps within existing processes. Within a governed platform, non-technical users can adapt existing solutions and automate repetitive tasks without waiting months for central support. At the same time, process insights give developers visibility into what is being built, enabling them to refine, standardise and scale applications more quickly across the organisation.
Data is opening up as well. Data fabrics and related architectures connect scattered information sources into governed layers that a wider audience can access safely. Instead of waiting on static reports, people can access relevant, trusted data when they need it, and use AI to interpret and apply it to their decisions.
When process insight and data access reach this level, best practices move beyond documentation or occasional training. Tools and workflows embed them into daily work, improving performance across the organisation.
Scaling Improvement Across the Organisation
As more individuals understand how their work connects to broader outcomes, organisations unlock a powerful driver of change. Process improvement no longer depends only on a small group of specialists. Employees can recognise when processes are inefficient or risky and have the means to address them at scale, inside an AI platform.
By encoding domain knowledge into AI assistants and digital coworkers within an enterprise-grade AI platform, organisations can share expertise across roles and levels. These AI-powered helpers do not replace professional judgment. They strengthen it. They surface options, highlight inconsistencies and provide context, while humans make the final decision. Over time, each interaction becomes both a learning moment and a new piece of institutional knowledge that organisations can capture and reuse.
In this model, process improvement is no longer episodic or confined to formal transformation projects. It becomes part of everyday work, inside a platform with AI tools that provide real-time feedback and recommendations.
AI, Noise Reduction, and Better Oversight
This shift raises a key question: if AI platforms make analysis, decision support, and process design more accessible, what happens to deep expertise?
There is a concern that easy access to AI advice might weaken people’s understanding. If answers are always a prompt away, will teams still develop the knowledge that comes from working through complexity? If people follow AI suggestions without grasping the logic, how meaningful can human oversight really be?
Over-reliance on instant guidance can create only surface-level competence. People may treat AI outputs as instructions rather than as inputs to their own reasoning.
On the other hand, used well, AI can create more room for expertise, not less.
By handling repetitive tasks and routine decisions, AI reduces the volume of low-value work that consumes people’s time. Teams can then focus on exceptions and refine how they make decisions. Instead of dealing with every routine request themselves, they can focus on work where context and experience matter most.
When AI removes more of the routine burden, teams have more capacity to focus on judgement, process design and oversight. That helps build expertise while keeping improvement connected to the wider goals and governance of the business.
Shaping AI, Not Just Living With It
As organisations grow more capable with AI, the challenge shifts from proving its value to expanding access to it. AI is moving from something that happens to the workforce to AI being something that is built and refined with the workforce.
Organisations should treat people as partners in shaping AI, rather than as operators of automated systems. When AI platforms can be combined with process visibility and human judgement, employees can have an outsized effect on the systems around them. They can influence how work is structured and how decisions are made. In that sense, AI redistributes who participates in designing better ways of working, and creates an opportunity to anchor that shift in thoughtful design and human expertise.
Vincent Guillevic, Director of Fraud Labs at Entrust, argues companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers
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Identity verification and tackling fraud began as a face-to-face process, built on human trust. Opening a bank account involved meeting a banker in person and from there, trust was established because both parties could see and interact with each other directly in branch.
Fast forward to the digital age and a lot of services have moved online. Identity verification has therefore shifted from in-person checks to remote identity verification. Today, we’re in an era where identity is now central to every interaction we have online.
Fraud has followed the same trajectory. Much like a burglar would test every possible entry point rather than just the front door, fraudsters probe every stage of the customer journey. They look for weaknesses at onboarding, during login, and throughout ongoing transactions and data requests.
That challenge has intensified in recent years. AI has given fraudsters faster, sophisticated and scalable tools. Deepfakes can bypass checks, AI‑generated documents can appear real, and phishing and impersonation attacks can now be automated at scale.
Once a fraudster gains access to a legitimate account, the damage escalates quickly. Global losses from account takeover (ATO) fraud were projected to reach $17 billion in 2025, up from $13 billion in 2024. While the underlying intent of fraudsters seeking the weakest point of entry, the breadth, speed and sophistication of modern attacks have.
Identity Fraud Patterns Across the Customer Lifecycle
Fraud can occur at any stage of the customer journey. From verifying identity at onboarding to securing connections and fighting fraud in everyday transactions. Each stage introduces its own risks, and attackers adapt their tactics based on where value can be extracted most efficiently.
In 2025, patterns showed a clear distinction between industries targeted for new account fraud and those targeted for account takeover fraud. Businesses that offer immediate incentives such as promotional offers or sign-up bonuses are primarily targeted for new account fraud. In contrast, businesses where accounts accumulate long-term financial or data value face higher levels of ATO.
Industries built around sign-up incentives or instance access experience most fraud at onboarding. For instance, in crypto, 67% of fraud attempts occur during account creation, largely driven by sign-up incentives. Vehicle rental follows a similar pattern, with 67% of fraud taking place at onboarding as attackers use fake identities to gain short-term access to high-value assets. In these sectors, low-friction onboarding creates opportunities to harvest incentives or establish accounts that later become avenues for future money laundering.
Account takeover fraud reflects a different strategy. Rather than creating fake accounts, attackers focus on compromising established accounts using tactics such as stolen credentials, phishing, malware, or social engineering. Entrust data shows this is most common in industries where accounts hold enduring value. In payments, 82% of fraud attempts occur after onboarding, while in professional services the figure is 62%. High-value, long-standing accounts are attractive because they enable fund transfers, loans, and access to identity-rich data, making them more valuable than newly created accounts.
These patterns highlight two critical realities. First, organisations can no longer optimise for one type of risk at the expense of another. Defending a single point in the journey inevitably leaves gaps elsewhere. Second, fraud has become highly professionalised. Modern fraud operations are organised, strategic, and adaptive, moving toward the highest rewards and the weakest controls.
Prevention Must Span the Entire Journey
If fraud can occur at any stage, prevention must operate at every stage. Organisations that implement robust, lifecycle-wide identity strategies save an average of $8 million per year in fraud-related costs. These savings come from detecting threats earlier, more accurately, and beyond a single checkpoint.
There are three areas where that lifecycle approach needs to be strongest.
Get onboarding right
Onboarding is the first opportunity to establish genuine trust. Strong Know Your Customer (KYC) or Know Your Employee (KYE) processes combine document verification with biometric checks such as face recognition or fingerprint scanning to confirm that the person applying is who they claim to be. Liveness detection adds a further layer by distinguishing real users from synthetic identities and deepfakes, which are linked to approximately one in five biometric fraud attempts.
With strong identity verification at onboarding not only reduces immediate fraud, but also limits the downstream damage caused with fraudulent accounts.
Secure existing accounts with continuous authentication
Verifying identity once is no longer sufficient. Continuous authentication, combining multi-factor authentication with biometric re-verification like facial recognition, allows businesses to protect established accounts without creating unnecessary friction for legitimate users.
Crucially, it enables authentication requirements to adapt dynamically as risk levels change, rather than applying the same static check regardless of context. In payments businesses, where most fraud targets the authentication process itself, this adaptability is key to mitigating attacks before losses occur.
Monitor behaviour in real time, not just identity
Device intelligence and behavioural signals make it possible to assess risk based on how users interact with services, flagging unusual login patterns, device anomalies, or out-of-character transactions.
As AI-driven fraud becomes more sophisticated and convincing, behavioural indicators provide another layer of ongoing fraud detection. Focusing monitoring on high-risk actions, rather than only high-risk identities closes a critical gap in traditional defences.
The Window of Opportunity
Fraud has always followed the customer journey. What has changed is the availability of advanced technology capable of tracking, analysing, and responding to threats at every stage. The key question for organisations is whether these capabilities are deployed as a connected strategy or left as isolated controls with gaps in between.
Companies that treat identity as a continuous thread rather than a single checkpoint will be better positioned to reduce losses and protect customers, and preserve the trust that underpins long-term digital relationships.
Andrew McLernon, CEO and co-founder at Interlink, on why culture is the real disruptor
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For much of modern business history, disruption has been framed as something external. An emerging technology or a competitor rewriting the rules. Often, markets shift faster than organisations can respond and leaders are told to move quicker, work harder and implement more systems to keep up.
Today, AI has become the latest catalyst for this narrative, with every week seeming to bring another promise of productivity gains or automation breakthroughs. Yet as AI accelerates, many organisations are responding in surprisingly familiar ways: longer hours, stricter oversight, everyone back to the office mandates and layers of new processes built on outdated foundations.
In my experience, this is the wrong response. The real disruption of the AI era isn’t technological. It’s cultural. And leaders who fail to recognise that, risk solving tomorrow’s challenges with yesterday’s assumptions.
The Illusion of Productivity
When economic pressure rises, organisations often default to visibility as a proxy for performance. Leaders want to see people working, whether that means more time in the office, more meetings or more activity. But activity isn’t the same as effectiveness.
AI is already capable of performing many routine tasks faster than humans, a fact that should lead us to rethink how work is structured. Instead, many businesses are doubling down on models that were designed for a different era, treating time spent on tasks as the primary measure of contribution, rather than outcomes achieved.
The irony is that this approach undermines the very productivity gains leaders say they want. People become busier but not necessarily more effective. Creativity declines, decision-making slows and, ultimately, innovation suffers because teams are exhausted rather than energised.
True productivity in an AI-enabled world comes from clarity and focus, not from squeezing more hours out of people.
Culture Before Performance
At Interlink, we’ve learned that performance rarely improves by targeting performance alone. It improves when culture enables people to do their best work.
Culture isn’t slogans or perks; it’s the operating system behind every decision. It determines whether people feel trusted or controlled, whether ideas are encouraged or suppressed and whether change is embraced or resisted.
As we scaled a profitable, AI-powered business across multiple continents, we discovered that culture has to scale before performance can. If it doesn’t, growth amplifies dysfunction. That realisation changed how we approached leadership. Instead of asking, “How do we get more output?” we began asking, “What conditions allow people to produce their best work consistently?”
The answers were not technological; they were human.
Redesigning Work Rather Than Reinforcing Old Models
One of the biggest leadership mistakes I see today is adding complexity to existing systems instead of redesigning them. Organisations introduce new tools without changing behaviours. They add layers of management without simplifying decision-making. They enforce policies intended to restore control rather than building trust.
For us, introducing a four-day working week was not about doing less; it was about focusing on what truly matters. Compressing time sharpened our priorities, improved decision-making and encouraged greater ownership of outcomes by everyone across the business. The result was counterintuitive for some observers: productivity rose, retention strengthened and creative thinking accelerated. When time had clearer boundaries, focus sharpened and accountability deepened.
Flexible and hybrid working emerged from the same philosophy. Instead of designing work around physical presence, we designed it around contribution and trust replaced oversight as the foundation of accountability.
These changes weren’t always comfortable and they absolutely required leaders to relinquish some traditional forms of control. But they reinforced a principle that has become increasingly clear: autonomy drives engagement and engagement drives performance.
The Tension Between ‘Back to the Office’ and the Future of Work
The current push for universal office returns reflects a deeper anxiety about how work is evolving. For some leaders, visibility feels like certainty. If people are physically present, it feels easier to manage performance. But this perspective risks confusing familiarity with effectiveness.
The future of work is unlikely to be defined by a single model. People’s roles, responsibilities and life circumstances vary too widely for one-size-fits-all solutions. Organisations that impose rigid structures in pursuit of control may find themselves losing talented individuals who value flexibility and trust.
That doesn’t mean offices are irrelevant. Physical spaces remain powerful for collaboration, learning and connection. The challenge is not choosing between remote or office-based work but designing environments that genuinely enhance productivity rather than simply recreating old habits.
The businesses that succeed will be those that treat flexibility as a strategic tool rather than a concession.
AI as an Amplifier of Leadership, not a Replacement
Because our business operates in AI-powered demand generation, we spend a great deal of time thinking about the relationship between automation and human expertise. AI excels at pattern recognition, scale and speed but what it lacks is context, empathy and strategic judgement.
The danger for leaders is assuming that technology alone can drive transformation. AI amplifies whatever culture already exists. In organisations built on trust and curiosity, it accelerates innovation; in environments dominated by fear or rigidity, it often automates inefficiency.
The competitive advantage lies not in whether a company uses AI (most soon will) but in how leaders integrate it into a culture that values learning and experimentation.
Simplicity as a Leadership Discipline
Another lesson from scaling is that complexity grows naturally. As businesses expand, processes multiply; communication becomes fragmented, and decision-making slows because too many layers intervene between ideas and action.
We’ve learned to treat simplicity as a leadership discipline. That means regularly rebuilding systems that no longer serve us, even when they once worked well. It also means resisting the temptation to add new structures simply because growth makes things feel messy. As well, simplicity requires intentional effort. Leaders must continually ask which processes genuinely add value and which exist only because they always have.
Leadership for an Uncertain Future
Perhaps the most important shift leaders must make is moving from control to clarity. In a world where technology evolves faster than organisational structures, certainty is increasingly rare. What teams need is not rigid instruction but clear purpose, shared values and the autonomy to adapt.
Leadership becomes less about directing tasks and more about shaping environments where people can thrive. That includes prioritising wellbeing not as a perk but as a strategic requirement. Burnout may produce short-term output, but it erodes long-term capability and the organisations that will define the next era of business are unlikely to be those that simply adopt the latest technology fastest. They will be the ones that rethink how work itself is designed, aligning technology with human potential rather than attempting to replace it.
Culture as the Ultimate Competitive Advantage
As AI becomes ubiquitous, technological differentiation will narrow. Tools that once seemed revolutionary will become standard. But what will remain distinctive is culture.
Culture determines how quickly teams learn, how openly they challenge assumptions and how resilient they are during uncertainty. It shapes whether innovation is encouraged or quietly resisted. And, in that sense, culture is not a soft concept; it is a strategic asset.
The real disruption of the AI age is not automation, it’s the opportunity to redesign leadership around trust, simplicity and human potential. Leaders who embrace that shift will find that technology accelerates their progress. Those who cling to outdated models may discover that even the most advanced tools cannot compensate for disengaged people.
Disruption isn’t about changing the industry first; it’s about changing how we lead.
Simon Pamplin, CTO of Certes, warns of the risks of supplier breach and how that can affect you
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Many organisations still assume that once their data is handed over to a cloud provider or managed service partner, the risk goes with it. That assumption is not only wrong, it’s also dangerous. Outsourcing IT services does not mean outsourcing accountability. When sensitive information leaves your environment without strong protection, you are effectively placing your reputation, regulatory standing and customer trust in someone else’s hands. When those controls fail, as they often do, it is the data owner who ultimately pays the price.
Regulators have become increasingly clear on this point. Responsibility for protecting data sits squarely with the organisation that owns it, not the supplier processing or moving it on their behalf. Contracts, assurances and compliance statements offer little comfort once data has been exposed.
As a result, supply chain security is no longer an operational detail to be left to technical teams. It is a board level issue that affects risk, compliance, reputation and long term resilience. Senior leaders are now expected to understand where their data travels, who has access to it and how it is protected at every step.
The reality is uncomfortable but unavoidable. Risk cannot be outsourced. Services, platforms and operations can be delegated, but accountability remains firmly with the data owner. The only way to break the link between supplier failure and organisational damage is to ensure that data stays protected wherever it goes.
Why third-party breaches hurt so much
Some of the most damaging recent breaches did not begin inside the organisations that ultimately suffered the consequences. Attackers found their way in through suppliers, shared platforms or service providers that sat outside direct control. Once inside, they were able to access and extract data that belonged to someone else entirely.
Despite this, it was the data owner that faced regulatory investigation, fines, legal action and lasting reputational damage. Customers didn’t blame the supplier; they didn’t even know it existed. They blamed the organisation they trusted with their information. Boards and executives are then left explaining why sensitive data was allowed to travel unprotected through third-party environments.
The false comfort of perimeter security
A common thread in many of these incidents is over-reliance on perimeter based security. Organisations focus heavily on protecting their own networks and identities, while assuming partners will do the same. In reality, attackers rarely respect organisational boundaries. They move through supply chains, exploit weaker links and target data wherever it is most accessible.
Once data leaves your environment, perimeter controls lose their value. If the information itself is not protected, a breach at any point in the chain exposes it. This is why traditional security approaches struggle to contain the fallout from supplier compromises.
Harvest now, decrypt later is already happening
There is an additional risk that many organisations are massively underestimating. Attackers are not only stealing data for immediate use. They are also running harvest now, decrypt later campaigns. Sensitive information is being exfiltrated today, stored, and held until cryptographic advances make it readable.
This is significant because data shared across supply chains retains its importance and value over time. Financial records, personal data, intellectual property and regulated information do not expire quickly. When quantum computing capabilities mature, encryption methods that were once considered strong will no longer offer adequate protection. Data stolen years earlier can suddenly become exposed.
The assumption that quantum threats are a distant concern misses the point. The risk is not when quantum computing arrives. The risk is that the data that will be valuable then is already being collected now. Without quantum-ready, Post-Quantum Cryptography (PQC)-safe security protection in place today, organisations are building a future liability into their supply chains.
Organisations need to be looking at these PQC-safe solutions now that focus on ensuring data remains protected even against future cryptographic breakthroughs. When applied to data in motion, it ensures that information remains unreadable wherever it travels, across internal systems, cloud platforms and third-party environments.
Securing data across the supply chain
The most effective way to reduce supplier risk is to protect the data itself, rather than relying on each partner’s infrastructure. Encryption in transit, strong control of encryption keys and clear policies governing how data flows between systems are critical.
When data is protected end-to-end, a supplier breach does not automatically become a business crisis. Even if attackers gain access to systems, the information they intercept is unusable. This removes much of the incentive for the attack and dramatically reduces the impact if one occurs.
Crucially, this approach works with existing systems. Many organisations rely on legacy platforms that are difficult or costly to replace. Protecting data flows around those systems allows them to remain in use while still meeting modern security and regulatory expectations.
Another benefit of data-centric protection is reduced dependence on supplier assurances. Rather than relying on the assumption that every partner has implemented perfect security, organisations can enforce their own protection standards at the data level. This shifts control back to the data owner and reduces exposure to weaknesses outside their direct oversight.
It also simplifies compliance. When organisations can demonstrate that sensitive data is consistently protected wherever it moves, regulatory conversations become far more straightforward.
Protecting what actually matters
The lesson from repeated third-party breaches is clear. Attackers go where the data is, not where the organisational chart says responsibility should lie. Organisations that focus solely on infrastructure security will continue to be caught out by supplier failures.
Those who take a data focused, quantum-secure approach can change the outcome. Breaches may still occur, but their impact need not define the organisation. When stolen data is unreadable, reputation, trust and regulatory standing are far easier to protect.
The message is simple. You may rely on suppliers, but your data is still your responsibility. Protect it accordingly.
Todd Moore, Global Vice President, Data Security Products at Thales, on why making AI security a boardroom priority today, will help firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation
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Financial Services organisations are responsible for some of the biggest growth in the global economy. Equally, they’re some of the most vulnerable. Like many other sectors, they’re racing to embrace AI, but with adoption comes new security risks.
According to Thales’ Data Threat Report: Financial Services Edition 81% of FinServ organisations are now investing in GenAI-specific security tools, with nearly a quarter using newly allocated budget. This surge in funding marks a turning point: AI security has moved from being an IT concern to a boardroom priority.
The fact that new budget lines are being carved out specifically for AI security signals a fundamental shift in corporate strategy. Boards increasingly recognise that protecting AI systems is as critical as safeguarding payment rails or core banking infrastructure. For an industry built on trust, resilience, and regulatory compliance, this investment wave shows how central AI has become to both risk management and competitive growth.
Balancing AI Innovation and Security
While FinServ organisations are aware of the security risks AI poses, they’re also seizing upon the opportunities it presents. The report has found that in 2024, FinServ businesses outpaced the broader market in AI deployment, leading in enabling employees to use AI and ahead in AI integration, which has continued into 2025. Additionally, 45% say they’re in the ‘integration’ or ‘transformation’ phases of their GenAI journey, compared to just 33% across wider industries.
AI’s ability to accelerate services, automate processes, and analyse data at scale makes it an exciting prospect, especially in the financial sector. This makes securing AI systems a priority for FinServ organisations, with increased GenAI integration reflecting developing organisational maturity and progress beyond experimentation.
The Risk
Yet the scale of opportunity is matched by the scale of challenge. AI systems require vast amounts of structured and unstructured data to conduct analysis and make recommendations.
For FinServ organisations, this often includes highly sensitive customer and transactional information, proprietary algorithms, and records bound by strict regulatory oversight. The risk is not only about whether AI systems themselves are secure, but whether the data they’re working from is accurate, as well as whether their adoption inadvertently creates new routes to data exposure and exfiltration.
Businesses need a clear strategy to fully understand how AI models are operating within their IT infrastructure, the applications they’re interacting with, and the data they’re accessing and pulling from.
The Response
Balancing AI’s opportunity and risk means embedding security at every stage, from design to deployment and ongoing monitoring. Newly allocated budgets for AI security, with nearly a quarter of FinServ firms making such investments, show how central AI has become to board-level strategy. These investments move firms beyond reactive fixes to proactive frameworks that evolve with the technology. AI security is no longer just an IT concern, it’s a strategic priority requiring collaboration between security, compliance, and business leaders. By factoring risk into early planning, organisations can align innovation with responsibility and build resilience for the long term.
Pioneering AI Security
Building on investment in AI-specific security is only the beginning. As scrutiny intensifies, the firms that will lead are those that treat AI security as integral to business strategy, not a bolt-on layer. Success will require visibility into how models behave, continuous validation against emerging risks, and adaptive controls that evolve with the threat landscape.
The financial services organisations that embed these safeguards into their core infrastructure will protect sensitive data as well as setting a benchmark for resilience and trust in an AI-driven economy. By making AI security a boardroom priority today, these firms position themselves to capture competitive advantage, safeguard customer confidence, and define the future of secure innovation.
Thales: AI is the New Insider Threat
Thales 2026 Data Threat Report Finds 70% of Organisations Rank AI as Top Data Security Risk
Data security has taken centre stage as the success of enterprise AI initiatives increasingly hinges on consistent, controlled access to proprietary organisational data sources. The 2026 Thales Data Threat Reportexamines the complex calculus that organizations must undertake to enable innovation while securing their most valuable asset – their data.
This research was based on a global survey of 3,120 respondents fielded via web survey with targeted populations for each country, aimed at professionals in security and IT management.
Jamil Jiva, Global Head of Asset Management at Linedata, on why the next chapter of AI-driven finance will be shaped not just by technology, but by creativity
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Beyond Data: Where AI Finds Unexpected Inspiration
The discussion about training AI largely focuses on concerns that accessible, human-generated data is limited and may soon run out completely. If this is the case, how can technology that depends on a seemingly endless stream of inputs to iterate, test, and adapt deliver the results we expect? AI relies on structured, high-quality data to thrive, but what happens when we run out of spreadsheets and financial models to train AI? We need new data sources to ensure it continues to learn, adapt, and deliver accurate insights. Video games stand out as offering some of the richest, most expansive, and complex environments for AI training.
At first glance, video games and financial operations seem to belong to entirely separate worlds. However, AI connects these domains, with models leveraging virtual-world training to tackle real-world financial tasks. Financial documents such as credit agreements and tax returns are often convoluted, unstructured, and labour-intensive to process. Therefore, AI designed to interpret such data must possess strategic reasoning, real-time adaptability, and advanced pattern recognition. So, could video games be the ideal training ground?
Contrary to popular belief, gameplay can significantly improve how people think, learn, and solve problems. The abilities required to excel at video games closely reflect the skills AI systems must acquire today.
Levelling Up: What Virtual Worlds Teach Machines
Practice leads to proficiency, a principle that applies to both humans and AI. Interestingly, many of the most significant advances in AI development have emerged not from conventional data training, but from taking creative approaches. Games push AI to emulate human thinking and sharpen its statistical intuition.
These game-trained models are neither expensive nor heavily reliant on resources, and they sidestep the issue of data scarcity. As a result, they are actively shaping the future of financial intelligence. The examples below offer a clear demonstration of the potential of gameplay.
Virtual Economies: Lessons from World of Warcraft
World of Warcraft, with millions of players interacting in an immersive and dynamic world, features an economy that closely mirrors real-world financial systems, complete with inflation, supply and demand cycles, and fraud risks. The game even inspired one of the most renowned epidemiological studies: when the in-game ‘Corrupted Blood’ plague spread unpredictably, scientists used it as a model for real-world pandemic simulations.
Financial models depend on vast, interconnected data networks, much like the economy in World of Warcraft. Organisations employ AI to continuously monitor patterns, detect anomalies such as fraud or misstatements, and optimise data extraction for financial reporting, mirroring the way AI analyses virtual economies.
Urban Chaos: GTA V and Real-World Simulation
While Grand Theft Auto (GTA) V is famous for its open-world chaos, researchers have leveraged its traffic systems and non-player character behaviours to train AI for applications such as self-driving cars, crime pattern recognition, and urban planning. At its heart, GTA provides a platform for AI to process vast amounts of unstructured data in real time.
Similarly, financial institutions manage millions of data points from a wide range of sources. Their AI tools must automatically extract insights, classify information, and normalise complex formats. GTA serves as a controlled yet intricate environment for simulating scenarios, enabling AI to optimise for real-world tasks through ongoing feedback loops.
Sandbox Creativity: Minecraft and Adaptive Thinking
Minecraft provides a sandbox environment where AI learns through exploration. OpenAI even trained an AI to play Minecraft by watching YouTube tutorials, closely mimicking the way humans learn. Similarly, any AI used by financial institutions must be able to self-learn from new document types and structures, adapting just as a Minecraft AI learns to survive.
Reinforcement learning, where AI improves based on feedback, is a key element of intelligent document processing. Thanks to its vast scalability and dynamic, hierarchical environments, Minecraft serves as an ideal setting for navigation and repeated feedback loops, helping models develop domain-flexible reasoning.
Multiplayer Mayhem: Dota 2 and the Art of Teamwork
Dota 2 stands out as one of the most complex competitive games ever created, presenting AI with challenges in real-time decision-making, strategic coordination, and adaptability. OpenAI Five, trained on the equivalent of 45,000 years of gameplay within just 10 months, managed to defeat renowned, professional human teams. As anyone who has mastered StarCraft knows, tactical adaptability is essential for gaining the upper hand.
Financial institutions operate in environments that are just as dynamic as the shifting levels of a video game. Market conditions, regulations, and data formats are in constant flux. AI must be able to adjust to new document structures, handle missing information, and navigate edge cases, much like AlphaStar adapts to an opponent’s unpredictable strategies.
From Pixels to Profits: Bringing Game Logic to Finance
Whether to streamline operations, mitigate risks, or make informed decisions in today’s data-intensive financial landscape, AI has the potential to fundamentally transform financial offerings, delivering personalised and evolving experiences that foster understanding and combine seamlessness with regulatory compliance.
Yet AI does not simply require more data from which to learn; it needs better data. Video games offer near limitless, pre-built, highly complex digital worlds where AI can test hypotheses, simulate scenarios, and refine decision-making models. By utilising these unique environments, AI is challenged to enhance its speed, accuracy, and efficiency.
The world of video games has many lessons we can learn when building AI, and given AI’s remarkable ability for transferable learning, it makes sense to leverage these pre-trained models to power essential financial workflows. It is more than just document processing; it is thinking, and the same intelligence that enables AI to defeat world champions in Dota 2 is now driving the next generation of financial AI solutions.
The next chapter of AI-driven finance will be shaped not just by technology, but by creativity. By embracing unconventional data sources such as the immersive complexity of video games, industry leaders will unlock new possibilities for personalisation, security, and customer engagement.
Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence
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The asset management industry is entering a structural inflexion point. The first wave of AI focused on improving productivity through copilots and automation. The next wave will fundamentally reshape how decisions are made, executed, and governed across the enterprise. This is not a technology upgrade. It is an operating model shift.
Despite significant investment, many firms remain trapped in fragmented AI experimentation. A majority are yet to realise meaningful economic returns from AI, not due to lack of capability, but due to a failure to redesign how intelligence is applied across the organisation. The gap between ambition and outcome is not a technology problem. It is a structural one.
From Automation to Decision Intelligence
The industry conversation has evolved. The question is no longer whether to adopt AI, but how to scale it across the enterprise. However, most firms are still approaching this challenge through the lens of automation, identifying tasks that can be executed faster or at lower cost. This delivers incremental value, but does not address the underlying constraint: the structure of decision-making within the organisation.
Traditional operating models are built around sequential workflows. Work moves from function to function: research, compliance, operations, and distribution, each dependent on the previous stage. This creates latency, duplication, and fragmentation. Agentic operating models shift the focus from tasks to decisions.
Instead of asking “Which processes can we automate?”, leading firms are asking: “Which decisions can be augmented or owned by intelligent systems?”
This shift enables organisations to move from sequential workflows to parallel decision systems; from human-led analysis to AI-assisted reasoning; from periodic insight to continuous intelligence. The result is not a marginal improvement. It is a step-change in how the enterprise operates.
The Pressures Driving Change
This transformation is not happening in a vacuum. Asset managers face mounting structural pressures: margin compression driven by fee pressure and passive competition; rising operational complexity from regulation and product proliferation; and advisor capacity constraints that limit scalable growth. Agentic operating models directly address all three.
By automating complex workflows, rather than individual tasks, firms can significantly increase advisor and analyst capacity without proportional cost increases. Parallel decision systems reduce the time required to launch products, respond to market events, and deliver client insights. This compresses cycles from months to days. Continuous monitoring of guidelines, portfolios, and operational processes reduces exposure to regulatory breaches and operational failures.
These are not theoretical benefits. They represent measurable improvements in cost-to-serve, time-to-market, and operational resilience.
Not all Intelligence is the Same
To scale AI effectively, organisations must recognise that not all problems require the same type of intelligence. Enterprise AI operates across three distinct layers, and conflating them is one of the primary reasons AI initiatives fail to scale.
Deterministic systems execute predefined rules with complete consistency. They are essential for functions where there is zero tolerance for error, trade validation, settlement processing, and regulatory reporting. If a business outcome must be identical every time, deterministic logic remains the correct approach.
Predictive systems use historical data to forecast outcomes. Applied in areas such as portfolio risk modelling, fraud detection, and client churn prediction, they generate probabilities and insights, but they do not interpret context or make decisions independently.
Agentic systems operate where problems require interpretation, judgment, and contextual understanding, investment guideline interpretation, regulatory document analysis, portfolio insights, and client communication. These systems can reason across complex information, generate insights, and take action within defined boundaries.
The ‘Different but Valid’ Dilemma
A critical challenge in adopting agentic systems is understanding how they behave. Traditional software produces identical outputs. Agentic systems produce reasoned outputs.
This introduces what I call the ‘different but valid’ dilemma. An agent may take a different reasoning path from a human and arrive at a different, but still correct, conclusion. This variability is not an error. It is inherent to reasoning systems.
The real risk lies in hallucination, outputs that are not grounded in data or evidence. Managing this requires organisations to clearly define where variability is acceptable. All AI-driven processes sit on a spectrum: deterministic actions with no variability (trade execution), predictive actions with controlled variability (risk scoring), and agentic actions with higher variability (investment insights).
Leading firms design systems where agents perform reasoning, deterministic systems enforce execution, and humans retain oversight on high-consequence decisions. This balance enables both flexibility and control.
The Operating Model Shift
The most significant change is not technological; it is organisational. Traditional models are built on functional workflows. Agentic models are built on coordinated decision systems.
Consider what launching a new investment product looks like under each model. In a traditional model, it involves sequential handoffs between teams, compliance reviews the guidelines, operations configures the systems, and distribution drafts the client narrative. Each stage waits for the last.
In an agentic model, intelligent systems operate in parallel: compliance agents interpret guidelines, operations agents configure constraints, distribution agents generate client narratives, and governance agents validate outputs. This orchestration compresses timelines, reduces friction, and enables continuous decision-making. It represents a fundamental redesign of how work is performed.
Governance: the Foundation for Trust
Trust is the prerequisite for scaling AI. Without it, adoption stalls, not because the technology fails, but because the organisation cannot adequately explain or defend the decisions it makes.
Leading firms implement governance models built on three principles. First, explainability: every decision must be traceable and auditable. Second, authority boundaries: agents operate within clearly defined limits. Third, human oversight: high-consequence decisions remain under human control.
Regulatory expectations will continue to evolve, but one principle remains constant: organisations must be able to explain how decisions are made.
Scaling AI is a Leadership Challenge
Executives must take a deliberate approach across four areas:
Define the intelligence model: map business problems to deterministic, predictive, or agentic systems.
Build the foundation: invest in data, infrastructure, and orchestration capabilities.
Redesign the operating model: shift from workflows to decision systems.
Implement governance to ensure transparency, control, and compliance.
Start with high-value use cases and expand rapidly across the enterprise. The firms that act now will establish a structural advantage in cost, speed, and decision quality. Those that do not risk being constrained by legacy operating models that cannot scale with the demands of modern markets.
The Question is not if, it is Who
The industry is not simply adopting new technology. It is redefining how decisions are made. The firms that succeed will not be those that deploy AI tools in isolation. They will be those who design the right form of intelligence for each problem, redesign their operating models around intelligent systems, and scale agentic capabilities across the enterprise.
This shift is already underway. The question is no longer whether it will happen. The question is which firms will lead, and which will be forced to follow.
Sanofi: Supporting the World’s Health Through Data
This month’s cover story spotlights Sanofi, one of the world’s largest pharmaceutical companies. For an organisation that puts the end-user – the patient – first, this requires an unwavering focus on R&D and continuous improvement. For the sake of the world’s health; every patient counts. So, when opportunities arose to improve services through data and advanced technology like AI, Sanofi brought in experts to steer and develop the journey.
Snehal Patel, Head of Global Data and AI Platform, takes a deep dive with Interface… “These innovations have fundamentally transformed Sanofi’s data and AI value chain,” says Patel. “It’s enabled scalable and efficient development across the organisation. We now have a far more agile development environment that supports the broader AI initiatives at Sanofi.”
Anson Cho, Director of Information Security & Data Protection at Langham Hospitality Group, discusses the pandemic’s silver lining and the development of a proprietary matrix to embed security into the heart of operational excellence.
“Our strategy wasn’t about over-engineering our systems to match the spend of a global financial institution; it was about increasing our defensive maturity so we are never an easy mark,” says Cho. “In cybersecurity, you want to ensure your barriers are sophisticated enough that attackers move on. We focus on staying ahead of the curve and continuously evolving so that our security posture remains a formidable deterrent.”
FNB: Redefining Data Science in Commercial Banking
Yudhvir Seetharam, Chief Analytics Officer at South Africa’s First National Bank (FNB) on a data science journey characterised by curiosity, culture and the drive for a competitive edge.
“Ours is a holistic approach focusing on the customer,” he explains. “Understanding the context of each customer journey and then using that context so that when we interact with you, we’re able to drive the right conversation with the right customer, at the right time, through the right channel and for the right reason. These ‘five rights’ make our interactions with clients more impactful than a spray and pray approach.”
Richard Ford, Chief Technology Officer at Integrity360, on why cybersecurity must move beyond control and embrace trust
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Cybersecurity has long been focused on building walls, but the biggest threat is already inside. Today, insider risk accounts for nearly half of all data breaches. This isn’t just about malicious actors, it’s about regular employees and trusted contractors who make simple, costly mistakes.
Remote and hybrid working has only intensified the problem. With teams distributed and work happening across cloud platforms and collaboration tools, it’s harder than ever to track what’s happening, let alone why. Although AI tools promise efficiency, they also introduce new vulnerabilities. Employees pasting code into chatbots or bypassing corporate tools to meet deadlines. All seemingly innocent, but highly risky.
Insider Risk
Ransomware gangs know this and are now skipping the technical breach altogether and going straight to the source – a company’s insiders. Whether through bribery or social engineering, attackers are finding that humans can be the weakest link in even the most well-defended environments. Despite this, most security budgets still focus outward.
Traditional tools like data loss prevention (DLP) struggle to keep up with today’s dynamic and unpredictable user behaviour. Meanwhile, simulated phishing tests and punitive training schemes often breed resentment, not resilience. It’s time to rethink the model.
Human Error, Human Fix
We need to stop treating employees as the problem and start making them part of the solution. Enter Human Risk Management (HRM), a behavioural approach to cybersecurity that recognises the complexity of modern work. HRM tools monitor real-world user behaviour, detect anomalies in context, and deliver just-in-time nudges to prevent risky actions before they happen. Instead of punishing mistakes, they help users avoid them in the first place.
Of course, technology alone won’t fix the issue, culture is key. Leadership must champion security as a shared responsibility, not an IT rulebook. Success should be measured by how quickly employees improve, not how often they slip up. Awareness campaigns need to be practical and rooted in real-world behaviour.
Organisations also need to understand how digital transformation has changed the risk landscape. Shadow IT is no longer a fringe issue, it’s how work gets done. Whether it’s a developer using an AI plugin or a marketer sharing files via a personal drive, employees will always find the fastest path to productivity. Security must meet them there, not block the way.
Cybersecurity Built on Trust
The smartest businesses are those that treat identity like infrastructure, and behaviour like a vital data stream. They invest in tools that adapt to people, not the other way around. This means a move away from a surveillance approach and embracing the nuance of human error and design systems that support.
In a world where threats are increasingly internal and AI is both a risk and a tool, cybersecurity can no longer be about control. It must be about trust, and that starts with understanding the humans behind the keyboards.
Dr. Yvonne Bernard, CTO at Hornetsecurity, on meeting the challenge of managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption
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The past year has been defined by acceleration. Threat actors rapidly embraced automation, AI, and social engineering. Scaling their tactics at unprecedented speed, while defenders raced to keep pace. Historically, defensive resilience evolves in step with attacker innovation, but in 2025 that balance began to falter.
In an analysis of over 6 billion monthly emails, Hornetsecurity’s Security Labs found that the volume of sophisticated threats grew faster than most security teams could adapt to. Malware-infected emails soared by 131%, scams increased by nearly 35%, and phishing attempts – powered by access to advanced AI – rose by 21% from the previous year.
Typically, attacks, even at volume, are easily filtered by good firewalls and secure email gateways. But the sophistication and AI-led nature of 2025’s boom made it even harder for organisations to defend themselves. The question now is: can security teams and businesses wrestle back control?
Evolving Cyberattack Landscape
AI enhances efficiency and precision. As such, cybercriminals use it to launch faster, more convincing and adaptive attacks, ranging from deepfakes to credential stuffing. As an example, there is a concerning trend of attackers increasingly using ‘MFA bypass kits’ to create deceptive login pages. These pages capture not only the user’s credentials but also have logic built in to handle MFA prompts as well. The unsuspecting user is then passed to the real login page for the target service and meanwhile the ‘kit’ grabs a copy of the user’s session token. This allows the attacker to impersonate the person and access their data.
Examples of such kits include Evilginx (open source) and the W3LL panel. Protecting against these attacks can be challenging, as they are adept at bypassing MFA safeguards. Threat actors often use compromised LinkedIn accounts, for example, to gain access to substantial information and connections. This enables them to impersonate trusted business connections. Paired with the weaponisation of Agentic AI, this will magnify existing vulnerabilities within an organisation, while introducing new ones that defy traditional containment models.
As it stands, the lack of oversight within organisations on the extent of AI’s adoption by cybercriminals has enabled the emergence of ‘Ransomware 3.0.’ Ransomware has evolved past simple encryption and exfiltration, with this next phase focusing on LLM-driven orchestration and a shift to data integrity manipulation.
To counter AI-accelerated compromises and ‘Ransomware 3.0’ in 2026, organisations must adopt a Zero Trust-based cyber resiliency strategy. This requires businesses to implement strong, non-phishable machine authentication, strict least-privilege access, and constant monitoring to protect the integrity of the data that users and AI agents can access. It should become the baseline expectations rather than aspirational goals for this year.
The Secret Value of ‘Least Privilege’ Access
Another strategy to proactively improve cybersecurity defences in 2026 is to enforce the principle of ‘least privilege’ access. This tactic grants users access only to the data that’s needed for their role. Limiting excessive access is important for preventing the potential for widespread data exposure and damage in the case of an account compromise.
Businesses, however, must strike a balance over access; if it’s too strict, it can hinder productivity and lead to shadow IT issues. Getting this balance right when it comes to privileged access is where sophisticated permission managers are invaluable tools to work with. They streamline the process and remove the guessing game of who and what to grant access to, thereby ensuring, in the case of an attack, that the entire organisation won’t be brought to its knees.
How CISOs are Adopting ‘Resilience, not Perfection’
The rate at which AI is advancing means not every organisation will be equipped with the tools or the know-how to tackle every AI-inspired attack. But as the saying goes, ‘prevention is better than cure’. It’s better to create a strong security culture than to continually chase after the next best tool.
Organisations can’t strengthen their resilience without involving every single person under their umbrella. That’s why CISOs must continue to invest in cybersecurity awareness programs.
These should include simulated AI-phishing attacks (phishing remains the number one attack vector) to test users and enable them to apply learnings from the modules.
If any user clicks on a phishing email, they should receive additional training at that very moment, to cement the learning. Over time, a good training system should automatically identify users who rarely fall for such attacks and reduce the training they receive while making the simulations they do receive more difficult. Conversely, giving persistent offenders additional bite-sized training and simulations can help improve security outcomes over time.
The key challenge for 2026 is managing the speed of AI adoption and harnessing its defensive capabilities while mitigating the risk of uncontrolled adoption. But with excellent training, cyberattack practice runs, and the adoption of Zero Trust principles, organisations will find themselves in a strong position.
About Dr. Yvonne Bernard
Dr. Yvonne Bernard is the CTO of Hornetsecurity by Proofpoint, Proofpoint’s business unit leveraging the Hornetsecurity product suite dedicated to managed service providers (MSPs) and small to mid-sized businesses (SMBs), providing next-generation cloud-based security, compliance, backup, and security awareness solutions that help companies and organisations of all sizes around the world.
Dr Megha Kumar, Chief Product Officer and Head of Geopolitical Risk at CyXcel, on whether our risk and regulatory frameworks and institutional cultures can keep pace with Agentic AI
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Within the next couple of years, Agentic AI is likely to progress from early stages of operation to be fully embedded within systems. Its expansion will be subtle rather than spectacular. It will integrate steadily into enterprise platforms, logistics networks, compliance workflows, cybersecurity operations centres and executive decision-support tools. Processes will move faster, operating expenses will decline and performance indicators will trend upward.
Yet these visible improvements mask a deeper challenge. The regulatory exposure, data governance pressures and erosion-of-trust risks associated with Agentic AI are being misjudged.
Unlike earlier AI applications designed primarily to generate outputs – whether text, imagery, or predictive insights – agentic systems are built to act. They sequence decisions, draw from multiple data environments, initiate consequential processes and function at scale with differing levels of human supervision. In sandbox environments this can seem contained and controllable. Over extended periods in live environments, however, sustained oversight, traceability and effective governance become significantly more complex.
Evolving Operational Complexity
There are two key challenges that businesses must address.
First, how do organisations monitor what agentic systems are doing once deployed? These systems evolve through updates, integrations and retraining and they interact with new data environments.
Second, how do you ensure responsible behaviour throughout the lifecycle? Regulators, policymakers and customers will likely expect firms to shift from compliance assurance to risk assurance and demonstrable evidence of trust and transparency.
The prevailing assumption is that human oversight will mitigate these risks. Human in the loop or human over the loop has become the default reassurance. In practice, however, that assumption breaks down far faster than many anticipate.
When a system works 95 per cent of the time, human reviewers limit their scrutiny. Behavioural science tells us that automation bias and complacency occur when automated systems are high-performing. Employees often become validators of AI outputs rather than critical examiners. The diligence gap widens gradually and then suddenly.
Facing Up to Difficult Questions
How do you incentivise employees to remain diligent checkers when the system mostly ‘works’? And how much time does effective oversight actually require? True review is not a cursory glance at a dashboard. It involves interrogating assumptions, validating inputs, checking context and assessing downstream consequences. In many cases, meaningful oversight may take nearly as long as performing the original task manually. When checking becomes more costly than doing the job yourself, pressure to ‘trust the system’ intensifies.
And what happens to accountability when oversight exists on paper but not in practice? Governance documentation may show layered review structures, escalation pathways and audit processes. Yet if humans are functionally disengaged, responsibility becomes dispersed. When errors surface, organisations may struggle to attribute fault – was it the model design, the data, the integrator, the operator or the reviewer who signed off without fully scrutinising?
Regulators are only beginning to grapple with these realities. In jurisdictions such as the European Union, the EU AI Act introduces risk-based obligations, documentation requirements and human oversight provisions. These are important steps, however, the operationalisation of those requirements in dynamic, agentic environments remain untested at scale. Compliance on paper will not automatically translate into resilient governance in practice.
Addressing the Trust Challenge
Beyond regulatory exposure, there is a broader trust challenge emerging.
As Agentic AI systems scale across industries, they will generate vast volumes of automated outputs – reports, communications, risk assessments, content, decisions and transactions. If errors or manipulations spread through interconnected systems, confidence in digital outputs may erode.
In geopolitically sensitive contexts, this has profound implications. Agentic systems interacting with external data sources could amplify disinformation, introduce biased datasets or make decisions based on manipulated inputs. The speed of automation may outpace the speed of verification. Trust, once diluted, is difficult to restore.
Data protection risks will also intensify. Agentic systems frequently require broad access privileges to perform tasks effectively. They may access internal databases and personal data and interact with third-party platforms. Each interaction creates potential exposure points. A single misconfiguration or prompt injection attack could trigger cascading consequences across systems.
The next phase of AI adoption will not simply amplify productivity: it will amplify regulatory, legal and reputational risk. This moment therefore demands serious scrutiny before agentic AI becomes deeply embedded in business infrastructure.
The Moment for Action has Arrived
So, what should organisations be doing now?
To begin with, organisations need to look past superficial, tick-box compliance. Effective governance cannot live solely in policy documents – it must function in day-to-day operations. This means investing in continuous monitoring capabilities, robust audit trails and real-time anomaly detection tailored specifically to Agentic AI behaviours.
In parallel, incentive structures should be redesigned. Meaningful human oversight will not happen if it is treated as secondary to speed or output. If employees are expected to provide meaningful review, organisations must allocate time, training and authority accordingly. Performance metrics should reflect risk management responsibilities, not just output rate.
Clear lines of accountability are equally important. Senior leadership and boards should determine who carries ultimate responsibility for outcomes produced by agents. Where third-party vendors are involved, responsibilities must be contractually and operationally defined. Incident response mechanisms should be rehearsed in advance, rather than presumed to work when pressure is high.
Expertise must also be integrated across functions. Legal, risk, compliance, cybersecurity, data protection and operational teams should be engaged from the outset. Deploying Agentic AI is not simply a technical upgrade – it reshapes the organisation’s risk profile.
Finally, resilience demands deliberate stress-testing. Leaders should examine not only pathways to success but how models fail at scale. How would the organisation respond if a system update embedded systemic bias, if an integration vulnerability enabled unauthorised activity or if automated actions eroded customer confidence? Rigorous scenario exercises, however uncomfortable, are essential to building genuine preparedness.
As Agentic AI advances, Risk Management Should Match its Pace
None of this is an argument against adoption. Agentic AI presents meaningful productivity improvements and the potential for sustained competitive differentiation. Organisations that deploy it with discipline and foresight may secure a measurable advantage. The danger lies not in adoption itself, but in pursuing acceleration without knowing the risks and putting the right guardrails in place.
The coming two years are critical for businesses. Before these systems become deeply embedded in core processes, organisations have an opportunity to shape the control environment around them. However, once agentic systems are fully embedded, retrofitting controls will be far more difficult and costly. Leaders must therefore treat this period as a design phase for oversight, not merely a race for competitive advantage.
Agentic AI is advancing rapidly. The defining question is whether our risk and regulatory frameworks and institutional cultures can evolve just as quickly.
As companies pour billions into developing their own AI tools, Fayola-Maria Jack, Founder and CEO of Resolutiion, argues that many are forgetting what worked well in the early tech era, confusing ownership with innovation
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Back in the very early days of computing, organisations rarely hesitated to buy the hardware and software they needed to modernise. Now we’re deep into the AI age. Many organisations are deciding the best approach to adopting the technology is to take building it into their own hands.
Many of the more traditional companies, like big banks, have publicly stated that they’re developing their own AI tools in house. Meanwhile, corporate investment in AI reached £191 billion ($252.3 billion) in 2024 and is only likely to have risen since..
Yet, the challenges of internal AI development are becoming abundantly clear. A recent report from MIT found that 95% of AI pilot projects failed to deliver any discernible financial savings or uplift in profits. It also found companies purchasing AI tools succeed about 67% of the time. Meanwhile, internal builds succeed only one-third as often.
Why do companies feel they need to build their own AI tools?
Those statistics alone show buying AI from specialised vendors and building partnerships is often the wiser choice. But, with a handful of traditional businesses deciding to lean the other way, it begs the question: why are these companies not only initially choosing the in-house route, but also persisting with it despite low success rates?
The instinct to ‘build’ is rooted in legacy thinking – and to some extent, a naivety around what makes AI solutions special. Traditional enterprises have long equated ownership with control: control over systems, data, and perceived competitive advantage.
When AI entered the scene, many executives applied that same logic, assuming that building in-house equated to ownership, at the heart of innovation. But this overlooks a fundamental truth that is unique to AI – AI isn’t another IT system you can own and stabilise. It evolves exponentially, not linearly. It demands constant retraining, rapid iteration, and deep specialisation – all at odds with the traditional corporate IT environment, which is built for stability and compliance, not experimentation and speed.
Are companies really investing in innovation?
Another common belief is that buying is seen as conceding leadership to outsiders. While building feels safer politically, signalling ‘we’re investing in innovation’. Ironically, though, that safety is often an illusion that leads to slower progress and higher long-term cost. But again, there is deep irony if talent is outsourced to India, or another foreign jurisdiction, on the basis of cheap labour.
The exact same dynamic plays out internally, too. AI initiatives are career-defining projects for senior technology leaders and they attract budget, visibility, and prestige. Once a build programme is launched, it’s politically difficult to pivot, even in the face of poor performance. As a result, the build strategy often survives by narrative rather than by evidence.
Underpinning all of this is the institutional belief that ‘our data is unique’ – that their data will deliver proprietary insight and competitive advantage. In reality, most internal data is messy, siloed, and outdated. It reflects years of practices that are often misaligned with best practice, and therefore should never be used to train AI. Instead of building capability, many organisations end up building complexity.
Increased Caution in Regulated Sectors
Alongside these misbeliefs, regulatory caution and data residency also play into the decision to build in-house; especially in regulated sectors like finance, healthcare, and government. Here, enterprises typically believe that adopting third-party AI tools may expose sensitive data to external environments they cannot fully control. Perhaps this is because data protection laws have created a heightened sensitivity to where data is processed and how it’s used to train models.
Take banks as an example – historically they have viewed data as a fortress, a core asset to be guarded. Their culture of confidentiality and regulation makes them instinctively cautious about sharing information externally. Add to this the fact that large banks already have substantial internal technology infrastructures and budgets, and building seems logical on paper. The truth, however, is that building internally doesn’t eliminate compliance risk, but often amplifies it. This is because companies take on the burden of securing systems, updating controls, and managing ethical frameworks themselves.
On the other hand, buying from specialist providers means adopting a system that’s been engineered for compliance at scale. Purchasing doesn’t dilute compliance, it accelerates it, because you inherit the expertise and validation of teams who do this. In fact, most reputable AI vendors now far exceed enterprise compliance standards, designing privacy-preserving architectures that mitigate these risks far more effectively than in-house teams can, full-time.
Competitive Edge
The financial sector’s competitive edge increasingly lies not in owning the algorithms, but in applying them better and faster. Challenger banks and fintechs have embraced this: they buy tools (whereby anti-money-laundering and fraud detection platforms are incorporated into model-risk management protocols aligned with regulatory expectations), they integrate, and they move rapidly. Traditional banks, by contrast, are still in a transitional mindset, modernising legacy systems while trying to preserve control. That’s why their build programmes are often more about transformation theatre than tangible AI capability, and will ultimately see them fall further behind.
Underestimation of AI’s Lifecycle Cost
Beyond the issues of legacy thinking, poor data quality and compliance risk, companies attempting to build in-house also face a number of additional challenges when it comes to the talent, time, and technical debt needed.
Talent: True AI expertise is scarce and expensive. Competing with the open market for top data scientists and ML engineers is unsustainable for most enterprises.
Time: AI doesn’t stop evolving while your internal team builds. By the time a prototype is ready, the underlying technology stack may have already advanced.
Technical debt: Maintaining models, retraining on new data, and ensuring explainability and auditability over time all demand continuous investment.
Most companies underestimate this lifecycle cost by an order of magnitude. Add to that the reputational risk of bias or error (especially when deploying AI in customer-facing contexts) and the true cost of internal builds can spiral quickly.
A Change in Mindset is Needed
As more of these challenges surface, we should see an uptick in companies moving towards buying AI rather than building it – and it’s a pattern that’s thankfully already emerging. As AI becomes infrastructure, not novelty, enterprises will mirror the software evolution of the 1990s and 2000s: moving from bespoke builds to modular adoption.
The early adopters that buy today will pull ahead dramatically because they can focus on application and differentiation, not on maintenance. In time, the ‘build’ approach will be seen much like writing your own word processor in 1995: a costly distraction from real innovation.
Organisations need to shift from ownership to orchestration. This requires humility, recognising that innovation now happens outside corporate walls, and confidence – trusting that your value lies in how intelligently you deploy technology, not in whether you wrote its source code. Culturally, companies need to redefine ‘strategic advantage’ as agility plus insight, not possession plus control. AI isn’t an asset you own; it’s a capability you cultivate.
In simpler terms, the companies that thrive in the AI age will be those that treat AI as an ecosystem, not an ‘ego system’.
Adam Spearing, VP of AI GTM EMEA at ServiceNow, on why those that invest in AI foundations now will shape their operating models on their own terms
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Much of the debate around AI still centres on pilots: which tools to test, which use cases to prioritise, which risks to manage. Executive teams commission proofs of concept, establish governance forums and assess compliance exposure. Far less scrutiny is applied to the consequences of waiting.
Traditional technical debt is familiar territory for CIOs. It stems from shortcuts, ageing platforms and deferred upgrades. It builds over time and is eventually addressed through structured modernisation programmes. Visible in legacy code, brittle integrations and manual workarounds. It appears on risk registers and capital plans. Leaders know how to describe it and, in principle, how to resolve it.
Forward-looking technical debt is different. It arises when organisations postpone the foundational changes needed for new ways of working. It is not created by past expediency, but by present hesitation. And it accumulates faster.
AI Adoption
In the context of AI, the effects are already emerging. Each quarter spent debating readiness instead of building it increases the distance between legacy operating models and AI-enabled competitors. As models improve and user expectations shift, that distance widens, reshaping competitive baselines. What begins as a modest capability gap can harden into structural disadvantage.
While companies debate whether to adopt AI, the margin for strategic choice narrows. Many organisations frame AI adoption as a binary decision: adopt now or wait until the technology matures further. In practice, the room for discretion is smaller than it appears. Time spent stalled in pilots or governance loops increases the gap between internal capability and market expectation.
More than 75% of organisations are expected to face moderate to severe AI-related technical debt in 2026, predicts Forrester. The issue will not simply be missed efficiency gains. It will be structural misalignment between how their systems operate and how work is increasingly done.
This misalignment often appears gradually. Teams rely on manual data preparation because underlying systems cannot support automation. AI tools are layered onto fragmented architectures and deliver inconsistent outputs. Employees experiment with external tools because internal platforms cannot provide the functionality they need. Each workaround creates further fragmentation.
Over time, these patterns compound. Integration backlogs expand. Security and risk teams struggle to enforce consistent controls across proliferating tools. Data governance becomes reactive rather than designed. What began as caution begins to constrain strategic options.
The AI Paradox
Here’s the paradox: organisations are either rushing into unsuccessful AI pilots that create immediate technical debt, or they’re avoiding AI entirely and creating forward-looking debt through inaction. Both paths lead to the same place – systems that can’t support the future of work.
AI isn’t just another technology layer to bolt onto existing infrastructure. It’s fundamentally changing how people interact with systems and how work gets done. Increasingly, AI becomes an interface through which employees access information, execute tasks and navigate processes. When AI becomes the interface – not just for customers but for employees navigating their daily tasks – organisations without AI-ready foundations will find themselves unable to compete on speed, efficiency, or experience.
The companies that hesitate aren’t just missing out on automation benefits today. They’re building a deficit that grows exponentially as AI capabilities advance. Each new model release, each competitor’s successful implementation, each customer expectation shift adds to the debt. Each significant model improvement raises the performance benchmark across the market. Unlike legacy systems that degrade slowly, this gap accelerates.
From Avoidance to Advantage
Breaking free from forward-looking technical debt requires a fundamental mindset shift. This isn’t about buying more technology or launching more AI pilots. It’s about creating the conditions for sustainable AI adoption that builds capability rather than complexity.
The organisations succeeding with AI aren’t the ones with the biggest budgets or the most aggressive rollouts. They’re the ones that took a deliberate, phased approach to ensuring their data, systems, and culture could support AI at scale. They treated readiness as an operational discipline rather than an innovation side project. They understood that AI adoption isn’t a destination, it’s a continuous capability that requires solid foundations.
This starts with honest visibility into current technology estates. Leaders must understand what systems can realistically support AI workloads, where data quality creates barriers, and which processes are ready for automation. Only then can organisations introduce AI incrementally, modernising systems where necessary rather than forcing new capabilities onto brittle foundations. Without that clarity, AI risks being layered onto structural weaknesses.
Modernisation therefore becomes targeted. Consolidating fragmented workflows, standardising data models and reducing unnecessary integration points increase the feasibility of scaling AI across multiple use cases. Early deployments focused on well-defined processes with clear data lineage can build internal confidence while strengthening governance practices.
Clear Debt to Stay Competitive
Forward-looking technical debt does not appear on a balance sheet. It shows up in slower product cycles, manual workarounds, integration backlogs and frustrated employees. It surfaces when competitors deliver AI-assisted services as standard and customers begin to expect the same everywhere. By the time these symptoms are visible, the underlying gap has already widened.
Timing therefore becomes a strategic variable. AI capability builds cumulatively: early investment in clean data, modern workflows and interoperable systems creates a base for continuous improvement. Each iteration becomes easier, faster and more reliable. Those that delay face the opposite trajectory: increasing complexity, rising retrofit costs and shrinking room for strategic choice.
The real issue is not adoption in principle. It is whether leadership teams are prepared to treat readiness as urgent rather than optional.
Reducing forward-looking technical debt requires acting before competitive pressure dictates terms, aligning technology modernisation with operating model reform, and accepting that disciplined progress now is less risky than accelerated catch-up later.
AI adoption will continue irrespective of individual organisational hesitation. Vendors will continue to refine their offerings. Regulators will clarify expectations. Customers and employees will adjust their behaviours. Those that invest in foundations now will shape their operating models on their own terms. Those that delay risk reacting to a competitive gap that is already commercially significant.
Adonis Celestine, Senior Director – Global Automation Practice Lead at Applause, on the rise of AI and why In a world of autonomous systems, trust is the ultimate competitive advantage
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Every generation of technology has its defining disruptor – the force that rises above the rest and reshapes its environment. In the mid-2000s, Marc Andreessen captured the moment when digital systems began transforming entire industries with his famous line: “software is eating the world”. At the time, software was the apex predator of technology, defining how value was created and delivered. Today, that hierarchy has shifted. Artificial Intelligence (AI) has reached the top of the technology food chain. Not just accelerating software, but fundamentally reimagining how it’s created, tested, and deployed.
AI is no longer just a tool; it is a co-creator. Developers now rely on AI daily to translate high-level intentions into working code. A practice sometimes known as ‘vibe coding’. Tasks that once took months can now be delivered in weeks, days, or even minutes. The pace is exhilarating, but it introduces challenges that traditional quality assurance (QA) practices were never designed to meet. And if QA cannot keep up, speed will come at the cost of reliability and trust.
When AI Outpaces QA
Conventional QA depends on predictability. Features are defined, code is written, and test cases verify the expected behaviour. However, AI disrupts this traditional model. Generative and Agentic AI systems don’t simply follow instructions; they interpret them. These systems adapt to context, learn from data, and can produce different outputs from the same prompt, influenced by factors such as training, temperature settings, and the model’s probabilistic nature. With development cycles now measured in minutes, traditional QA handoffs are often impossible.
This has led to a growing gap between speed and certainty. Teams can ship products faster than ever, yet it’s becoming much more difficult to ensure consistent, ethical, or safe behaviour in real-world conditions. Enterprises are already experiencing AI-powered features that fail in ways conventional testing could not anticipate, undermining trust and creating new risks.
Hidden Risks in Autonomous AI Workflows
AI-driven development introduces blind spots that traditional QA often struggles to detect. One key issue is context drift. This occurs when AI performs well in controlled testing environments but behaves unpredictably when faced with edge cases, cultural differences, or ambiguous inputs. For example, a customer-facing chatbot might pass functional tests but produce biased or misleading responses when deployed on a global scale.
Another challenge is compound autonomy. When multiple AI agents are involved in code generation, testing, and deployment, the system may begin to validate its own processes. Without human oversight, errors can propagate unnoticed. An AI agent might ‘approve’ certain behaviours because they statistically align with previous outputs. Rather than meeting user or business expectations.
Invisible change also complicates QA efforts. AI models continuously evolve through processes like retraining, prompt tuning, or data updates. A feature that worked flawlessly last week may function differently today. Traditional regression testing often fails to capture these subtle but significant shifts.
Most critically, AI workflows blur the lines of accountability. When failures occur, it can be unclear whether the issue lies with the model, the data, the prompt, the integration, or the deployment pipeline. QA teams must continuously validate not only the outputs but also the decision-making processes behind them.
Redefining Quality and Trust in an AI World
Slowing AI development is neither practical nor beneficial. Organisations must redefine quality in a probabilistic, AI-driven environment. Quality now extends beyond just correctness. It involves ensuring that systems operate reliably in real-world scenarios. This shift requires moving from static test cases to continuous, adaptive validation.
QA teams must evolve into ‘quality intelligence’ teams, broadening their responsibilities from simply detecting defects to actively fostering trust in AI systems. AI-assisted testing is crucial in this process. It can automatically generate extensive test cases by analysing requirements and code patterns. It can predict defects using machine learning. Detect visual inconsistencies across devices, and produce realistic, privacy-compliant synthetic test data. Additionally, Agentic AI can autonomously maintain and self-heal test scripts, adjusting their logic as underlying code or user interfaces change.
Furthermore, AI systems themselves need rigorous evaluation. Techniques such as red teaming, rainbow teaming, benchmarking, bias and ethics checks, and drift monitoring are essential to help promote AI’s reliability, fairness, and alignment with business objectives.
Human oversight is critical. While AI can scale testing and automate numerous tasks, critical thinking, risk assessment, and judgment cannot be fully delegated. Humans must guide, validate, and refine AI outputs to maintain both quality and trust.
Emerging Roles and Responsibilities
AI is reshaping professional roles. Developers are increasingly using AI by instructing machines through natural language rather than traditional programming methods. This shift has led to the emergence of new roles such as AI agent orchestrators, prompt engineers, QA specialists for autonomous systems, and governance leads who ensure ethical and auditable AI practices.
These roles are essential for maintaining human oversight. Developers and testers must experiment, validate, and continuously refine AI outputs while being cautious not to rely too heavily on AI.
Trust in the Age of the Apex Predator
As with any apex predator, AI has changed the rules of the game. Software once “ate the world” by making systems programmable. Today, AI “eats software” by making it autonomous, capable of creating, modifying, and deploying autonomously. In this new environment, speed is no longer the ultimate measure of success; trust is. Systems may move fast, but without rigorous QA, ethical oversight, and human judgment, they may not be reliable, accurate or ethical.
The new apex predator demands adaptation. Organisations navigating this AI-driven era must embrace automation and innovation, but pair it with strong quality practices, governance, and continual human oversight. Only by combining these elements can companies ensure their AI systems are not only fast and efficient but also dependable and aligned with business objectives. In a world of autonomous systems, trust is the ultimate competitive advantage.
Tom Lanaway is Head of Innovation at Connective3, a global brand & performance marketing agency. He leads a team building AI-powered marketing measurement and marketing intelligence tools.
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Most businesses are asking the wrong question about AI. They’re asking, ‘Which AI tool should we use?’ They should be asking: ‘Can our people actually think with AI?’
I run an innovation team at a marketing agency. We’ve spent the last two years building AI into everything we do, including measurement, content, strategy, and automation. We’ve got lots of tools, 18 different products to be precise.
Below is what I’ve learned. But the tools aren’t always the bottleneck; sometimes the skills are.
The Tennis Racket Problem
A colleague put it perfectly recently: “AI is a tool. Think of it as if you’ve got a smart assistant sat there. But it’s saying, I’m going to give you the best tennis racket, now go and play in a Grand Slam.”
That metaphor stuck with me because it captures something the artificial intelligence hype cycle keeps missing. We’ve convinced ourselves it democratises everything. That anyone can now do anything. That the barrier to entry has collapsed. And there’s truth in that, but it’s incomplete. The barrier to access has collapsed, but the barrier to effectiveness hasn’t. Give someone GPT-4, and they can generate text. Give them the best tennis racket, and they can hit a ball. But the gap between hitting a ball and playing at Wimbledon is still vast. Most organisations are stuck in that gap, wondering why their AI investments aren’t transforming anything.
Three Skills That Aren’t Always Present
When I look at where teams struggle and where I see the same patterns across other businesses, three specific competencies keep showing up as gaps:
1. Problem Decomposition
Not everyone knows how to break down complex work into chunks that AI can help with. This sounds simple, but it isn’t. Most people approach AI with whole tasks such as ‘Write me a marketing strategy’, ‘Analyse this data’ Or ‘Create a campaign’. AI will then produce something, but it’s usually mediocre, because the person hasn’t done the harder work of understanding which specific parts of that task AI is good at, and which parts need human judgment. The skill isn’t using AI; it’s knowing what to give it. Someone who is brilliant at their job but can’t decompose problems will get worse results from AI than someone more junior who understands how to break work into the right pieces.
2. Output Assessment
How do you know if what AI gives you is good? This is where intuition becomes essential and it’s also where the ‘AI replaces expertise’ narrative falls apart. You need domain knowledge to evaluate AI output. You need enough experience to feel when something’s off, even if you can’t immediately articulate why. You need the pattern recognition that comes from years of doing the actual work. Artificial Intelligence doesn’t replace that intuition; it requires it. The best AI users I’ve observed aren’t the most technical; they’re the ones who’ve built up enough expertise in their field to quickly assess whether AI output is useful, directionally correct, or completely off base. They know what good looks like, so they can recognise it when they see it, or notice when it’s missing.
3. Articulation
Can you clearly express what you really want? This is the unglamorous core of the whole thing. Some people struggle to articulate their requirements to other humans, let alone to AI. We’ve all sat in meetings where someone spends 20 minutes explaining what they need, and you’re still not sure what they want. AI makes that problem worse. The skill isn’t ‘prompt engineering’ in the technical sense; it’s the much older skill of clear thinking and clear communication. If you can’t articulate what you want specifically, precisely, with the right context and constraints, you won’t get useful output from AI or from anyone else.
The Uncomfortable Implication
Here’s what this means for how businesses should think about AI investment:
Stop leading with tools: Most organisations have tool fatigue already. Another platform, another integration, another training session on which buttons to click. It’s not working.
Start with the human work: Before asking ‘What AI should we use?’, ask ‘Can our people break down problems, assess output, and articulate requirements?’ If they can’t do those things well without AI, they won’t do them well with AI either.
Invest in the skills, not just the access: This doesn’t mean AI prompt engineering courses; it means developing clearer thinking, better problem decomposition, and sharper articulation. These are old skills, applied to new tools.
Accept that expertise still matters: The people who’ll use AI best are the ones who already know their domain deeply. AI amplifies competence; it doesn’t create it.
Connected Intelligence Isn’t About Connected Systems
I’ve spent a lot of time thinking about how different marketing channels and data sources connect and how you build intelligence across systems rather than in silos.
But I’ve come to think the more important connection isn’t between systems, it’s between human judgment and AI capability. The integration layer that matters most is the one between the person and the tool.
Get that wrong, and it doesn’t matter how sophisticated your AI stack is. Get it right, and even basic tools become powerful.
Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…
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Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams to identify and respond quickly, whilst also meeting regulatory timeframes for handling complaints and supporting vulnerable customers.
Netcall: AI-Powered Sentiment
The specialist bank has worked with Netcall to deploy AI-powered sentiment analysis using Netcall’s Liberty Create platform. The solution reduces manual effort and improves operational efficiency by bringing customer emails from multiple mailboxes into a single interface. Incoming messages are automatically analysed to identify dissatisfaction, highlighting cases that may require faster intervention. This allows urgent cases to be prioritised, helping HTB to resolve issues before they escalate and improve the customer experience.
“Our AI-powered sentiment analysis solution rapidly processes vast amounts of email data. Its efficiency allows our team to focus on resolving customer enquiries and issues rather than sorting priorities. The streamlined process ensures swifter responses and better customer outcomes, upholding our reputation for exceptional customer service.” Ed Eames, Head of Customer Savings Operations at Hampshire Trust Bank.
The application was built by the Hampshire Trust Bank development team using Liberty Create. It worked closely with Netcall to integrate AI sentiment analysis into existing processes. Customer-facing teams were involved throughout to ensure the solution aligned with established workflows and regulatory requirements.
Customer Service Control
A key benefit of the approach is the level of control it gives internal teams. Keywords, sentiment thresholds, and classifications can be adjusted directly. This allows rapid refinement as customer behaviour changes or new regulatory considerations emerge, without waiting for development cycles.
“Liberty Create has enabled my development team to work with remarkable agility. The ability to rapidly create and refine applications to meet ever-evolving business needs has significantly enhanced our efficiency. This allows us to deliver a wealth of new features to end users and customers with speed. With the integration of AI, we’ve been able to advance our processes while ensuring exceptional customer service. Our Sentiment Analysis application launch is a prime example of this.” Trina Burnett, Head of Engineering at Hampshire Trust Bank.
The sentiment analysis system also supports automated and ad-hoc reporting. This provides a single source of insight into customer interactions and actions taken. This helps reduce manual effort, supports audit and compliance activity, and enables teams to continuously improve customer service operations.
“As scrutiny around customer experience and accountability increases across UK financial services, the ability to listen, adapt and respond at pace is becoming a defining capability for banks seeking to maintain trust and service standards,” said Alex Ballingall, Key Account Manager at Netcall.
“HTB’s approach shows how banks can use AI-driven insight practically. Turning customer communications into faster action without adding operational complexity,” Ballingall concluded.
About Netcall
Netcall is a leading provider of low-code and customer engagement solutions. A UK company quoted on the AIM market of the London Stock Exchange. By enabling customer-facing and IT talent to collaborate, Netcall takes the pain out of big change projects. It helps businesses dramatically improve the customer experience, while lowering costs. Over 600 organisations in financial services, insurance, local government and healthcare use the Netcall Liberty platform to make life easier for the people they serve. Netcall aims to help organisations radically improve customer experience through collaborative CX.
In the world of MedTech, innovation does not happen in isolation. It relies on deeply interconnected digital ecosystems that span research and development, manufacturing, clinical environments and global corporate operations. For Olympus, a global medical technology company with 30,000 employees operating across multiple regions and regulatory environments, cybersecurity has become a foundational enabler of trust, resilience and patient safety.
At the centre of this transformation is Ryan Larsen, Global Head of IT Security at Olympus, whose role sits at the intersection of technology, leadership and mission-driven purpose. His mandate is clear: ensure that Olympus’ global digital and operational environments remain secure, reliable, and able to support innovation at scale.
“In practical terms, I’m responsible for the cyber defence and digital resilience of Olympus as a global MedTech company,” Ryan explains. “That means ensuring our systems, data and people are protected so innovation can move quickly, safely and with trust across R&D, manufacturing and corporate operations worldwide.”
Virginia Farm Bureau: An Enterprise CIO’s Journey
Virginia Farm Bureau is an organisation renowned for resiliency, collaboration, commitment to a greater cause, diversity and service to its members. For outgoing CIO Patrick (Pat) Caine leadership at Virginia Farm Bureau has never been about technology for technology’s sake. After 18 years as CIO, his role evolved into what he describes as that of an “enterprise technology leader,” responsible for supporting a uniquely complex organisation whose mission stretches far beyond insurance or IT.
“I’m responsible for all aspects of enterprise IT,” he explains. “Founded in 1926, Virginia Farm Bureau is a diverse membership organisation with four major business entities and multiple companies that provide agricultural advocacy and related agricultural business support services, healthcare insurance sales and administration, P&C Insurance, and a large entertainment property that hosts the State Fair of Virginia.”
Gowling WLG: Implementing Human Centred AI
When we talk about AI finding its feet within a business, the obvious challenge is change management. How do you ensure your team is on board with the change? What if they have technical questions? How does a business address their fears and concerns? This is where having a people-focused leader and a technology-focused leader forming a united front is incredibly valuable.
Kelly Davis is the Chief People Officer at international law firm Gowling WLG. Al Hounsell is the Senior Director, AI Innovation & Knowledge. Davis has been in HR leadership roles for most of her career. During that time, she has been very intentional about the way she has moved between industries.
Hounsell started his career as an entrepreneur. He then went to business school and law school, before ending up in a large global firm. There, he fell in love with the nascent legal technology ecosystem. He joined Gowling WLG over a year ago. His goal is to reimagine the practice of law by infusing it with technology.
New research from Appian shows strong optimism among public sector workers about artificial intelligence (AI) transforming public services. However, awareness among the public remains limited,…
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New research from Appian shows strong optimism among public sector workers about artificial intelligence (AI) transforming public services. However, awareness among the public remains limited, with 75% of surveyed UK adults aged 18+ (representing approximately 41 million people*) unable to name a single way in which the public sector currently uses AI.
The 2026 UK Public Sector AI Adoption Outlook report surveyed 1,000 public sector workers and 1,000 UK citizens. It reveals a clear divide between those tasked with delivering AI-enabled services and those who use them. While two thirds (67%) of public servants believe it will improve public services over the next five years – rising to 87% among director-level leaders – only 44% of citizens share this optimism. Afigure closely mirrored by workers in administrative roles (40%).
This disconnect could be explained by the way AI is currently being deployed inside government. Nearly half (45%) of initiatives operate as bolt-on experiments or standalone tools rather than being embedded into core service workflows. Many applications remain invisible to citizens – limiting public awareness of where and how artificial intelligence is already in use.
“Too much AI in the public sector is still being used as a personal productivity tool rather than embedded into the processes that actually run services. When AI is treated as a bolt-on experiment or standalone tool, it struggles to deliver meaningful impact – our research shows nearly half of government’s application of AI falls into that trap. If organisations want AI to move beyond pilots and produce real value, it has to be integrated into core processes from the start.”
Peter Corpe, Industry Lead UK Public Sector at Appian
Public Trust in AI Remains Limited
Public trust in responsible AI use remains low across much of government. Fewer than half of UK citizens trust central government (39%) or local government (44%) to use it responsibly – placing government behind retailers (60%), banks (55%) and consumer technology companies (54%). The clear exception is the NHS, which commands a 63% net trust rating, making it the most trusted organisation for AI use across both public and private sectors.
Regarding AI making decisions without human oversight, 67% of public sector workers are comfortable with the technology selecting cases for tax or benefits compliance checks compared with 40% of citizens, while 56% of public sector workers support its use in analysing NHS scans versus 40% of citizens. Concerns about AI also extend beyond individual decisions, with the majority of the public worried about implications around data security and privacy (67%), job losses (63%), auditability of decisions (61%) and ethical oversight and bias (59%).
Fixing Processes Should Come Before Delivering AI at Scale
Inside government, enthusiasm for AI is tempered by concerns about execution. Less than a third (29%) of public sector workers say their organisation or department is delivering on most of its commitments. A similar proportion say they are moving slower than planned (27%), while a quarter (25%) identify a significant gap between AI strategy and delivery.
One year on from the AI Opportunities Action Plan, where the Government allocated £2bn to implement research and resources, the new research findings point to a growing disconnect between strategic ambition and service delivery reality. Nearly 9 in 10 public sector workers (89%) say their organisation is not fully able to leverage AI.
This delivery challenge is widely recognised by both public sector workers and citizens. A majority of public sector workers (55%) and citizens (56%) agree that existing processes must be fixed before new technologies are introduced, prioritising process improvement over deploying new AI tools.
“AI is only as good as the work you give it,” said Corpe. “This research shows strong belief in AI’s potential, but also a clear warning: without fixing the underlying processes first, it will struggle to deliver on its promise. Serious AI is not about experimentation or standalone tools – it’s about applying intelligence to the core processes that keep public services running.”
Different Priorities, Same End Goal
While both citizens and public sector workers agree that existing processes must be fixed as a priority, the research reveals contrasting expectations of what AI should deliver. Citizens want AI investment to deliver faster services (35%), improved public safety and fraud prevention (27%) and easier-to-use digital services (26%).
By contrast, public sector workers are more focused on efficiency gains (47%) and cost savings (41%), highlighting that citizens focus on outcomes they directly experience and public sector workers focus on how those outcomes are delivered.
The 2026 UK Public Sector AI Adoption Outlook was commissioned by Appian and conducted independently by Censuswide. The study surveyed 1,000 UK public sector workers, including 250 director-level respondents or above, and 1,000 UK citizens aged 18+.
Gregory Mostyn, CEO and co-founder of Wexler, on why the era of generalist AI tools is over, and how the future will focus on high-precision AI designed for specific industries
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For decades, the UK’s professional services sector, including areas such as Law, Insurance, and Wealth Management, has argued that its business value is locked in its access to proprietary data and the specialised labour required to navigate it. Investors, lured by the moat of institutional knowledge, priced these companies accordingly. However, the first quarter of 2026 has seen significant AI disruption within the professional services market. The catalyst wasn’t a single event, but rather a move by foundational model providers that turned the industry’s most defensible assets into commodities.
When Anthropic launched its specialised legal AI plugin, OpenAI integrated a real-time insurance underwriting engine directly into its interface, and Alturist Corp automated bespoke tax strategies, the market reacted harshly. As professional services titans such as RELX, MoneySuperMarket, and St James’s Place saw their share prices decline by more than 10% in a matter of hours, the message became clear: the era of treating AI as a ‘future risk’ is over.
The market has been awoken to the fact that foundational AI models are no longer just plugins or nice ‘add-on’ tools; they are competitors. The move by foundation-model providers into professional services – like the legal sector – is not a one-off shock, but rather an inevitability.
The Proliferation of Information
Historically, a law firm’s competitive advantage was its access to information – repositories of case law, proprietary research, and historical contracts. Investors and clients valued these companies on the assumption that this data constituted an impenetrable barrier to competitors. Before AI entered the mainstream, the cost of extracting actionable information from thousands of pages of data required a small army of junior associates and hundreds of billable hours.
In 2026, that moat has mostly evaporated. Recent benchmarks show that frontier models now achieve 80% accuracy on complex documents, compared with the 71% average of a human associate. More importantly, they do it at a fraction of the cost. It is now estimated that the inference cost for a system at the level of GPT-3.5 dropped by more than 280-fold between November 2022 and October 2024. It’s predicted that UK law firms will reduce their chargeable hours by 16% through the implementation of AI.
The narrative that AI would be able to handle only ‘low-level’ tasks, such as NDAs or simple contract summaries, has all but evaporated. Anthropic’s move into high-stakes litigation support validates this trend.
AI – From Swiss Army Knives to Scalpels
An error made by many law firms when AI became entrenched within the market was to treat it as a ‘plug-in’, a nice-to-have built onto existing internal software. Many adopted general-purpose tools, often referred to as ‘Swiss Army knife’ solutions, that covered the breadth of legal work but lacked the precision, jurisdictional nuance, and risk-weighted requirements for high-stakes professional services.
The 2026 market reaction highlighted the needs of a ‘scalpel’ approach – those that go deep in a specialised vertical within a legal workflow. For example, instead of a junior associate spending billable hours searching through case files to establish the facts of a case, they could use a ‘fact intelligence’ platform that can automate that process into minutes, whilst increasing accuracy by 95% versus 78% for human reviewers and up to 90% savings in large-scale litigation. The market is no longer rewarding firms for having information. Rather, it rewards those who can apply it at the lowest possible cost and friction.
Reallocating Capital Across Professional Services
We’re already seeing investors withdrawing from the traditional software market and reallocating that capital into specialised AI firms. However, the risk for legacy players is that they are being disrupted from both ends. From the bottom, they are losing the efficiency game to generalist foundation models from companies such as OpenAI and Google, which are commoditising the ‘knowledge’ aspect of professional services, including basic advice and contract drafting. At the top, they are losing the expertise game to specialised firms that use AI as a precision instrument; their overhead would be lower than that of a traditional Magic Circle firm, allowing them to undercut prices while maintaining profit margins.
The result is a massive reallocation of capital. Investments into vertical AI (AI built for one specific industry) are expected to surge to $115 billion by 2034. The market no longer bets on labour with tools, but on autonomous workflows. Investors have realised that the value lies in the middle layer – the software that sits between a general foundation model and a specific industry’s needs.
Innovation or Obsolescence
So far, the first market fluctuation of 2026 has taught us that you cannot outrun new technologies. To survive, firms must stop treating AI as an add-on and treat it as a foundation for their core business infrastructure.
For UK professional services, the choice is no longer whether to adopt AI, but whether they can evolve quickly enough to avoid becoming the training data for companies building foundational models. The firms that remain in 2030 will recognise that the competitive landscape has changed. You’re not just competing with your peers, but with the compute cycles of the world’s most powerful AI labs.
The era of generalist AI tools is over, and the future will focus on high-precision AI designed for specific industries.
Kevin Janzen, CEO of Gaming & EdTech AI Studio at Globant, on how AI will change the way games are made and expand the market
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Every major games studio is now experimenting with artificial intelligence. From generating NPC dialogue to automating animation and video assets. AI is promising to speed up production and lower costs for developers.
According to Boston Consulting Group (BCG), the gaming industry finds itself at a crossroads…. Looking to gain the momentum it felt between 2017 and 2021, where revenue surged from $131 billion to $211 billion. And AI could be at the forefront of this pivotal moment.
But as AI becomes central to how games are built, studios face a major challenge. Adopting automation without losing authenticity. For developers and retailers alike, this becomes a business concern that deserves close attention. Creativity sits at the heart of gaming, and the choices studios make today will influence what reaches players tomorrow. For the technology channel, this transformation means faster release cycles, broader product diversity, and a need for sharper forecasting.
A New Phase in Gaming’s Evolution
For most of gaming’s history, every era has been defined through visuals. Each generation has delivered stylistic, immersive worlds, such as the blocky charm of Minecraft to the cinematic realism of Red Dead Redemption 2.
Now, the real change is happening behind the scenes. AI is reshaping how games are built and experienced. Development teams are using AI to handle time-consuming tasks such as vast world-building creation and animation. This frees artists to focus on what players remember – the design and storytelling.
Players are already seeing the benefits in their gameplay. AI lets games adapt or adjust difficulty based on players’ skill levels, or change dialogue based on a player’s choices. This makes gaming worlds feel realistic, responsive and more personal.
With budgets continuing to climb for gaming studios, these new features matter. AI gives studios breathing room to experiment. Smaller teams can take creative risks, and established developers can experiment and test new ideas without derailing production. However, efficiency and costs aren’t the only gains as AI is creating space for developers to be more ambitious than ever before.
Automation and Artistry
For all its promise, AI also brings creative risk. Gamers notice when a quest feels repetitive or when dialogue sounds mechanical. And if AI is used carelessly, developers risk losing authenticity.
That sense of care is what keeps players invested. Whether it’s hand drawn detail, or play-driven choices. Games like this show what happens when technology supports vision rather than replacing it.
That’s why the industry’s embrace of AI is such a gamble. Used well, AI can help developers create richer, more personalised worlds. But used carelessly, it risks stripping away the artistry that makes games memorable.
The Ripple Effect Across the Supply Chain
As AI becomes a standard tool, development processes are speeding up and opening new creative possibilities. Independent studios now have access to the kind of production power once limited to major developers. That shift means faster pipelines and ultimately, more games reaching the market.
For retailers and resellers, this brings both opportunity and pressure. A consistent stream of releases can guarantee sales across the year, while lower production costs encourage more niche or experimental games that appeal to new audiences. Greater variety and volume benefits the market, but it also makes it harder to predict which games will break through.
Players are becoming more aware of how games are made and AI’s role in development. They’re starting to ask not only how a game plays, but also how it was built. Understanding the intent behind a studio’s use of AI – one that uses AI as a genuine creative tool and those that rely on it as a shortcut – will help retailers anticipate demand and spot the games with long-term potential.
The Right Way to Play the AI Game
The studios using AI most effectively have a few things in common. They keep AI in the background, using it to manage routine work, such as generating textures and landscapes, so creative teams can focus on narrative and emotional tone.
They also use AI to make experiences more personal. Thoughtful application of adaptive systems allows games to respond to individual play styles, adjusting difficulty and pacing to keep players engaged. This level of design deepens engagement and gives players a sense that the world responds to them personally.
Another area where AI is also making an impact is making games more inclusive. More than 400 million people around the world play with a disability, and new tools are expanding access – from adaptive controls to real-time translation that lets players connect across languages. As gaming becomes more diverse, the audience grows for everyone, including retailers, who can reach a larger, more engaged customer base.
When automation complements gaming artistry, it strengthens the relationship and trust between the developer and the player. Creativity becomes the main focus again, and that’s what keeps players loyal.
Balancing Innovation and Trust
AI is fast becoming integral to how games are conceived, built, and experienced — and that shift will reshape the entire value chain. For developers, success will come from balancing automation with artistry, ensuring that AI enhances creativity rather than replaces it.
For retailers, distributors, and partners, this transformation offers both opportunity and responsibility. A faster, more diverse release pipeline will bring fresh sales potential, but also greater complexity in forecasting and curation. The winners in this new phase of gaming will be those who can spot titles where AI adds genuine depth, inclusivity, and player connection — not just production speed.
Handled thoughtfully, AI won’t just change how games are made, it will expand the market for everyone involved in bringing those experiences to players. That’s a game worth playing for the entire tech channel.
JP Cavanna, Director of Cybersecurity at Six Degrees, on balancing the risks and benefits of AI in cyber defence strategies
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Undeniably, AI is here to stay. Having become part of day-to-day life, it’s hard to remember what life was like without it. But when it comes to cybersecurity, is it causing more harm than good?
Recent research outlines that 73% of organisations have already integrated AI into their security posture. The technology is clearly becoming a cornerstone of modern cybersecurity. Organisations are turning to AI not just as a tool, but as a partner in security operations, leveraging its capabilities to identify malicious activity faster, guide investigations, and automate repetitive tasks.
For it to be truly effective, though, AI must be paired with human expertise – but this is where organisations are starting to become complacent. Given the growing sophistication of cyber-attacks, and even AI-powered attacks, many are removing the human element while expecting AI tools to do all the work for them, leaving them even more vulnerable to threats. This overreliance risks creating blind spots, where critical thinking, contextual understanding, and instinct are overlooked. Without the balance of human judgement, AI can amplify mistakes at scale, turning efficiency into exposure.
The Cybersecurity Paradox
This situation puts many organisations in a potentially difficult position. On the one hand, AI can significantly improve the efficiency of security operations. In the typical SOC, for example, AI technologies can process alerts in around 10-15 minutes. This represents a significant improvement over human analysts, who can easily require twice as long for the same task.
Aside from the obvious efficiency gains, applying AI to these repetitive, time-pressured processes can also significantly reduce the scope for human error. And in turn, take considerable pressure off security analysts. Going some way to battling alert fatigue, an increasingly well-documented and persistent problem. In these circumstances, valuable human experience and specialist expertise can instead be more effectively applied to complex investigations, strategic decision-making, and other higher-value priorities.
On the flipside, however, AI remains prone to generating inaccurate or misleading insights, and users may not realise they are applying the wrong information to potentially serious security issues. Similarly, habitual blind trust in AI outputs can easily erode performance levels and even introduce new vulnerabilities. There is also scope for sensitive data to enter public environments, with the potential to cause compliance issues. This kind of information can also reappear in future versions of the AI model in question, therefore resulting in further data exposure risks.
Parallels with IoT Adoption
The situation mirrors that seen in the early days of IoT adoption, where the rush to innovate would often override security considerations. In this current context, therefore, human oversight and vigilance are extremely important. Clear governance frameworks, defined accountability, and continuous monitoring must underpin any AI deployment. Therefore ensuring that innovation does not outpace risk management or compromise long-term resilience.
A Growing Arms Race
If that wasn’t challenging enough, threat actors are also in on the AI boom in what has already been described as an ‘arms race’. In practical terms, AI tools are already widely used to create more convincing phishing attacks free from some of the more obvious traditional tell-tale signs of criminal intent, such as imperfect grammar or a suspicious tone.
Deepfake technology has also raised the stakes. We’ve all seen how convincing AI-generated video has already become. This is now finding its way into real-world examples, with one fake video reportedly causing a CFO to authorise a large financial transfer as a result.
At the same time, technology infrastructure is constantly under attack by AI-powered tools. They can be used to analyse defensive systems and identify weaknesses faster than humans. The net result of these developments is that defenders constantly play catch-up, as they can only respond to new attack vectors once discovered. The underlying takeaway is that at present, AI cannot be trusted to operate autonomously. Instead, human intuition, scepticism and contextual understanding remain essential to spotting emerging tactics.
As attackers refine their methods at machine speed, organisations need to resist the temptation to match automation with automation alone. They must double down on strategic thinking and continuous skills development.
Balancing Benefits and Risk
So, where does this leave security leaders who are looking to balance the benefits and risks? Firstly, and to underline a fundamental point, while AI offers scale and speed, it cannot replace critical human oversight. Organisations should view AI as an enhancer, not a replacer. Success lies in promoting partnership, not substitution.
Strong governance is vital. This should start with clear AI usage policies that define what can and cannot be shared with AI tools, while proper data classification and access control ensure that sensitive information is protected. In addition, regular validation of AI outputs can help to prevent inaccurate or misleading results from being unnecessarily acted upon.
Then there are the perennial challenges associated with employee awareness training, which is vital for avoiding complacency and understanding the limitations of generative AI tools. Cyber leaders should also monitor how AI is being used inside and outside the corporate environment, as staff often experiment with tools on personal devices.
Get this all right, and security teams can put themselves in a very strong position to embrace AI, safe in the knowledge that they have the guardrails and processes in place to balance innovation and efficiency with effective human-led oversight. Ultimately, success will depend not on how much AI is deployed, but on how intelligently it is governed and refined alongside the people responsible for securing an organisation.
A 2026 survey of nearly 1,000 C-suite executives found that 87% of companies now use AI in their core operations. However, AI errors and…
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A 2026 survey of nearly 1,000 C-suiteexecutives found that 87% of companiesnow use AI in their core operations. However, AI errors and rework continue to cost businesses over $67bn a year
Loopex Digital’s January 2026 analysis identified several common mistakes companies make when relying on AI.
1. Giving AI Too Much Control in HR
AI-led hiringfilters out 38% of top-level candidates before human review because it relies on keyword matching. Candidates respond by adjusting CVs to fit those words, often hiding real experience.
“When we started to use AI in our hiring process, we saw some strong candidates get rejected,” said Maria Harutyunyan, co-founder of Loopex Digital. “Out of 100 applicants, the 2 candidates that would’ve been hired didn’t make it because they used different wording instead of the exact keywords.”
How to fix this: “We simplified our job descriptions, removed buzzwords that didn’t matter, and limited AI to shortlisting. The quality of hires improved immediately,”said Maria.
2. Trusting AI Notes Without Review
AI note-takers often struggle with background noise and poor audio, leading to inaccurate notes. In many cases, up to 70% of summaries focus on side comments rather than decisions.
“We tested 10+ AI note-takers across50 of our regular meetings. Most of the main summaries ended up being jokes and half-finished sentences,” said Maria. “Key decisions were either unclear or missing entirely from the AI summary.”
How to fix this: “We limited AI notes to action points and decisions,” said Maria. “Everything else is filtered out or reviewed manually, cutting note clean-up from half an hour to minutes.”
3. Letting Artificial Intelligence Replace Your Customer Support Team
When customers realise they’re speaking to AI, call abandonment jumps from 4% to 25%. Even when customers stay on the line, AI tools can get policy and pricing details wrong, leading to confusion, complaints, refunds, and extra clean-up work for support teams.
How to fix this: Use AI only for simple FAQs, not complex cases. Define clear escalation rules for cancellations, complaints, and legal issues and route those to a human immediately. Restrict your AI from creative responses in support, only letting it use approved templates.
Maxio analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected
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Analysis of $40B+ in billings data shows vertical focus and AI innovation driving success, while growth inflection points emerge earlier than expected
Growth remains strong for B2B SaaS and AI companies, but volatility is high, according to the B2B Growth Report by Maxio, a leading billing automation and revenue management platform. While the market is healthy overall, with the average company growing 18% year over year, more than 35% of companies experienced a decline, revealing an industry where growth increasingly depends on focus, discipline and execution rather than market momentum alone.
The report analyzed over $40 billion in billings data across 2,000+ companies from 2024-2025, revealing unexpected patterns in how growth varies by company size, business model, investment backing, and approach to AI. The findings challenge conventional assumptions about scaling thresholds, the universal benefits of AI adoption, and the predictability of growth trajectories.
“Growth didn’t disappear in 2025; it became harder to earn,” said Alan Taylor, Chief Operating Officer at Maxio. “The winners weren’t chasing every trend. Whether AI-native or traditional SaaS, the top performers stayed focused on solving real customer problems.”
Key Report Findings:
Growth is still the norm, but it’s not universal: Average company growth reached 18%, while aggregate market growth was closer to 13%, reflecting slower expansion among larger, more mature businesses. Nearly two-thirds of companies grew year over year, yet more than one-third declined. Down years remain common across all revenue bands.
Growth slows earlier than expected: The data revealed inflection points at around $5 million in billings with another slowdown beyond $25 million, not the typical $1 million, $10 million or $50 million marks, showing the operational challenges of scaling.
Vertical focus outperforms horizontal scale: Vertically focused companies grew faster than horizontal peers (20% vs 16%), reinforcing the value of specialization in competitive markets.
Capital helps, but doesn’t guarantee faster growth: Bootstrapped companies nearly matched VC-backed growth (20% vs. 22%), though scale differed dramatically with VC-funded companies nearly 4x larger. Private equity-backed companies focused more on profitability, growing 13% on average while skewing significantly larger than other cohorts.
AI accelerates, but only at the core: Truly AI-led companies, with AI central to product and positioning, grew fastest at 21%. However, AI-enhanced companies lagged at 16%, while non-AI companies quietly outperformed at 19%. This pattern suggests that AI adoption alone does not guarantee impact—AI implementation without clear value differentiation may not translate into competitive advantage.
“Average growth numbers only tell part of the story,” said Ray Rike, founder and CEO at Benchmarkit. “What stood out is how early growth friction shows up. Teams that identify where and why growth is accelerating will be best positioned to focus their resources on the market segments that provide faster growth.”
2026 Outlook
Despite a more competitive and complex environment, industry optimism is back and strong. Seventy-two percent of companies expect to grow faster in 2026 than 2025. However, leaders are entering the year with more measured expectations around buyer scrutiny, competition and the need for operational efficiency.
Sustainable growth is built, not assumed, the report found. Companies that understand their true growth levers, invest with intent, and maintain discipline as they scale will be best positioned to win in 2026.
Maxio is the billing and financial reporting platform trusted by over 2,000 SaaS, AI and subscription businesses worldwide. With $18B+ in billings under management, Maxio empowers finance teams to scale recurring revenue, automate quote-to-cash and deliver the insights needed to grow confidently.
Some Europe & Middle East CIOs anticipate up to 178% ROI on AI investments, with further efficiencies expected as Agentic AI scales
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Enterprises have moved decisively from AI pilots to scaled implementations, driven by proven benefits and expectations of significant financial returns, according to the Lenovo Europe & Middle East CIO Playbook 2026 with research insights by IDC. Nearly half (46%) of AI proof-of-concepts have already progressed into production, with organisations projecting average returns of $2.78 for every dollar invested.
The 2026 Lenovo CIO Playbook: The Race for Enterprise AI, draws on insights from 800 IT and business decision makers in Europe and the Middle East. It captures a regional inflection point and reinforces the value proposition for enterprise AI as both real and immediate. It calls on CIOs to act now to avoid lagging competitors. The research marks a clear shift from AI experimentation to measurable value creation, with nearly all (93%) of those surveyed planning to increase AI investments in the next 12 months. At an average spending growth rate of 10%, and 94% anticipating positive returns.
Enterprise AI Adoption in Europe and the Middle East
AI is now recognised as a core engine of business reinvention and competitive advantage. However, AI adoption in the markets is progressing at different speeds. Reflecting varying levels of digital maturity, regulatory readiness, and investment capacity, and there is a clear overconfidence problem among CIOs. While 57% of organisations in Europe and the Middle East are approaching or already in late-stage AI adoption, only 27% have a comprehensive AI governance framework. Further limitations in data quality, in-house expertise, integration complexity, and organisational alignment are causing a mismatch between ambition and readiness.
With Agentic AI overtaking Generative AI as the top priority for CIOs in 2026, these factors will prevent many organisations from fully capitalising on AI’s potential, leaving significant returns unrealised. Moreover, 65% of organisations are focused on scaling Agentic AI across their operations within 12 months, but only 16% report significant usage today, with the majority still piloting or actively exploring use cases.
More advanced markets such as Scandinavia, Italy, and the UK are moving beyond pilots, with a majority of organisations already systematically adopting AI and increasing focus on hybrid and edge deployments to support scale. In contrast, parts of Southern and Eastern Europe remain earlier in their AI journeys, with a higher proportion of organisations still in planning or early development stages. Meanwhile, the Middle East is emerging as a fast-moving growth market, showing strong adoption momentum and a sharp year-on-year increase in interest in advanced and Agentic AI.
Across the region, hybrid deployment models dominate as organisations balance innovation with data sovereignty and operational control. While interest in Agentic AI is accelerating. This signals a broader shift from experimentation toward more autonomous, production-ready AI use cases, even as readiness levels continue to vary by market.
“We’re now seeing clear returns from the AI pilots and proof-of-concepts organizations have invested in, with AI delivering measurable impact across the region. But many are not fully equipped with the skills, governance and readiness needed to scale AI to its full potential. As priorities shift toward Agentic AI, and compliance with regulation such as the EU AI Act becomes imperative, trust and scale must be built in from the start. Those who don’t, risk leaving tangible returns on the table.”
Matt Dobrodziej, President of Europe, Lenovo
Hybrid AI Now Preferred Enterprise Architecture
The research shows that real-world business and financial considerations are accelerating the shift toward hybrid AI. Factors such as data privacy, advanced security requirements, and the need to customise and optimise infrastructure are driving adoption of this model, which blends public cloud, private cloud, and on-premises compute. Nearly three out of five (58%) organisations now prefer hybrid as their primary AI deployment model.
Scalable, high-performing AI infrastructure is a critical enabler of enterprise AI success. Respondents in the region highlighted the importance of compute that is both cost- and energy-efficient. This factor ranked second overall, with many identifying it as key to moving AI from pilots into reliable production.
With AI PCs and edge endpoints central to an effective Hybrid AI strategy and securely running AI workloads locally, deploying AI-capable devices has emerged as the top IT investment priority for 2026.
“CIOs across the region are entering a decisive phase of AI adoption where agentic AI and enterprise-scale inferencing are moving from experimentation to core business priorities,” said Dobrodziej. “To unlock real value, organisations need strong foundations, including secure, energy-efficient infrastructure, flexible hybrid architectures, and AI-capable devices and edge endpoints that bring inference closer to where data is created, and work happens. When combined with the right governance and services, this end-to-end approach enables enterprises to innovate confidently, responsibly, and at scale.”
Lenovo recently introduced Lenovo Agentic AI, a full-lifecycle enterprise solution for creating, deploying, and managing AI agents, alongside Lenovo xIQ, a suite of AI-native platforms designed to simplify and operationalise AI across the enterprise. Built on the Lenovo Hybrid AI Advantage™, these offerings combine hybrid infrastructure, platforms, and services to address governance, integration, and performance from day one. Supported by the Lenovo AI Library of proven use cases, CIOs can reduce risk, accelerate time-to-value, and scale AI initiatives with greater confidence as they move beyond experimentation.
To further enable real-world deployment, Lenovo ThinkSystem and ThinkEdge inferencing servers help enterprises turn trained models into production-ready, low-latency AI applications across data center, cloud, and edge environments. By enabling faster, more efficient inference at scale, Lenovo helps CIOs bridge the gap between AI ambition and day-to-day business impact.
Building on this end-to-end AI foundation, Lenovo’s Smarter AI for All vision is focused on bringing AI to more people and businesses at scale, from enterprise infrastructure to AI PCs that deliver intelligent, personalised experiences directly to users. As outlined at Lenovo Tech World at CES 2026, Lenovo is advancing this vision across its AI PC and smartphone portfolio, with Lenovo and Motorola Qira representing one example of how personal AI can enhance productivity by understanding context across devices and helping users get things done.
Learn more about how enterprises can accelerate AI adoption with the right infrastructure, governance, and partnerships:Explore the full 2026 CIO Playbook report.
About the CIO Playbook Study
This is the third year of surveying CIOs in Europe and the Middle East, with Lenovo commissioning IDC which conducted research between 16th September 2025 and 17th October 2025. This year’s report draws on insights from 800 IT and business decision makers in Europe and the Middle East. Industries represented include: BFSI, Retail, Manufacturing, Telco/CSP, Healthcare, Government, Education and others.
About Lenovo
Lenovo is a US$69 billion revenue global technology powerhouse, ranked #196 in the Fortune Global 500, and serving millions of customers every day in 180 markets. Focused on a bold vision to deliver Smarter Technology for All, Lenovo has built on its success as the world’s largest PC company with a full-stack portfolio of AI-enabled, AI-ready, and AI-optimized devices (PCs, workstations, smartphones, tablets), infrastructure (server, storage, edge, high performance computing and software defined infrastructure), software, solutions, and services. Lenovo’s continued investment in world-changing innovation is building a more equitable, trustworthy, and smarter future for everyone, everywhere. Lenovo is listed on the Hong Kong stock exchange under Lenovo Group Limited (HKSE: 992) (ADR: LNVGY). To find out more visit https://www.lenovo.com, and read about the latest news via ourStoryHub.
Robert Cottrill, Technology Director at digital transformation company ANS, explores how businesses can harness the potential of AI while mitigating the growing risks to cybersecurity and privacy
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AI can transform businesses, but is it also opening the door to cyber risks? Fuelled by competitive pressure and rising government support through the UK’s Industrial Strategy, it’s no surprise that more and more businesses are racing to adopt AI.
But there’s a catch. The more businesses scale their AI adoption, the bigger their attack surface becomes. Without a proactive and structured approach to securing AI systems, organisations risk trading short-term efficiencies for long-term vulnerabilities.
The AI Boom
AI investment is skyrocketing. Businesses are deploying generative AI tools, machine learning models, and intelligent automation across nearly every function, from customer service and fraud detection to supply chain optimisation. Platforms like DeepSeek and open-source AI models are now part of the mainstream tech stack.
Initiatives like the UK’s AI Opportunities Action Plan are fuelling experimentation and adoption. AI is now seen not just as a productivity tool, but as a critical lever for digital transformation.
However, the rapid pace of AI deployment is outpacing the development of the security frameworks required to protect it. When integrated with sensitive data or critical infrastructure, AI systems can introduce serious risks if not properly secured. These risks include data leakage through AI prompts or model training, as well as AI-generated phishing and social engineering attacks
While technical threats often take centre stage, businesses also can’t forget the increasing regulatory requirements surrounding AI. As AI systems become more powerful, enabling businesses to extract valuable insights from vast datasets, they also raise serious ethical and legal challenges.
Regulatory frameworks like the EU AI Act and GDPR aim to provide guardrails for responsible AI use. But these regulations often struggle to keep up with the rapid advancements in AI technology, leaving businesses exposed to potential breaches and misuse of personal data.
The Need for Responsible AI Adoption
To build resilience while embracing AI, businesses need a dual approach:
1. Prioritise AI-specific training across the workforce
Cybersecurity teams are already stretched. Introducing AI into the mix raises the stakes. Organisations must prioritise upskilling their cybersecurity professionals to understand how AI can both protect and threaten systems.
But this isn’t just a job for the security team. As AI tools become embedded in daily workflows, employees across functions must also be trained to spot risks. Whether it’s uploading sensitive data into a chatbot or blindly trusting algorithms, human error remains a major weak point.
A well-trained workforce is the first and most crucial line of defence.
2. Adopt open-source AI responsibly
Another key strategy for reducing AI-related risks is the responsible adoption of open-source AI platforms. Open-source AI enhances transparency by making AI algorithms and tools available for broader scrutiny. This openness fosters collaboration and collective innovation, allowing developers and security experts worldwide to identify and address potential vulnerabilities more efficiently.
The transparency of open-source AI demystifies AI technologies for businesses, giving them the confidence to adopt AI solutions while ensuring they stay alert about potential security flaws. When AI systems are subject to global review, organisations can tap into the expertise of a diverse and engaged tech community to build more secure, reliable AI applications.
To adopt responsibly, businesses need to ensure that the AI they are using aligns with security best practices, complies with regulations, and is ethically sound. By using open-source AI responsibly, organisations can create more secure digital environments and strengthen trust with stakeholders.
Securing the Future of AI
AI is a transformative force that will redefine cybersecurity. We’re already seeing AI being used to automate threat detection and response. But it’s also powering more advanced attacks, from deepfake impersonation to large-scale automated exploits.
Organisations that succeed will be those that embed cybersecurity into every stage of their AI journey, from innovation to implementation. That means making risk management part of the innovation conversation, not a downstream fix.
By taking a responsible approach, investing in training, leveraging open-source AI wisely, and embedding cybersecurity into every layer of the business, organisations can unlock AI’s potential while defending against its risks.
AI is a double-edged sword, but with thoughtful adoption, businesses can confidently navigate the complex landscape of AI and cybersecurity.
Vertiv expects powering up for AI, Digital Twins and Adaptive Liquid Cooling to shape future Data Centre Design and Operations
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Data Centre innovation is continuing to be shaped by macro forces and technology trends related to AI, according to a report from Vertiv, a global leader in critical digital infrastructure. The Vertiv™ Frontiers report, which draws on expertise from across the organisation, details the technology trends driving current and future innovation, from powering up for AI, to digital twins, to adaptive liquid cooling.
“The data centre industry is continuing to rapidly evolve how it designs, builds, operates and services data centres, in response to the density and speed of deployment demands of AI factories,” said Vertiv chief product and technology officer, Scott Armul. “We see cross-technology forces, including extreme densification, driving transformative trends such as higher voltage DC power architectures and advanced liquid cooling that are important to deliver the gigawatt scaling that is critical for AI innovation. On-site energy generation and digital twin technology are also expected to help to advance the scale and speed of AI adoption.”
Extreme densification – accelerated by AI and HPC workloads; gigawatt scaling at speed – data centres are now being deployed rapidly and at unprecedented scale
Data centre as a unit of compute – the AI era requires facilities to be built and operated as a single system
Silicon diversification – data centre infrastructure must adapt to an increasing range of chips and compute
The report details how these macro forces have in turn shaped five key trends impacting specific areas of the data centre landscape.
1. Powering up for AI
Most current data centres still rely on hybrid AC/DC power distribution from the grid to the IT racks, which includes three to four conversion stages and some inefficiencies. This existing approach is under strain as power densities increase, largely driven by AI workloads. The shift to higher voltage DC architectures enables significant reductions in current, size of conductors, and number of conversion stages while centralising power conversion at the room level. Hybrid AC and DC systems are pervasive, but as full DC standards and equipment mature, higher voltage DC is likely to become more prevalent as rack densities increase. On-site generation, and microgrids, will also drive adoption of higher voltage DC.
2. Distributed AI
The billions of dollars invested into AI data centres to support large language models (LLMs) to date have been aimed at supporting widespread adoption of AI tools by consumers and businesses. Vertiv believes AI is becoming increasingly critical to businesses but how, and from where, those inference services are delivered will depend on the specific requirements and conditions of the organisation. While this will impact businesses of all types, highly regulated industries, such as finance, defence, and healthcare, may need to maintain private or hybrid AI environments via on-premise data centres, due to data residency, security, or latency requirements. Flexible, scalable high-density power and liquid cooling systems could enable capacity through new builds or retrofitting of existing facilities.
3. Energy autonomyaccelerates
Short-term on-site energy generation capacity has been essential for most standalone data centres for decades, to support resiliency. However, widespread power availability challenges are creating conditions to adopt extended energy autonomy, especially for AI data centres. Investment in on-site power generation, via natural gas turbines and other technologies, does have several intrinsic benefits but is primarily driven by power availability challenges. Technology strategies such as Bring Your Own Power (and Cooling) are likely to be part of ongoing energy autonomy plans.
4. Digital twin-driven design and operations
With increasingly dense AI workloads and more powerful GPUs also come a demand to deploy these complex AI factories with speed. Using AI-based tools, data centres can be mapped and specified virtually, via digital twins, and the IT and critical digital infrastructure can be integrated, often as prefabricated modular designs, and deployed as units of compute, reducing time-to-token by up to 50%. This approach will be important to efficiently achieving the gigawatt-scale buildouts required for future AI advancements.
5. Adaptive, resilient liquid cooling
AI workloads and infrastructure have accelerated the adoption of liquid cooling. But conversely, AI can also be used to further refine and optimise liquid cooling solutions. Liquid cooling has become mission-critical for a growing number of operators but AI could provide ways to further enhance its capabilities. AI, in conjunction with additional monitoring and control systems, has the potential to make liquid cooling systems smarter and even more robust by predicting potential failures and effectively managing fluid and components. This trend should lead to increasing reliability and uptime for high value hardware and associated data/workloads.
Vertiv does business in more than 130 countries, delivering critical digital infrastructure solutions to data centres, communication networks, and commercial and industrial facilities worldwide. The company’s comprehensive portfolio spans power management, thermal management, and IT infrastructure solutions and services – from the cloud to the network edge. This integrated approach enables continuous operations, optimal performance, and scalable growth for customers navigating an increasingly complex digital landscape.
Jon Abbott, Technologies Director of Global Strategic Clients at Vertiv, asks how we can build a generation of data centres for the AI age
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The promise of artificial intelligence (AI) is enlightenment. The pressure it places on infrastructure is far less elegant.
Across every layer of the data centre stack, AI is exposing structural limits – from cooling thresholds and power capacity to build timelines and failure modes. What many operators are now discovering is that legacy models, even those only a few years old, are struggling to accommodate what AI-scale workloads demand.
This isn’t simply a matter of scale – it is a shift in shape. AI doesn’t distribute evenly, it lands hard, in dense blocks of compute that concentrate energy, heat and physical weight into single systems or racks. Those conditions aren’t accommodated by traditional data hall layouts, airflow assumptions or power provisioning logic. The once-exceptional densities of 30kW or 40kW per rack are quickly becoming the baseline for graphics processing unit- (GPU) heavy deployments.
The consequences are significant. Facilities must now support greater thermal precision, faster provisioning and closer coordination across design and operations. And they must do so while maintaining resilience, efficiency and security.
Design Under Pressure
The architecture of the modern data centre is being rewritten in response to three intersecting forces. First, there is density – AI accelerators demand compact, high-power configurations that increase structural and thermal load on individual cabinets. Second, there is volatility – AI workloads spike unpredictably, requiring cooling and power systems that can track and respond in real time. Third, there is urgency – AI development cycles move fast, often leaving little room for phased infrastructure expansion.
In this environment, assumptions that once underpinned data centre design begin to erode. Air-only cooling no longer reaches critical components effectively, uninterruptible power supply (UPS) capacity must scale beyond linear load, and procurement lead times no longer match project delivery windows.
To adapt, operators are adopting strategies that prioritise speed, integration and visibility. Modular builds and factory-integrated systems are gaining traction – not for convenience, but for the reliability that controlled environments can offer. In parallel, greater emphasis is being placed on how cooling and power are architected together, rather than as separate functions.
Exploring the Physical Gap
There is a growing disconnect between the digital ambition of AI-led organisations and the physical readiness of their facilities. A rack might be specified to run the latest AI training cluster. The space around it, however, may not support the necessary airflow, load distribution or cable density. Minor mismatches in layout or containment can result in hot spots, inefficiencies or equipment degradation.
Operators are now approaching physical design through a different lens. They are evaluating structural tolerances, rebalancing containment zones, and planning for both current and future cooling scenarios. Liquid cooling, once a niche consideration, is becoming a near-term requirement. In many cases, it is being deployed alongside existing air systems to create hybrid environments that can handle peak loads without overhauling entire facilities.
What this requires is careful sequencing. Introducing liquid means introducing new infrastructure: secondary loops, pump systems, monitoring, maintenance. These elements must be designed with the same rigour as the electrical backbone. They must also be integrated into commissioning and telemetry from day one.
Risk in the Seams
The more complex the system, the more attention must be paid to the seams. AI infrastructure often relies on a patchwork of new and existing technologies – from cooling and power to management software and physical access control. When these systems are not properly aligned, risk accumulates quietly.
Hybrid cooling loops that lack thermal synchronisation can create blind spots. Overlapping monitoring systems may provide fragmented data, hiding early signs of imbalance. Delays in commissioning or last-minute changes in hardware specification can introduce vulnerabilities that remain undetected until something fails.
Avoiding these scenarios requires joined-up design. From early-stage planning through to testing and operation, infrastructure must be treated as a whole. That includes the physical plant, the digital control layer and the operational processes that bind them.
Physical Security Under AI Conditions
As infrastructure becomes more specialised and high-value, the importance of physical security rises. AI racks often contain not only critical data but hardware that is financially and strategically valuable. Facilities are responding with enhanced perimeter control, real-time surveillance, and tighter access segmentation at the rack and room level.
More organisations are adopting role-based access tied to operational state. Maintenance windows, for example, may trigger temporary access privileges that expire after use. Integrated access and monitoring logs allow operators to correlate physical movement with system behaviour, helping to identify unauthorised activity or unexpected patterns.
In environments where automation and remote management are becoming standard, physical security must be designed to support low-touch operations with intelligent systems able to flag anomalies and initiate response workflows without constant human oversight.
Infrastructure as an Adaptive System
The direction of travel is clear. Infrastructure must be able to evolve as quickly as the workloads it supports. This means designing for flexibility and for lifecycle. It means understanding where capacity is needed today, and how that might shift in six months. It means choosing platforms that support interoperability, rather than locking into closed systems.
The goal is not simply to survive the shift to AI-scale compute. It is to build a foundation that can keep up with whatever comes next – whether that is a new training model, a change in energy market conditions, or a new set of regulatory constraints.
Jan Van Hoecke, VP AI Services at iManage and a highly experienced computer scientist with a passion for technology and problem-solving. on navigating the AI landscape for success in 2026
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The AI landscape faces a number of big shifts in 2026. Agentic AI will undergo a reality check as enterprises discover the gap between marketing hype and actual capabilities, while organisations will go through a mindset change from treating AI hallucinations as crises to managing them, acknowledging the inherent limitations of the technology. There will also be a shift in how data will be structured in AI systems, to help the move from just finding facts (“what”) to understanding reasons (“why”). Middleware application providers will face new challenges, as those vendors controlling both platforms and data will become more influential. Finally, standardised AI chat interfaces will evolve into smarter, dynamically generated, task-specific user experiences that adapt to immediate needs.
Agentic AI Reality Check
2026 is the year when agentic AI will get a reality check, as the gap between marketing promises made in 2025 and their actual competencies will become starkly visible. As enterprise adopters share the mixed successes of agentic AI, the market will begin to differentiate between true autonomous agents and the clever workflow wrappers.
Currently, many products promoted as AI agents are, in reality, rigidly programmed systems that simply follow predefined paths. They cannot independently plan or adapt in real-time to accomplish tasks. The current evolution of AI agents closely resembles the development of autonomous vehicles: early self-driving cars could only maintain lane position by relying strictly on preset instructions, and likewise, today’s AI agents are limited to executing narrowly defined tasks within established workflows. True autonomy, where AI agents can dynamically perform and solve complex problems better than humans and without human intervention, remains, for now, an aspirational goal.
AI Hallucination Goes from Crisis to Management
In 2026, the AI hallucination crisis will reach a critical juncture as organisations realise they must learn to coexist with the current fundamentally imperfect technology – until a new technology comes into play that can effectively address the issue. The focus will shift from AI hallucination ‘crisis’ to management.
As the industry deliberates who carries the liability for AI’s mistakes and inaccuracies – the tool makers or the users – enterprises will stop waiting for vendors to solve the problem and take matters into their own hands. They will adopt a variety of pragmatic risk mitigation strategies – from double and triple-checking work, and enforcing human oversight for high-stakes decisions, to taking hallucination insurance policies.
Major model builders acknowledge that current foundational LLM technology cannot eliminate hallucinations and ambiguity through incremental improvements alone. New technology is needed. Until then, and perhaps with the realisation that a technological breakthrough is years away, users will start driving the hallucination conversation – both by building systematic defenses within how they use AI, and forcing vendors to accept shared responsibility through better documentation and clearer model limitations.
The Next Evolution in AI Data Architecture Lies in a Shift from “What” to “Why”
There will be a fundamental shift in how data is structured for AI systems, driven by the limitations of current approaches in answering complex questions. While Retrieval Augmented Generation (RAG) has proven effective at locating information and answering “what” questions, it struggles with the deeper “why” and “how” inquiries.
This limitation stems from RAG’s flat-file architecture, which excels at locating information but fails to capture the complex interconnections and relationships that underpin meaningful understanding and knowledge, especially in specialised domains like legal and professional services information.
The solution lies in AI-driven autonomous structuring of data. These systems will be better placed (than humans) to reveal critical relationships across multiple data points at scale, also highlighting the contextual dependencies essential for answering the “why” and “how” questions effectively.
Consequently, in 2026, with machines taking the lead, the method of structuring data will undergo a complete transformation, gradually eliminating the human role in creating structure, to reveal the business-critical interconnections across multiple data points.
Middleware AI Apps Squeeze
Given the essential link between data and AI, middleware companies that specialise in building custom applications layered on top of data platforms will begin to get pushed to the margins, forced to compete on niche features – while the core value of data and insight is captured by the platform owners. The true leaders will be those organisations that both own and manage their data, while also offering an AI-powered interface that enables users to interact with their data securely and efficiently, fully leveraging the capabilities of modern AI technology.
Shift to AI-generated, Task-Oriented User Interfaces
In 2026, the current traditional vendor-designed, standard AI chat-based user interfaces will transition to dynamically AI-generated task-specific user interfaces that adapt to users’ immediate needs. This represents a fundamental shift from standardised software – for example, where everyone uses identical Microsoft Word or SharePoint interfaces – to personalised, short-term user interfaces that exist only as long as the user requires them for a specific task.
This transformation will also address the critical pain point that users typically have – i.e, the crushing cognitive load of navigating bloated, feature-rich software. Instead of searching through endless menus in an overstuffed application like Excel, the user will simply state their goal – “Compare the Q3 and Q4 sales figures for our top 5 products and show me a chart” – and the AI will instantly generate a temporary, purpose-built interface – a “micro-app” – solely designed for that one single task.
In the context of dynamically generated user interfaces, both data storage and the creation of bespoke interfaces will be managed by AI. The AI organisations that will truly lead in providing such bespoke user interface-generating capability are those that possess and control their own data.
About iManage
iManage is dedicated to Making Knowledge Work™. Our cloud-native platform is at the centre of the knowledge economy, enabling every organisation to work more productively, collaboratively, and securely. Built on more than 20 years of industry experience, iManage helps leading organisations manage documents and emails more efficiently, protect vital information assets, and leverage knowledge to drive better business outcomes. As your strategic business partner, we employ our award-winning AI-enabled technology, an extensive partner ecosystem, and a customer-centric approach to provide support and guidance you can trust to make knowledge work for you. iManage is relied on by more than one million professionals at 4,000 organisations around the world.
Santo Orlando, Practice Director – App, Data and AI Services at Insight, on how your organisation can level up with Agentic AI
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By now, most of us have heard of Generative AI. Many businesses have already adopted the technology for tasks like customer service, code generation and content creation. Generative AI, however, is only the start; we’re only scratching the surface of the potential that AI has to offer
Enter Agentic AI
Unlike Generative AI, which relies on human input and prompts, Agentic AI can act autonomously to fulfil complex tasks without human intervention. As a result, nearly 45% of business leaders think Agentic AI will outpace Generative AI in terms of impact, and more than 90% expect to adopt it even faster than they did with generative AI. However, despite its promise, our joint understanding of Agentic AI – and how to implement it – is still very much in its infancy.
So, where do you start? To kickstart your Agentic AI journey here are five fundamental steps to consider.
Generative AI vs Agentic AI
If Generative AI is like having a personal assistant, supporting you one-on-one to speed up your tasks, then Agentic AI is more like having a dedicated team of smart, individual coworkers who can take initiative and get things done across your business – without needing constant oversight.
One powerful example of this in action is in sales. With Agentic AI, organisations are able to receive real-time insights during discovery calls. The AI ‘agents’ allow sales reps to respond with timely, relevant information, helping them build trust, operate faster and close deals more effectively.
By collecting and analysing data from across teams, agents can uncover patterns, translate complex metrics into actionable strategies and even highlight opportunities that might otherwise be unintentionally overlooked. In some early implementations, sales teams have reported saving five to ten hours per rep each month – adding up to thousands of hours redirected toward deeper customer engagement.
The one-to-one relationship we’ve grown accustomed to with Generative AI has evolved into the one-to-many dynamic of Agentic AI, which is capable of handling tasks for multiple users and automating entire business processes. Even more impressively, agents can make decisions, control data and take actions on their own. A capability that can seem daunting without a clear understanding of how it works.
That’s why businesses need to start small, and here are a few practical steps to get going quicklyand wisely with agentic AI.
Step 1: Getting your data ready
Agentic AI is the logical progression for organisations already exploring generative tools. However, the data needs to be in an optimal condition – clean, organised and secure – before autonomous agents can be deployed effectively.
As such, eliminating redundant, outdated and trivial (ROT) data is vital. Without removing ROT, agents may rely on obsolete information, leading to inaccurate or misleading outputs. For example, this could happen if a company deploys an HR chatbot that’s connected to outdated data sources. If an employee were to ask about their 2025 benefits, the chatbot might pull information from as far back as 2017, resulting in confusion and misinformation.
Proper file labelling, standardised document practices and use of version histories in place of multiple saved versions helps to ensure agents access only the most relevant and accurate information.
Step 2: Start with low-risk cases
Agents work on a transactional basis, charging for each operation, which can quickly add up. As such, it’s wise to experiment with simple, low-stakes applications first. This approach allows for quicker deployment and demonstrates immediate value to the business without significant costs or risks.
One example could be using an agent to assess sentiment in social media responses following a product launch. This can offer real-time feedback on public perception and inform messaging strategies. Other low-risk use cases include generating reactive press releases and monitoring competitor websites. Additionally, prioritising automation of routine tasks, especially those involving platforms like Salesforce, SharePoint, or Microsoft 365, allows teams to maximise impact without costly system overhauls.
Overall, organisations need to be willing to fail fast and expect failure. It won’t be perfect from the start. However, an experimental pilot approach helps to efficiently refine AI agents, reducing the risk of costly mistakes and making sure that only effective solutions are scaled up.
Step 3: Create a single source of truth
Establishing a dedicated, cross-functional team to explore agentic AI use cases helps prevent siloed adoption and supports enterprise-wide visibility. This team should span as much of the organisation as possible and include representatives from departments such as marketing, finance and technical solutions.
Collaborative workshops can then act as a forum to identify key processes that would benefit from autonomous capabilities and help businesses align potential applications with specific departmental objectives and broader business goals.
Step 4: Learn, learn and learn
Many companies underestimated the importance of training and governance with Generative AI – and Agentic AI is no different. Organisations need to establish clear governance to define how AI agents should and shouldn’t be used, covering not just technical implications, but HR, compliance and risk concerns as well.
Equally, businesses and those employed must understand Agentic AI’s full functionality to get the most out of it. Like with almost all technical training, AI education cannot be viewed as a one-time ‘tick-box’ exercise. Ongoing learning is necessary to keep pace with new capabilities and best practices.
For example, consider what’s already emerging, like security agents that automate high-volume threat protection and identity management tasks; sales agents that find leads, reach out to customers and set up meetings; and reasoning agents that transform vast amounts of data into strategic business insights.
Step 5: Reviewing ROI
Enthusiasm around Agentic AI is high. But before organisations dive in headfirst, it’s important they first define success. Technology can’t be the solution if there is uncertainty surrounding the goal. Successful deployment requires a clear definition of the problem organisations are looking to solve and knowledge of how to align the solution with measurable business value. Without this, initiatives risk stalling at the experimental stage.
Key performance indicators should also be identified early. These may include increased productivity, time savings, cost reduction or improved decision-making. Establishing these benchmarks and taking a data-driven approach ensures that AI initiatives align with business goals and demonstrate tangible benefits to stakeholders.
Moving forward
The process of switching to Agentic AI is about changing how businesses handle everyday problems with wide ranging effects, not just about using cutting edge technology. Iteration and learning along the way, as well as deliberate, measured adoption are the keys to increasing value. It’s simple. Success with AI starts with small, straightforward actions and use cases.
Kyle Hill, CTO of leading digital transformation company and Microsoft Services Partner of the Year 2025, ANS, explores how businesses of all sizes can make the most of their AI investment and maintain a competitive edge in an era of innovation
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Across the world, businesses are clamouring to adopt the latest AI technologies, and they’re willing invest significantly. According to Gartner, generative AI has produced a significant increase in infrastructure spending from organisations across the last few months, which prompted it to add approximately $63 billion to its January 2024 IT spending forecast.
Capable of reshaping business operations, facilitating supply-chain efficiency, and revolutionising the customer experience, it’s no wonder major enterprises are keen to channel their budgets towards AI. But the benefits of AI can extend beyond large enterprises and make a considerable difference to small businesses too if adopted responsibly.
Game-Changing Innovation
Most SMBs don’t have the same ability for taking spending risks as their larger counterparts, so they need to be confident that any investments they do make are worthwhile. It’s therefore understandable why some might assume it to be an elite tool reserved for the major players.
To understand how SMBs can make the most of their AI investments, it’s important to first look at what the technology can offer.
Across industries, AI is promising to be a game changer, taking day-to-day operations to a new level of accuracy and efficiency. AI technology can enhance businesses of all sizes by:
Enhancing customer experience
Businesses can use AI tools to process and analyse vast amounts of data – from spending habits and frequent buys to the length of time spent looking at a specific product. They can then use these insights to provide a more tailored experience via personalised recommendations, unique suggestions and substitution offers when a product is out of stock. And, with AI chat functions, businesses can provide more timely responses to any questions or requests, without always needing an abundance of customer service staff on hand.
Powering day-to-day procedures
One of the most common and inclusive uses of AI across organisations is for assisting and automating everyday tasks including data input, coding support and content generation. These tools, such as OpenAI’s ChatGPT and Microsoft Copilot applications, don’t require big investments to adopt. Smaller teams and businesses are already using them to save valuable employee time and resources and boost productivity. This also saves the need for these organisations to outsource these capabilities where they might not have them otherwise.
Minimising waste
AI is also helping businesses to drive profit, minimising wasted resources, and identifying potential disruptions. By tracking levels of supply and demand, AI can automatically identify challenges such as stock shortages, delivery-route disruptions, or a heightened demand for a particular product. More impressively, however, they are also capable of suggesting solutions to these problems – from the fastest delivery route that avoids traffic, to diverting stock to a new warehouse. Such planning and preparation help businesses to avoid disruptions which costs valuable time, money, and resources.
According to Forbes Advisor, 56% of businesses are already using AI for customer service, and 47% for digital personal assistance. If organisations want to keep up with their cutting edge-competitors, AI tools are quickly becoming a must-have for their inventory.
For SMBs looking to stay afloat in this competitive landscape of AI innovation, getting the most out of their technological investment is crucial.
Laying down the foundations
Adopting AI isn’t as straightforward as ‘plug and play’ and SMBs shouldn’t underestimate the investment these tools require. Whilst many of the applications may be easy to use, it’s important that business leaders take time to fully understand the technology and its potential uses. Otherwise, they risk missing some major benefits and not getting the most from their investment, particularly as they scale out.
Acknowledging the potential risks and challenges of implementing new AI tools can help organisations prepare solutions and ensure that their business is equipped to manage the modern technology. This can help businesses to avoid costly mistakes and hit the ground running with their innovation efforts.
SMB leaders looking to implement AI first need to ask the following:
What can AI do for me?
Are day-to-day administration tasks your biggest sticking points? Or are you looking to provide customer service like no-other? Identifying how AI might be of most use for your business can help you to make the most effective investments. It’s also worth considering the tools and applications you already have, and how AI might enhance these. Many companies already use Microsoft Office, for instance, which Microsoft Copilot can seamlessly slot into, making for a much smoother rollout.
Can my business manage its data?
AI is powered by data, so having sufficient data-management and storage processes in place is necessary. Before investing in AI, businesses might benefit from first looking at managed data platforms and services. This is crucial for providing the scalability, security and flexibility needed to embrace innovation in a responsible and effective way.
What about regulation?
The use and development of AI are becoming increasingly regulated, with legislation such as the EU AI Act providing stringent, risk-based guidance on its adoption. Keeping up with the latest rules and legislative changes is vital. Not only will this help your business to maintain compliance, but it will also help to maintain trust with customers and employees alike, whose data might be stored and processed by AI. Reputational damage caused by a data breach is a tough blow even for big businesses, so organisations would be wise to avoid it where possible.
Embracing Innovation
This new age of AI is exciting; it holds great transformative potential. We’ve already seen the development of accessible, affordable tools, such as Microsoft Copilot, opening a world of new innovative potential to businesses of all sizes. Those that don’t dip their toes in the AI pool risk getting left behind.
The question smaller businesses ask themselves can no longer be about whether AI is right for them; instead, it should be about how they can best access its benefits within the parameters of their budget.
By thoroughly preparing and taking time to understand the full process of AI adoption, SMBs can make sure that their digital transformation efforts are a success. In today’s world, this is the best way to remain fiercely competitive in a continuously evolving landscape.
About ANS
ANS is a digital transformation provider and Microsoft’s UK Services Partner of the Year 2025. Headquartered in Manchester, it offers public and private cloud, security, business applications, low code, and data services to thousands of customers, from enterprise to SMB and public sector organisations. With a strong commitment to community, diversity, and inclusion, ANS aims to empower local talent and contribute to the growth of the Northwest tech ecosystem. Understanding customers’ needs is at the heart of ANS’s approach, setting them apart from any other company in the industry.
The ANS Academy is rated outstanding by Ofsted and offers in-house apprenticeships across a range of technology disciplines. ANS has supported more than 250 apprentices to gain qualifications in the last decade via apprenticeships across technology, commercial, finance, business administration and marketing.
ANS owns and operates five IL3‐accredited data centres in Manchester and has an ecosystem of tech partners including Microsoft (Gold Partner), AWS, VMWare, Citrix, HPE, Dell, Commvault and Cisco. It is one of the very few organisations to have received all six of Microsoft’s Solutions Partner Designations.
Cathal McCarthy, Chief Strategy Officer at Kore.ai, on why now is the time for enterprises to take stock and set themselves up for a long-term, successful future in applying AI where it can make the most difference
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The generative AI boom has triggered a wave of enterprise experimentation. From proof-of-concepts to customer-facing AI Agents, which can be launched at pace but too often in isolation. This comes as MIT’s latest report finds that only 5% of Generative AI pilots are successful, with the majority failing due to poor integration with enterprise systems and in-house implementations without engagement with expert vendors.
As adoption grows, so does the call for accountability. Control and centralisation is more important than ever. Siloed operations and experimentation pilots have meant that there are a trail of disconnected tools, incomplete experiments and sometimes confusion within enterprises of where AI is being used and who is using it, meaning it can’t be governed effectively.
Now is the time for enterprises to take stock and set themselves up for a long-term, successful future in applying AI where it can make the most difference. The state of play today shows where clear changes are needed.
AI Islands
In a recent report from Boston Consulting Group and Kore.ai, 80% of AI leaders say they now favour platform-based strategies over scattered deployments. These platforms are not just about efficiency; they’re quickly becoming the only viable model for visibility, scalability and governance.
The consequences of fragmentation are starting to show. CIOs and CTOs are sounding the alarm on siloed AI solutions that make it harder to measure impact, manage risk, or move quickly. This is often the case when AI tools and solutions are implemented in-house and without proven expertise.
These ‘AI islands’ are hard to govern, expensive to integrate and nearly impossible to scale responsibly. More than half surveyed in the report say current AI solutions are slowing them down and nearly three-quarters highlight explainability and compliance as top concerns. Clearly, connecting these AI islands together via a common platform can offer more long-term benefits such as better governance, faster time to market, and cost consolidation.
Regulation Demands New Architecture
Where governance could have been considered a final step by some, it now has to be a design principle from the outset. Transparency, auditability, and oversight must be built into the very fabric of how AI is developed, deployed and monitored.
Take the EU AI Act for example, the world’s first broad AI law, now applying to general-purpose AI models from August 2nd, 2025. The rules aim to boost transparency, safety and accountability across the AI value chain while preserving innovation.
According to the BCG report, 74% of leaders believe new regulations will significantly influence how they roll out AI across their organisations. And for good reason. Fragmented systems don’t just introduce inefficiency, they create gaps that regulators, stakeholders and customers are not ready to accept.
For all the talk of regulation as a constraint, it’s also an opportunity. Regulations should be seen as catalysts, rather than roadblocks. Companies that ensure governance is hard-wired into their AI projects don’t just avoid risk, they create greater trust. And this means greater adoption. This is what leaders need to see, as increased adoption of AI products ensures sustainable, long-term growth.
Enterprises in industries holding sensitive and personal data like BFSI, healthcare and retail, are already adopting a platform-based approach. Not only does this ensure integration across the business but also means it future proofs compliance, meeting industry and government regulated standards today but also building in parameters for upcoming regulations.
Gaining Control
Adopting a platform model doesn’t limit creativity. And it doesn’t mean sacrificing flexibility. Instead of juggling multiple tools, you get one place to plug in what you’ve built and get the best of what’s out there. By running all of your AI capabilities under one unified platform and set of guardrails, your teams across the organisation move forward with one framework, which means, they move faster, make quicker decisions and have a clear understanding of what is – and isn’t – working.
Most importantly, a platform turns compliance into a competitive and operational advantage. You can swap models, scale pilots and grow without silos tripping you up, and bring centralised control. This momentum is crucial for scaling and growing an organisation. Platforms create the foundation to scale AI responsibly and effectively and that’s key for future-proofing AI projects and creating impact that matters.
Interface hears from Emergn CTO Fredrik Hagstroem on approaches to AI best practice that can drive positive business transformations
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What does it actually mean for an organisation to be AI-ready, beyond having the right tools and data
“Being AI-ready is fundamentally about openness to learning and the ability to react quickly. While having the right tools and well-managed data is essential, true readiness is defined by an organisation’s capacity to operate, monitor, and measure the effectiveness of AI solutions.
We often see organisations invest heavily in implementation and tooling, only to realise that no one is prepared to take responsibility for running, monitoring, and improving AI systems.
AI-savvy organisations design solutions differently depending on the type of work, operational versus knowledge work, and, for knowledge work, focus on measuring effectiveness rather than just productivity.”
Where do most companies go wrong when trying to embed AI into their operations?
“Many companies treat AI solutions like traditional IT projects, using user acceptance as a checkpoint between development and handover to IT operations. This approach often fails before it even begins.
AI performs tasks that typically require human intelligence, perception, reasoning, and decision-making. While AI can execute these tasks with far greater precision and consistency than humans, someone within the organisation remains ultimately accountable for the results.
The most common misstep is underestimating the need to provide users with the right level of oversight and control so they can accept accountability for AI-driven decisions.
For example, explaining how AI decisions are made and demonstrating that they are ethical and fair depends not only on transparency and traceability but also on maintaining control and proper training data records.”
How can leaders prevent transformation fatigue during AI-driven change initiatives?
“Change is inevitable, so responding to it is part of effective leadership. AI will transform how businesses operate, but transformation fatigue arises when people feel constantly subject to change rather than in control of it.
Deliberate planning and thoughtful communication help, but the most effective approach is to empower people to feel more in control. This often involves organising teams around value streams that cut across business, technology, and operations.
Leaders can ensure teams have the skills and information necessary to take ownership of outcomes and make adjustments based on real results. This is especially important with AI solutions, which should be structured to provide continuous feedback, allowing teams to monitor performance, improve models, and refine processes based on learning.”
What kind of mindset and cultural shift is required for AI to deliver long-term value?
“Delivering long-term value from AI requires a shift from control to collaboration, and from predictability to adaptability. Organisations focused on individual targets and siloed accountability often struggle to realise AI’s full potential.
Value emerges when teams adopt a collective mindset, defining success by shared outcomes, whether customer experience, business impact, or strategic growth. Individual productivity only matters when it benefits the whole system.
Another critical shift is embracing uncertainty. Traditional corporate cultures often reward certainty and fixed plans. Cultures that support experimentation, feedback loops, and incremental change are more likely to see lasting benefits from AI.
This cultural evolution isn’t just about tools; it’s about how work is structured, how teams interact, and how decisions are made. Empowering teams to act fast, learn fast, and improve fast is central to sustaining AI-driven value.”
How can organisations balance AI experimentation with maintaining trust, transparency, and alignment with business goals?
“Each AI initiative should be evaluated based on the type of work and value it aims to deliver, whether efficiency, experience, or innovation. Different goals require different levels of oversight and distinct success metrics, making a portfolio approach to investment essential. Maintaining alignment with business goals means focusing on outcomes rather than outputs.
This requires systems where feedback, transparency, and learning are built in from the start, allowing initiatives to fail gracefully. Trust begins with a clear governance framework, as AI, like any transformative technology, can have unintended consequences. Transparency is not just audit trails; it’s about inviting dialogue, sharing lessons learned, and adapting as standards and regulations evolve.
Experimentation and learning go hand in hand. Delivering incremental value early builds credibility and transparency, helping teams understand what works and what doesn’t. Ultimately, AI is only valuable to the extent that it drives the business toward its strategic goals.”
How do organisations deal with some of the risks associated with AI – hallucinations, privacy issues, etc. – and how do they go about both securing essential data and overcoming employee resistance to the technology?
“Treating AI adoption as an iterative, feedback-driven process is key to managing risks. Success is less about getting everything perfect from the start and more about structuring work to minimise unintended consequences and adapt quickly.
“Hallucinations” is a misleading term. Today’s AI doesn’t imagine things; it follows programmed rules based on probabilities and patterns. Like any software, AI carries risks of errors or mismanaged data.
What is new is how AI uses data, to train models that imitate human decision-making. Without careful management, models can produce biased or unethical outcomes. Technology does not remove employee accountability. Recognising this allows organisations to design AI solutions with lower risk.
Designing solutions with humans in the loop is critical. It promotes transparency and explainability and is the most effective way to overcome resistance while maintaining control over outcomes.”
Join thousands of attendees in Dubai for the 2nd annual Artificial Intelligence & Data Science conference and find out what’s new in Data & AI
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Attend one of the leading international conferences aimed at gathering world-class researchers, academics, industry experts, and students to present and discuss the recent innovations in Artificial Intelligence (AI), Machine Learning, and Data Science. As technology increasingly transforms industries and societies globally, this conference offers a valuable chance to exchange ideas, share knowledge, and build collaborations. These will define the future of intelligent systems and data-driven decision-making. Register for tickets now!
Artificial Intelligence & Data Science – The Conference Program
The program of the conference aims to offer both theoretical and practical viewpoints with keynote talks by global experts, oral and poster sessions, panel sessions, exhibitions, and courses. Participants will be able to learn about the latest methods in AI and Data Science from real-world use cases. Join discussions regarding the ethical, social, and technological issues involved with using AI in various fields from healthcare, finance and education to retail, transportation and smart cities.
Expected Take-Aways:
Technical Insights & Deep Learning
Future-Ready Competencies
Actionable Tools & Recipes
Business & Strategic Frameworks
Network & Collaborations
Visibility & Recognition
Confidence & Vision
Career Development & Leadership Skills
Networking in Dubai
The host city, Dubai, also lends a unique flavour to the conference. As a world-renowned centre of innovation, business and technological advancement, Dubai is known for its world-class infrastructure and international accessibility. It’s the perfect platform for international collaboration. In addition to professional interaction, delegates can also sample the city’s cultural diversity and lively atmosphere, complementing their conference experience.
Among the key objectives of the conference is to ensure networking and cooperation among the attendees. Researchers, practitioners, students, and policymakers can meet, learn from each other, and discover possible partnerships that stimulate innovation. Students and young professionals learn from mentorship, exposure to new technologies, and the opportunity to showcase their work to the world. Industry attendees learn about the latest trends and solutions that guide strategic decision-making and competitive edge.
Artificial Intelligence & Data Science is a gateway to knowledge, cooperation, and innovation. It provides participants with the tools, networks, and intelligence needed to succeed in the fast-changing technological landscape.
If you are a researcher, professional, student, or policymaker, attending the Artificial Intelligence & Data Science Conference 2026 in Dubai is an unbeatable chance to help shape the future of AI and Data Science across the globe. Register for tickets now!
Samsung and OpenAI Announce Strategic Partnership to Accelerate Advancements in Global AI Infrastructure
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Samsung will bring together technologies and innovations across advanced semiconductors, data centres, shipbuilding, cloud services and maritime technologies
OpenAI, Samsung Electronics, Samsung SDS, Samsung C&T and Samsung Heavy Industries have announced a letter of intent (LOI) for their strategic partnership to accelerate advancements in global AI data centre infrastructure and develop future technologies together in relevant fields. This expansive collaboration will bring together the collective strengths and leadership of Samsung companies across semiconductors, data centres, shipbuilding, cloud services and maritime technologies.
The signing ceremony was held at Samsung’s corporate headquarters in Seoul, Korea, attended by Young Hyun Jun, Vice Chairman & CEO of Samsung Electronics; Sung-an Choi, Vice Chairman & CEO of Samsung Heavy Industries; Sechul Oh, President & CEO of Samsung C&T; and Junehee Lee, President & CEO of Samsung SDS.
Samsung Electronics
Samsung Electronics will work with OpenAI as a strategic memory partner to supply advanced semiconductor solutions for OpenAI’s global Stargate initiative. With OpenAI’s memory demand projected to reach up to 900,000 DRAM wafers per month, Samsung will contribute toward meeting this need with its extensive lineup of high-performance DRAM solutions.
As a comprehensive semiconductor solutions provider, Samsung’s leading technologies span across memory, logic and foundry with a diverse product portfolio that supports the full AI workflow from training to inference.
The company also brings differentiated capabilities in advanced chip packaging and heterogeneous integration between memory and system semiconductors, enabling it to provide unique solutions for OpenAI.
Samsung SDS
Samsung SDS has entered into a potential partnership with OpenAI to jointly develop AI data centre and provide enterprise AI services.
Leveraging its expertise in advanced data center technologies, Samsung SDS will collaborate with OpenAI in the design, development and operation of the Stargate AI data centers. Under the LOI, Samsung SDS can now provide consulting, deployment and management services for businesses seeking to integrate OpenAI’s AI models into their internal systems.
In addition, Samsung SDS has signed a reseller partnership for OpenAI’s services in Korea and plans to support local companies in adopting OpenAI’s ChatGPT Enterprise offerings.
Samsung C&T and Samsung Heavy Industries
Samsung C&T and Samsung Heavy Industries will collaborate with OpenAI to advance global AI data centers, with a particular focus on the joint development of floating data centers.
Floating data centers are considered to have advantages over data centers because they can address land scarcity and lower cooling costs. Still, their technical complexity has so far limited wider deployment.
Building on their proprietary technologies, Samsung C&T and Samsung Heavy Industries will also explore opportunities to pursue projects in floating power plants and control centers, in addition to floating data center infrastructure.
Starting with the landmark partnership with OpenAI, Samsung plans to fully support Korea’s goals to become one of the world’s top three nations in AI and create new opportunities in the field.
Samsung is also exploring broader adoption of ChatGPT within the companies to facilitate AI transformation in the workplace.
About OpenAI
OpenAI is an AI research and deployment company. Our mission is to ensure that artificial general intelligence benefits all of humanity.
About Samsung Electronics Co., Ltd.
Samsung inspires the world and shapes the future with transformative ideas and technologies. The company is redefining the worlds of TVs, digital signage, smartphones, wearables, tablets, home appliances and network systems, as well as memory, system LSI and foundry. Samsung is also advancing medical imaging technologies, HVAC solutions and robotics, while creating innovative automotive and audio products through Harman. With its SmartThings ecosystem, open collaboration with partners, and integration of AI across its portfolio, Samsung delivers a seamless and intelligent connected experience.
Robert Cottrill, Technology Director at digital transformation company ANS, explores how businesses can harness the potential of AI while mitigating the growing risks to cybersecurity and privacy
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AI can transform businesses, but is it also opening the door to cybersecurity risks?
Fuelled by competitive pressure and rising government support through the UK’s Industrial Strategy, it’s no surprise that more and more businesses are racing to adopt AI.
But there’s a catch. The more businesses scale their AI adoption, the bigger their attack surface becomes. Without a proactive and structured approach to securing AI systems, organisations risk trading short-term efficiencies for long-term vulnerabilities.
The AI Boom
AI investment is skyrocketing. Businesses are deploying generative AI tools, machine learning models, and intelligent automation across nearly every function, from customer service and fraud detection to supply chain optimisation. Platforms like DeepSeek and open-source AI models are now part of the mainstream tech stack.
Initiatives like the UK’s AI Opportunities Action Plan are fuelling experimentation and adoption. AI is now seen not just as a productivity tool, but as a critical lever for digital transformation.
However, the rapid pace of AI deployment is outpacing the development of the security frameworks required to protect it. When integrated with sensitive data or critical infrastructure, AI systems can introduce serious risks if not properly secured. These risks include data leakage through AI prompts or model training, as well as AI-generated phishing and social engineering attacks
While technical threats often take centre stage, businesses also can’t forget the increasing regulatory requirements surrounding AI.
As AI systems become more powerful, enabling businesses to extract valuable insights from vast datasets, they also raise serious ethical and legal challenges.
Regulatory frameworks like the EU AI Act and GDPR aim to provide guardrails for responsible AI use. But these regulations often struggle to keep up with the rapid advancements in AI technology, leaving businesses exposed to potential breaches and misuse of personal data.
The Need for Responsible AI Adoption with Cybersecurity
To build resilience while embracing AI, businesses need a dual approach:
1. Prioritise AI-specific training across the workforce
Cybersecurity teams are already stretched. Introducing AI into the mix raises the stakes. Organisations must prioritise upskilling their cybersecurity professionals to understand how AI can both protect and threaten systems.
But this isn’t just a job for the security team. As AI tools become embedded in daily workflows, employees across functions must also be trained to spot risks. Whether it’s uploading sensitive data into a chatbot or blindly trusting algorithms, human error remains a major weak point.
A well-trained workforce is the first and most crucial line of defence.
2. Adopt open-source AI responsibly
Another key strategy for reducing AI-related risks is the responsible adoption of open-source AI platforms. Open-source AI enhances transparency by making AI algorithms and tools available for broader scrutiny. This openness fosters collaboration and collective innovation, allowing developers and security experts worldwide to identify and address potential vulnerabilities more efficiently.
The transparency of open-source AI demystifies AI technologies for businesses, giving them the confidence to adopt AI solutions while ensuring they stay alert about potential security flaws. When AI systems are subject to global review, organisations can tap into the expertise of a diverse and engaged tech community to build more secure, reliable AI applications.
To adopt responsibly, businesses need to ensure that the AI they are using aligns with security best practices, complies with regulations, and is ethically sound. By using open-source AI responsibly, organisations can create more secure digital environments and strengthen trust with stakeholders.
Securing the Future of AI
AI is a transformative force that will redefine cybersecurity. We’re already seeing AI being used to automate threat detection and response. But it’s also powering more advanced attacks, from deepfake impersonation to large-scale automated exploits.
Organisations that succeed will be those that embed cybersecurity into every stage of their AI journey, from innovation to implementation. That means making risk management part of the innovation conversation, not a downstream fix.
By taking a responsible approach, investing in training, leveraging open-source AI wisely, and embedding cybersecurity into every layer of the business, organisations can unlock AI’s potential while defending against its risks.
AI is a double-edged sword, but with thoughtful adoption, businesses can confidently navigate the complex landscape of AI and cybersecurity.
Enterprise-wide AI platform security protects sensitive data and governs integrations to help organisations scale Agentic AI with confidence
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ServiceNow the AI platform for business transformation, has unveiled its new Zurich platform release. It delivers breakthrough innovations with faster multi-agentic AI development, enterprise-wide AI platform security capabilities, and reimagined workflows. New intelligent developer tools enable secure vibe coding with natural language. This helps turn employees into high-velocity builders and creators and lower the barrier to app creation. Built-in security capabilities, including ServiceNow Vault Console and Machine Identity Console, natively secure sensitive data across workflows. This governs integrations to help organisations scale Agentic AI and innovations with confidence. The introduction of autonomous workflows turns data into action through agentic playbooks. Uniquely offering the flexibility to apply AI and human input in workflows where and when it’s needed for greater control and efficiency.
AI Transformation with ServiceNow
Enterprise leaders are racing to move beyond table-stakes AI implementations to unlock transformative, tangible results. According to Gartner, “By 2029, over 60% of enterprises will adopt AI agent development platforms to automate complex workflows previously requiring human coordination.” The ServiceNow AI Platform delivers this transformational promise across the enterprise. It underpins a new era of highly efficient human-AI collaboration.
“Zurich marks a turning point for enterprise AI. ServiceNow is delivering multi-agentic AI systems in production that are not just powerful, but governable, secure, and built for scale,” said Amit Zavery, president, COO, and chief product officer at ServiceNow. “We are transforming the enterprise tech stack to be AI-native. From autonomous workflows that act on data with precision, to developer tools that democratise high-velocity innovation. With built-in controls for security, risk, and compliance, we’re helping organisations move beyond experimentation. And into a new era of intelligent execution.”
Vibe Coding Meets Enterprise Scale
According to Gartner, “Agentic AI features will be near ubiquitous, embedded in software, platforms and applications, transforming user experiences and workflows.” The introduction of ServiceNow Build Agent and Developer Sandbox provides resources for employees to work with AI more efficiently. They can now do this conversationally, and at scale, to solve real problems in every corner of the business.
Build Agent is a breakthrough for enterprise app creation—bringing vibe coding to the rigor of the ServiceNow AI Platform. In seconds, employees can turn an idea into a production-ready application by asking in natural language. Say, “Create an onboarding app that assigns tasks to HR, IT, and Facilities,” and Build Agent handles the rest. Design, build, logic, integrations, testing, and industry-leading governance included. What sets it apart is enterprise discipline: every app comes with audit trails, security, and compliance built in. Developers and citizen creators alike get the speed of AI with the confidence of enterprise-grade control, in a streamlined interface.
Developer Sandbox empowers developers to build better applications, faster, while maintaining the highest standards of quality. Sandboxes provide isolated environments within a single instance, so multiple teams can collaborate, build, and test new features without conflicts, and rapid scale doesn’t come at the cost of control. Teams can version, iterate, and deliver without waiting in line for developer resources. Developers can safely experiment with vibe coding, test AI-powered workflows, and resolve version control issues before changes go live. This reduces rework, shortens feedback loops, and helps teams ship higher-quality applications rapidly with lower risk.
Security That Enables AI Strategy
As enterprises adopt autonomous workflows powered by agentic AI, securing how these systems access data and communicate across environments is essential. Zurich introduces new built-in AI platform security capabilities to make it easier to protect sensitive information. It can also govern integrations and manage growing AI footprints.
The newServiceNow Vault Console provides a guided experience to discover, classify, and protect sensitive data across workflows. For example, an admin managing customer service operations can now identify personal data across tickets, apply different types of protection policies, and track compliance activity. The console also offers recommendations for protecting newly discovered sensitive data, along with customizable dashboards to monitor key metrics. What used to require manual configuration across multiple tools can now be managed in one place, with intelligent insights and a streamlined experience.
Machine Identity Console addresses the need for integration security with enterprise-grade authentication and authorization, delivering control over bots and APIs head on. As the ServiceNow AI Platform scales, every API connection, including those from AI agents, introduces another identity to manage and determine what it can access. This console gives platform teams visibility into all inbound API integrations using machine identities such as service accounts and keys, flags outdated or weak authentication methods, and provides clear steps to strengthen security. If an integration is using basic authentication or hasn’t been active in 100 days, the console spots it and helps resolve it.
Digital Transformation
“At Kanton Zürich, digital transformation is central to how we deliver secure and efficient public services. Since 2018, ServiceNow has enabled us to centralize and standardize our processes with data security as a top priority,” said Jürg Kasper, head of business solutions, Kanton Zürich. “Zurich’s latest advancements in both security and AI will allow us to automate more complex workflows, unlocking new efficiencies that enhance how we serve our citizens—with greater speed, clarity, and assurance.”
Without built-in security and trust, scaling AI comes with risk. These new security features in Zurich build upon ServiceNow’s AI Control Tower, announced in May 2025, which provides enterprise-wide visibility, embedded compliance, and end-to-end lifecycle governance for Agentic AI systems. By centralising oversight of every AI agent, model, and workflow, native or third-party, the AI Control Tower ensures organisations can scale AI with confidence, aligning innovation with enterprise-grade security and trust.
Turn Data Into Outcomes With Autonomous Workflows
As organisations rapidly scale AI, they face the added challenge of delivering solutions consistently, reliably, and responsibly. Enterprises need the right guardrails, full visibility, and strong governance to achieve service delivery. Or they risk eroding trust and slowing results. ServiceNow’s AI Platform does all this in a single platform. It sets a new standard for how organisations can create autonomous workflows to turn data into action and AI into measurable business impact.
Agentic playbooks from ServiceNow bring people, automation, and AI together seamlessly, powering autonomous workflows. A traditional playbook is a structured sequence of automated steps. These are based on predefined business rules and processes—ideal for ensuring consistency, efficiency, and trust. Agentic playbooks amplify this model by embedding AI into the trusted framework. AI agents eliminate manual effort, completing tasks in seconds and accelerating execution. This frees employees to focus on higher-value work where human judgment matters most. For example, in a credit card support situation, an agentic playbook can guide an AI agent to verify someone’s identity. It can freeze a card, send a replacement and notify the customer while allowing a human agent to step in. The result: governed, efficient, and trusted work—supercharged by AI to deliver faster, smarter outcomes.
The ServiceNow Zurich platform release also seamlessly combines Process and Task Mininginsights within a unified platform. These new capabilities give organisations an end-to-end understanding of how work gets done. Revealing where human expertise is essential, and where AI agents can deliver the greatest impact. With process intelligence built directly into the platform, customers can move seamlessly from insight to action. Streamlining operations, applying AI where it matters most. And accelerating real business outcomes without the complexity of disconnected legacy tools.
The Financial Transformation Summit (FTS), presented by MoneyNext, took place June 18-19 2025 at London’s ExCeL Centre, Royal Victoria Dock. With over 2,000 attendees, 300+ speakers, and 400 roundtables, it stood out as one of the most immersive and interactive events in the financial services calendar.
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FinTech Strategy hit the conference floor at the heart of the action delivering insights from experts across Banking, Insurance, Wealth, and Lending at Financial Transformation Summit (FTS).
Financial Transformation Summit attendees from banking, insurance, wealth, lending, fintech, consultancy, and regulatory sectors convened for two days packed with keynotes, panel talks, immersive demos, and networking among 60+ exhibitors and startups.
Co-located streams – Banking, Insurance, Wealth, and Lending part of themed zones– meant that ticket-holders could explore adjacent sectors fluidly across a guiding theme: culture, collaboration, and customer centricity driving tech adoption and transformation.
Programme Highlights
Keynotes & Panels
1. Data Silos & Cross‑Institutional Collaboration
A panel featuring senior leaders from EVLO, Aon, Schroders, and Brit Insurance tackled how institutions – despite collectively spending over $33 billion annually on data – still struggle to collaborate due to privacy concerns and regulation. Innovative solutions included federated learning, anonymised client IDs and consent-backed APIs.
2. Digital Insurance via Wallets
Anna Bojic (Miss Moneypenny Technologies) unveiled a fresh take on insurance – embedding policy and claim data into Apple/Google Wallets. The idea: dynamic customer interaction directly from smartphone wallets, enhancing real‑time engagement and retention.
3. ESG Economics & Market Reality
Marc Kahn (Investec) challenged ESG orthodoxy, urging firms to emphasise human and planetary wellbeing – beyond purely financial returns – to capture stakeholder trust and sustainable growth.
4. People & Psychological Safety
Kirsty Watson (Aberdeen Group) and Vikki Allgood (Fidelity International) underlined that technological investments are futile without organisational design and psychological safety. Allgood cited a McKinsey study revealing only 26% of leaders build teams with a sense of safety – a critical step toward innovation.
5. Human‑Centred AI
Monica Kalia (Planda AI) championed AI that models individual financial contexts – recognising diversity within demographic cohorts and personalizing services accordingly.
Roundtable Experiences at FTS
At the event’s heart were the TableTalk roundtables – 400+ small-group sessions, each led by a subject-matter expert. These were limited to six participants each, enabling deep, peer-led discussions on themes like:
AI in risk and compliance
Open banking integration
ESG data standards
Cyber resilience
Change management and culture adaptation
Attendees consistently praised their interactive nature – far removed from the stage‑focused “listening” format often critiqued at other conferences.
Demonstrations & Exhibitor Showcase
Over 60 exhibitors presented tech-driven innovations: Generative AI, open‑banking APIs, ESG reporting tools, embedded finance solutions, and more. A few standouts were:
CRIF highlighted AI-powered credit scoring with ESG overlays – promising dynamic risk assessments backed by sustainability data
Emerging FinTechs demoing AI compliance engines, digital wallet insurance packaging, and data-sharing platforms
Hylanddemonstrated the intuitive end-user experience of its Hyland Content Innovation Cloud™ and showed how easy it is to configure, tailor and deploy solutions that can empower key stakeholders across any business
The demo zone allowed engaging, hands-on exploration and real-time Q&As; it complemented the content with practical insights.
Standout Themes & Strategic Insights
1. Tech is Not Enough Without Culture
Recurrent messaging emphasised that culture, trust, governance, and psychological safety are foundational – not secondary – to digital initiatives. Technology alone won’t deliver transformation without a people-first mindset.
2. Cross‑Sector Data Collaboration
Despite heavy investment, institutions still operate in silos. Shared, secure infrastructure and regulatory-aligned frameworks are being prototyped, but broad adoption remains a work in progress.
3. AI-as-a-Personalisation Backbone
AI is shifting from automation to empathy. Organisations showcased tools to hyper-personalise offers yet maintain privacy and inclusion – moving beyond outdated demographic frameworks into genuine behavioural understanding.
4. Embedded Finance & Digital Wallets
Insurance via wallet applications and embedded finance models point to seamless customer journeys – less app hopping, more value delivered at the point of need.
5. Rebalancing ESG & Profit Metrics
Speakers emphasised integrating ESG factors into performance metrics – not just for compliance, but as an operative advantage anchored in long-term stability and stakeholder trust.
Who Should Attend FTS Next Year?
Ideal for:
Transformation and change leaders
CTOs, CIOs, and Heads of Innovation
Data and AI strategists
Operational and HR leaders focused on culture
FinTech innovators and solution providers
If you’re crafting digital transformation strategies, an attuned leader in financial services, or a consultant embedding tech in legacy environments, this summit provides rich, actionable content.
Expect next year’s event to build on this foundation:
More AI-specific tracks, possibly Generative AI streams
ESG deep-dives with case studies on implementation
Expanded regulator involvement around data governance and cross-border compliance
FTS: Final Verdict
Overall, the FTS 2025 delivered on its brand promise:
Interactive and inclusive: 400 roundtables empowered voices across levels.
Cross‑sector learning: Banking, Insurance, Wealth, and Lending streams offered both breadth and depth.
Insightful keynotes: Big ideas on AI, ESG, data-sharing, and culture were well-explored.
Real-world relevance: Exhibitor demos connected theory with practice.
Networking with purpose: Opportunities to engage, learn, and collaborate were abundant.
The Financial Transformation Summit struck a compelling balance between big-picture vision and granular, execution-level insight. It emphasised that while technology enables; culture, customer centricity and collaboration drive real progress. The format – with its roundtables, demos, and keynotes – offered a dynamic platform for knowledge exchange.
If you attended, chances are you left with practical next steps. If you didn’t, you missed one of the most interactive, future-focused events shaping financial services transformation today.
Collaborating with Amdocs has been a game-changer for Telkom. Here’s why.
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As telecom companies race to adopt generative AI, a critical shift is underway – from generic copilots to deeply verticalised, telco-grade agents. Amdocs, in collaboration with AWS and NVIDIA, is leading this evolution with its amAIz Agents – introducing a new class of AI agents built specifically for the telecom industry.
Unlike general-purpose AI, verticalised agents are built with domain-specific knowledge, reasoning, and telco ontology that reflect the complexity of telecom operations. These agents understand service plans, billing structures, and network topologies, enabling them to deliver context-aware responses and take meaningful action.
Amdocs, NVIDIA and AWS released a publication that defines and showcases how AI agents can be tailored for specific telecom domains, illustrating the concept of ‘agent verticalization’ and its impact on operational efficiency and customer experience. These domain-specific agents, across every telco domain like care, sales, network, and marketing, work in coordination, enabling end-to-end automation and intelligent customer engagement through seamless orchestration.
In the whitepaper, ‘AI Verticalization for Telco’, Amdocs outlines the essential traits of telco-grade agents such as composable architecture, reasoning, and agentic experience, and enterprise-grade traits such as trust, security, and cloud-native scalability.
Amdocs: Three decades as a key transformation partner
It’s a rare thing, in the fast-paced world of technology, for partnerships to last decades. However, for Telkom, Amdocs has been by its side for almost 30 years. The latter has played a critical role in supporting both mobile and wireline operation through its B/OSS platforms. These platforms are regarded as industry leaders, and Telkom has been able to navigate major shifts with Amdocs’s help, from legacy to next-gen digital stacks.
“We have been in this game for some time, being the digital backbone of choice for South Africa, really, Amdocs has been a strategic partner of Telkom for over 30 years,” says Dr Noxolo Kubheka-Dlamini, Chief Digital and Information Officer at Telkom. “We have a shared goal of delivering a better, faster, and more seamless experience to our customers. What stands out about Amdocs is their deep domain expertise, strong delivery capabilities, commitment to our success, and ability to evolve with our ambitious goals. We see them as an extension of our own teams.”
Hermann Tischendorf is the Chief Information & Technology Officer at MTN MoMo (the telco’s mobile money division). He reveals a bold roadmap for leveraging FinTech to drive financial inclusion across the African continent.
“MoMo is comparable in monthly active users to some of the top ten FinTechs globally. We’re playing in the same league as Revolut or Nubank – but in much more complex markets,” notes Hermann. “Access to financial services is fundamental. Without it, people are excluded from the global economy. Our services are the equaliser. They allow individuals in frontier markets to participate in trade, store value, and ultimately improve their quality of life.”
Pima Community College: Digital Transformation on a Public Sector budget
Higher education is typically seen through this lens. Slow to adopt new technologies, traditionally inflexible, and held back by a lack of funding. At Pima Community College in Tucson, Arizona, a quiet revolution is underway that subverts these expectations. The college is a publicly funded, two-year higher education institution. Serving Pima County and beyond, it has an annual student body of 38,000 served by almost 2,500 faculty and staff.
Led by Isaac Abbs, Assistant Vice Chancellor for IT and CIO, the college is undergoing an extensive IT transformation. This has unlocked immense value through bold, visionary leadership. Crucially, it is being achieved without a major increase in budget explains Abbs.
“If, as an IT leader, you become a truly innovative partner and move the organisation forward, the dollars are there.”
State of Missouri: Security as a Foundation for Innovation
Megan Stokes, Director of Cloud Security & Strategy at State of Missouri, digs into the many ways in which the agency is leveraging technology – and how it’s keeping the citizens of Missouri at the forefront.
“I have the opportunity to guide agencies through best practices, helping them access the right resources, the right expertise, and make sure that the solutions they’re building on are really secure and well architected going forward,” she explains. “That includes a focus on risk management, access control, optimisation, governance and compliance, and long-term strategy. There’s always something new to think through, and that keeps the role really exciting and engaging. There’s always lots of work to be done.”
RAKBANK: A Banking Transformation in the UAE
Our cover story explores the digital transformation journey of RAKBANK in the UAE. Head of Digital Transformation, Antony Burrows, reveals the agile practices, enterprise-wide enablement and people-first culture delivering digital banking with a human touch.
“Culture is the cornerstone,” Antony stresses. RAKBANK codifies this into its Four Cs Framework – Connect, Communicate, Collaborate and Celebrate. “Here in the UAE, banks are pivoting from a model of ‘we know everything’ to recognising that one of the best ways to deliver continuous change and value to customers is through partnerships with startups and FinTechs. It’s no longer banks versus startups – it’s banks and startups, working together for the customer. This shift is especially meaningful as banks expand beyond traditional services to focus on customers’ broader financial lives.”
Digital DNA – Exploring core infrastructure, platform strategies, and foundational technologies.
Embedded Intelligence – AI, machine learning, data strategies, and real-time analytics.
Beyond Fintech – Partnerships between fintechs and other sectors like retail, health, and climate.
Governance 2.0 – Regulation, digital identity, privacy, and ESG compliance.
Day three featured more impactful sessions across all four pillars, offering attendees more valuable insights and strategies for innovation.
Highlights from Key Sessions at Money20/20 Europe:
How to Create and Leverage FinBank Partnerships
The discussion focused on the evolution and success of FinTech partnerships with banks. Key points included the shift from transactional partnerships to more collaborative, value-driven relationships, emphasizing joint KPIs and product creation.
Alex Johnson, Chief Payments Officer, Nium
“You really have to differentiate. You really have to stand out for a bank to say, ‘Yeah, I like what you offer enough to go through, six months of onboarding.’ Dare I say, maybe more.”
John Power, SVP, Head of JVs & AQaaS, Fiserv
“The legacy system, it’s a fact of life. They’re there. They’re pervasive. They’re going to be here for a long time, and banks historically have made huge investments in those platforms and systems. So I think both the challenge for the for the bank and the opportunity for the FinTech is, how do you at the front end of those legacy systems develop new products that can scale and that you can bring cross border easily and readily.”
“It really is cutting the line to be able to deliver opportunity for customers and to be able to expand propositions for new customers.”
“The economic development supply chains shifting to low to middle income countries are incredibly important right now, and cross border payment rails have not been good in low middle income countries.”
Where Fintech goes Next: Tapping into Platforms and Verticals
The discussion centred on the democratisation of financial services through embedded finance. The panel emphasised the importance of data quality, personalisation, and strategic partnerships in delivering seamless financial experiences – ultimately enhancing customer satisfaction and improving business efficiency.
“Embedded finance is going to be defined by region and use cases.”
Amy Loh, Chief Marketing Officer – Pipe
“Small businesses don’t want to manage their business through a bunch of different tools that are stitched together. They’re looking to platforms to do everything for them and keep high end services.”
“Most platforms or merchants out there trying to diversify revenue, and they will get auxiliary revenue, or maybe get primary revenue through FinTech activity.”
The Neobanks Strike Back
In a dynamic exploration of neobanking’s evolution, Ali Niknam revealed bunq’s remarkable journey from a tech-driven startup to a sustainably profitable digital bank. By leveraging AI across every aspect of their operations, bunq has transformed traditional banking, reducing support times to mere seconds and creating a hyper-personalised user experience. Niknam emphasised the power of user-centricity, showing how innovative features like simple stock trading and multi-language support can democratise financial services.
The bank’s strategic approach – focusing on user needs rather than investor expectations – has enabled them to expand thoughtfully, with plans to enter the UK and US markets. By embracing technological change and maintaining a relentless commitment to solving real customer problems, bunq exemplifies the next generation of banking.
Ali Niknam, Founder & CEO, bunq
“Somewhere in the 70s, we let go of the gold standard, and now currencies are basically floating. The only reason why a dollar or a euro is worth what it’s worth is because of trust and perception. Philosophically, it’s very logical that we have found another abstraction layer by introducing stablecoin, which is not much else than a byte number that has a denomination currency as a backing asset that itself doesn’t have anything as a backing asset. A lot of people might ask, ‘Why would you need a stablecoin? We have euros. I go get a coffee, pay with Apple Pay or cash.’ But there are many countries on this planet where the local currency is not stable. If your country has an inflation rate of 30,000% like Zimbabwe, you would really love to use a different currency. The US dollar has been the currency of choice, but as a normal person, you cannot access the US dollar. A US dollar stablecoin that you can access by simply having a mobile phone – that’s going to be transformational for large groups of people.”
Innovating When Regulation Can’t Keep Up: Lessons from NASA
Lisa Valencia covered an array of topics, from her 35 year career at NASA and Guinness World Record to the rise of private entities like SpaceX, which has launched 180 missions this year, and the increasing role of public-private partnerships in space exploration. The speaker also touched on international collaborations, particularly with the European Space Agency and the Italian Space Agency, and the potential for space tourism and colonization of the moon.
Lisa Valencia, Programme Manager/Electrical Engineer – Pioneering Space, LC (ex NASA)
“Back in the day, NASA got 4% of the national budget. Now it’s down to just 0.1%, so we’ve had to get creative with private partnerships. SpaceX is the perfect success story. They came to us in 2007 needing money after some rocket mishaps, and look at them now! From my balcony, I see their launches every other day. They’re planning 180 launches this year alone.Talk about a return on investment!”
“We’re planning to colonise the South Pole on the moon. The idea is to extract water and hydrogen from the regolith—both for living there and for fuel.”
Scaling Internationally in 2025: Funding, Innovating, and Breaking into New Markets
The conversation focused on the growth and strategy of fintech companies, particularly those with a strong presence in Europe and the US. The panel featured Ingo Uytdehaage, CEO and co-founder of Adyen, and Alexandre Prot, CEO of Qonto. Both leaders expressed a preference for organic growth over acquisitions, emphasizing the importance of scaling efficiently before pursuing an IPO.
Ingo Uytdehaage, CEO and co-founder of Adyen
“I think an important part of scaling a company is not just thinking about your product, but also considering the markets you want to address, and how you ensure you become local in each country.”
“We realised over time that if we really want to bring the customers, we need to have the best licenses to operate. A banking license gives you a lot of flexibility.”
“Being independent from other companies, other financial institutions, that gives you flexibility to build what your customers really want.”
“I think it’s very important, also in Europe, that we continue to be competitive. If you think about regulations and AI, we shouldn’t try to do things completely differently compared to the US.”
Alexandre Prot, CEO of Qonto
“We need to be very strict about tech integration and avoiding legacy which slows us down.”
“We still need to scale a lot before we have a successful IPO. A few team members are working on it and getting the company ready for it. But, the most important thing is just scaling efficiently in the business, and maybe an IPO would be welcome in a couple of years.”
Putting The F in Fintech
The panel discussion focused on the role of women in FinTech based on personal experiences.
Iana Dimitrova, CEO, OpenPayd
“At times, being underestimated is helpful, because if you’re seen as the competition, driving an agenda is becoming more difficult. So what I found, actually, over a period, is that bringing your emotional intelligence, leaving the ego outside of the outside of the room, and just focusing on execution is is incredibly helpful.”
Megan Cooper, CEO & Founder, Caywood
“The moment we start defining ourselves as like a female leader or a female entrepreneur, you almost kind of put yourself in a bit of a box. And so I think just seeing yourself on an equal playing field and then operating it on an equal playing field and interacting in that way is quite advantageous.”
“We can’t just want diversity and hope it happens. We actually have to be intentional about creating it.”
Valerie Kontor, Founder, Black in Fintech
“Black women make up 1.6% over the FinTech workforce, but when we look at the financial reality of black women by the age of 60, only 53% of black women have enough money in their bank account to retire. We need to start marrying people in FinTech and the people that we need to serve.”
Money20/20 Europe 2025 closed its doors but the next edition of the conference will return to Amsterdam from June 2–4, 2026, promising to continue the tradition of shaping the future of financial services…
From June 9-13, London Tech Week gathers investors, enterprises, and startups from around the world to network, learn, and solve the most pressing challenges facing the IT sector.
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London Tech Week 2025 is coming. The event will take place from June 9–13 at Olympia London, and is one of the world’s largest tech events, drawing over 45,000 attendees from across 90 countries. Designed to bring together the innovators creating the technologies of the future, the investors who fund them, and the enterprise tech leaders who adopt them, the event is one of the most impactful gatherings of tech professionals in the industry.
“Innovators. Investors. Tech giants. The visionaries applying new tech to solve the world’s biggest problems. Enterprise tech leaders who are creating solutions to make work easier and life more fun,” according to the event website. “They all come to London Tech Week to see where tech will take them next.”
This year, London Tech Week is expanding, occupying double the space at Olympia, new features and a whole new experience. Keynote and expert speakers at this year’s event include: Dame Melanie Dawes, Chief Executive at Ofcom; Darren Hardman, Corporate VP & CEO at Microsoft UK; Dr Jean Innes, CEO of the Alan Turing Institute; Sir Tim Berners-Lee, inventor of the World Wide Web; renowned science educator and broadcaster, Professor Brian Cox; and many, many more.
This year’s event targets key demographics across the tech space, including…
Startups
Attending this year’s event are future unicorns, top investors and the tech leaders of tomorrow. Attendees have the opportunity to connect with visionary founders from some of the UK and Europe’s most exciting startups, and learn how they’re approaching funding, scaling, and solving some of the world’s most pressing challenges.
Enterprise
Attendees will also have the opportunity to learn how large corporates are pushing the boundaries of innovation by embracing emerging technologies. This year’s London Tech Week will feature insights from top industry leaders about how they are driving productivity, efficiency, and competitiveness across various sectors.
Investors
London is home to a world class investment ecosystem, with VCs, CVCs and angel investors. Many will be attending this year’s event — on the lookout for their next venture. The London Tech Week 2025 enhanced app is designed to help startups and other investment-seekers find people with the right profile in order to maximise their time at the event.
“London Tech Week is THE gathering spot, not even in London or in the UK, but in Europe. You can meet wonderful tech companies here.” – Canva
Image courtesy of London Tech Week 2025
The Fringe
The London Tech Week Fringe Event programme takes place from 9 – 13 June across London, featuring smaller organisations and niche topics you won’t find on the more mainstream technology conference circuits. The event’s partners cover a wide range of topics from emerging areas to established industry trends. This year the event it featuring fringe events covering SpaceTech, Healthcare, Areospace & Automotive, Investment, AI, Entrepreneurship, and more.
Learning Labs
Back for its second year at London Tech Week, the Learning Labs offer diverse content and learning opportunities. These sessions, presented by our leading event sponsors, cater to all experience levels. Learn about The Tech Lifecycle, AI and Data Integration, Natural Intelligence, Building a Strong Digital Core, and more. Learn more about attending London Tech Week 2025 here.
US Department of Homeland Security: Integrating with the Intelligence Community
Zeke Maldonado, CIO at the US Department of Homeland Security (DHS) is tasked with integrating the Department with the intelligence community. During times of change, governments need innovative, strategic leadership more than ever. And that’s where inspirational figure like Maldonado come into play.
“I remain committed to the DHS mission and want to take it to the next level. Many of the services we provide require substantial improvements, and I am eager to see how our modernisation efforts can help achieve the desired objectives. We play a crucial role in automating and enhancing the vetting process for non-US citizens, making it significantly more efficient.”
Cotality: The AI-powered Property Platform
Cotality,the AI-powered property and location intelligence platform, is making the real estate industry more efficient, smarter, and more resilient against climate change by leveraging the Google Cloud Platform.
Chief Data and Analytics Officer, John Rogers, explains how… “Buying a home is the biggest purchase in most people’s lives, so we’re passionate about making sure the system works for them.”
Nemko Digital: Pioneering Trustworthy AI
Nemko boasts more than 90 years of building trust in physical products, Today, Nemko’s digital division is leading the way in defining that trust in an increasingly complex and connected world with its pioneering approach to trustworthy AI reveals Managing Director, Dr Shahram Maralani.
“We want to be one of the top five players in this space. Our goal is to make the world a safer place.”
Tech Show London is coming to Excel March 12-13. Register for your free ticket now!
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Unlock unparalleled value with a single ticket that gets you free access to five industry-leading technology shows. Welcome to Cloud & AI Infrastructure, DevOps Live, Cloud & Cyber Security Expo, Big Data & AI World, and Data Centre World.
Tech Show London has it all. Don’t miss this immersive journey into the latest trends and innovations.
Discover tomorrow’s tech today
Unleash Potential, Embrace the Future. Hear from the greatest tech minds, all in one place.
Dive into a world where cutting-edge ideas shape your tomorrow. Tech Show London is the epicentre of technology innovation in London and beyond, hosting the brightest minds in technology, AI, cyber security, DevOps, and cloud all under one roof.
The Mainstage Theatre is not just a stage; it’s a launchpad for innovative ideas. Witness a stellar lineup featuring world-renowned experts from across the tech stack, influential C-level executives, key government figures, and the vanguards of AI and cybersecurity. All ready to share ideas set to rock the industry.
GLOBAL INSPIRATION, LOCAL IMPACT
Seize the opportunity to be inspired by global visionaries. Furthermore, with speakers from the UK, USA, and beyond, prepare to be inspired by transformative concepts and actionable strategies from technology insiders, ensuring your business stays ahead in an ever-evolving technology landscape.
Where the future of technology takes the stage
Secure your competitive edge at Tech Show London, the UK’s award-winning convergence of the industry’s brightest tech minds.
On 12-13 March 2025, gain vital foresight into the disruptive technologies reshaping your market, and position your organisation at the forefront of technology’s next frontier.
If you’re defining your business’s tech roadmap, register for your free ticket to join us at Excel London.
We chat with the CIO of Urenco, Sarah Leteney, about the ways this unique business leverages technology, and the big difference a small team can make.
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Urenco does things a little differently. It has to. It supplies uranium enrichment services and fuel cycle products for the nuclear industry – a niche that requires a lot of specialist care and attention. Urenco has a clear vision for the net zero world. A world in which carbon-free energy is the norm. And for its CIO, Sarah Leteney, this means approaching the world of technology in different and interesting ways.
Leteney speaks exclusively to Interface Magazine about what it means to operate IT in a high-risk environment that requires an enormous amount of consistency. She also discusses the types of systems that are vital to Urenco, how the business leverages suppliers, bringing in the most talented possible people, and how Urenco balances a small team with a high pressure environment.
How does the role of CIO within the nuclear industry differ from one for a consumer goods company?
Most CIOs spend their time thinking about how to talk to customers through the rapid exchanges that are needed to maintain the flow of high volumes of traffic. They need to know how to keep up with their competitors in terms of customer experience and how to quickly bring new products to market.
At Urenco, we are quite literally the polar opposite of this. We are concerned with the consistency and timeliness of highly individualised communications with our customers, how internal control software can enable the accurate flow of information to our regulators, and how to support our teams to keep track of every gram of raw material, and product in our organisation. Our systems are vital to keep our operations safe and reliable. It is not fast-paced – rather a very careful and considered environment where accuracy is everything.
What is it like to enable and provision services in such an environment? Can you keep in touch with market trends? Is there much recognition of what you do?
I work in a high threat environment and there are many special considerations to understand. There is a certain cadence and rhythm to what we do and we have to work at a pace which suits the organisation, rather than keep up with the latest trends in the IT industry. Although, we do keep abreast of developments through networks such as Gartner and Aurora and introduce them where appropriate and relevant.
In relation to the recognition of this role, like every other CIO out there, you are noticed more when something is not working properly. That said, Urenco is very good at making you feel as if you are part of something that matters. People readily ask you questions and understand when something is a minor glitch compared to something more significant. And we actively encourage people to report issues because that is how you get continuous improvement. Overall, the organisation takes care of my team, we’re not under siege when things go wrong and what we do is widely appreciated.
What sorts of systems are you looking after and what are the challenges around these?
We have all the same systems that you see in many other large organisations, plus a few really niche products used only in our industry.
Like lots of businesses, we are on a SAP journey, moving existing systems into S4. This programme impacts all parts of the organisation and we have to drive the changes forward from a business point of view. We consider the IT team an enabler for this work as it’s ultimately the transformation of our business processes which we are trying to facilitate.
We also look after the information assets of the organisation – both the structured and unstructured data. Like many organisations, it’s an on-going process to work out how to extract genuine business insights from vast amounts of historical data which has been stored in multiple places and not always in the most logical manner. We have a significant amount of historical information which still remains important (think plant designs and maintenance records, etc.) so effective archiving and retention policies are very much at the forefront of our minds. It’s so easy to over store or over classify information in an effort to be ‘safe rather than sorry’, but in reality, as well as increasing on-going costs, this sort of behaviour tends to make it harder to find what you need. We are investigating new technologies to help us search through our data faster and more effectively than ever before.
We’re also currently extending into the Operational Technology sphere, sharing our experience and tools with our OT colleagues and directly addressing operational security challenges, investing significantly in our cyber defences to further strengthen our plant security services.
What is it like to work in a company with a large turnover but a relatively small number of employees? How does that affect the service you provide?
We try to think through what every employee needs from IT and provide them with the level of service their role requires, regardless of their position in the business. We are in the fortunate position where having fewer employees means individual changes to software, hardware, or SAAS costs tend to have a less significant impact on our profitability than in many organisations with higher staff complements. Many organisations have tiers of users which determine the level of service received. However, in our organisation, every minute of everyone’s time is important, as we don’t have many employees driving our engine forward. We are investing in our employee experience as one of the key organisational imperatives working alongside our colleagues in the People and Culture team, and this is going to be an on-going focus for us for the next few years.
Whilst the company turnover is important, it is less of a driving factor for us in IT. We benchmark ourselves against what proportion of operational expenditure we are investing in IT and IS to ensure we invest an appropriate amount in IT for an organisation of this size.
How do you work with your team to ensure they can provide the most effective service to the business?
We are organised primarily around our production sites, with a centralised team to provide shared services like architecture and finance. The organisation is only two layers deep in most teams, so information flow is mainly managed by direct cascade. The senior team is made up of heads of shared functions and site IT managers, and opinions flow freely between them.
Our IT Leadership team has a monthly two-day meeting where we come together in person. We sit together without our PCs and the constant pinging of information. This helps us to realign, to reprioritise matters, and include coaching and learning techniques. We all have daily pressures in our lives, and these meetings are about supporting each other and working effectively together.
Once a quarter we also visit one of our sites as a group, hosted by our IT site managers. This is critical to us because we cannot do our jobs without thoroughly understanding the experience of IT services on the ground. These visits also allow us to meet up with our business colleagues as part of their site leadership teams so we can exchange experiences and strategic thinking quite freely in person.
We also run monthly townhall meetings for all members of the IT team, and invite our colleagues from Information Security to join us. We have found this to be a really valuable information exchange point. IS can hear exactly what we are saying to the wider team on the ground, so they can gain real insight into our issues first hand. Our key suppliers are also invited to these sessions on a quarterly basis, again to foster free exchange of information.
How about diversity and inclusion – what are you doing within that area and what have you achieved?
This is one of the biggest areas I would like to tackle further. Within our company, like the whole of the nuclear sector, the age of our employees is increasing year on year as we have a very low employee turnover. So we have a small number of vacancies on an annual basis and we are working hard to get a better talent pool for when these opportunities arise, reaching out to people with a wider range of backgrounds.
Our strategy includes blind sifting, engaging with people who have had periods of time out of the workplace and may need to work certain hours, and being open to job-sharing. It is possible for us to be very flexible and we are trying to ensure this is known out in the world of recruitment.
One area we are doing really well in right now is neurodiversity. We have a significant proportion of our team who identify as neurodivergent and a new staff network focussing on the specific issues of importance to this community was actually started by a member of our team.
I’d love to see an ethnicity and gender mix in the future which is closer to the population norms in each of our operating countries and I’m pleased to say that our talent acquisition partners are working hard to promote our roles in new talent pools with a much more diverse population.
How do you work with your suppliers to maintain a good relationship with them?
We’re currently in the process of diversifying our IT supply base. We have had a couple of really strong suppliers for a long period of time who work very closely with us, but what we are aiming to do now is widen our group of key suppliers to create a supplier ecosystem consisting of four different types of partner – Advisory, Development, Configuration, and Support. A key part of this initiative will be about embedding the behaviours we would like suppliers to demonstrate when working with us to create an inclusive and transparent relationship, which we are progressing through setting up a Urenco Academy to provide initial onboarding and on-going behavioural reinforcement of Urenco’s core values across our partnerships.
You recently won a CIO 100 award. How did that come about and what reaction did you get from people who know you?
The CIO 100 award came about through my external mentor asking me why I wasn’t looking at it! He encouraged me to put myself forward for consideration. Sometimes you need a bit of a push from a critical friend to remind you that whilst you see how much remains to be done, it’s good to acknowledge the great results you have already achieved.
The most gratifying thing about the whole experience for me was that you are judged by really experienced CIOs, so they fully understand the complexity of what you do. I’m incredibly grateful and humbled to be included in such an inspiring group of people, who are all wrestling with organisational struggles and trying to keep up in a fast-paced world, solving problems all day, every day.
My colleagues were delighted for me and sent lots of congratulatory messages. I think my team were slightly surprised because they also don’t always see what a good job they are all doing. One of them was even inspired to send an AI-created poem in celebration!
Urenco gave me the opportunity to take on a challenging and exciting role initially as an interim CIO. They chose to promote from within despite having strong external candidates, and not only that, but they asked if I would like to have a mentor in my first year to help me to cement the skills I wanted to strengthen for my own peace of mind. I’m not sure what else I could have asked for from this organisation. When I look at the award all I really think, looking back over the last three years, is ‘how amazing is that’!
Xerox has been a household name for decades. For many, it’s associated with photocopiers and printers. After all, it’s the…
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Xerox has been a household name for decades. For many, it’s associated with photocopiers and printers. After all, it’s the largest print company in the world. But it’s also a technology powerhouse that’s been at the forefront of a great deal of innovation. It has undergone a journey of evolution and reinvention into an IT and digital services provider. That’s what led to the business acquiring a large managed service provider, Altodigital, in 2020.
Derek Gunton has spent nearly 20 years in the technology sphere. He came to Xerox as part of the Altodigital acquisition. Altodigital also started out as a management print organisation and evolved into the IT services side, so its journey mirrors Xerox’s in many ways. “Now, as we move into the next technological age powered by AI and automation, we’ve put ourselves in a good position,” says Gunton.
“Xerox continues to evolve as a company. It recently announced the acquisition of another large managed services IT business called Savvy, which will double the size of the IT services business. That gives us a lot of speciality, a lot of scale, and prepares us for that leap into the technologies of the future.”
Supporting Lanes Group’s technology
Xerox has been supporting Lanes Group in its own growth journey for a few years now. It doesn’t provide print services, but the IT and digital services Xerox is gradually becoming known for. The relationship began during the COVID-19 pandemic, when the working environment was very different. Businesses were trying to figure out how to continue to operate as normally as possible and provide certainty for staff.
“There were just two of us from Xerox working with them, and we were talking about room planning software,” says Gunton. “How do you manage how many people are in the building? How do they book spaces, or manage people in line with the COVID legislation that was in place? The conversation started there. Then, we were asked what we could do around providing some managed service desk support just to assist the internal team at the time – and it’s grown from there. Four years later, we have over 30 members of staff dedicated to the Lanes account, supporting more than 4,000 users across over 50 states.
“We’re very much an operation that compliments Lanes Group. The thing that has always worked well is that we have the ability to respond and scale. Lanes have been on their own journey over the last few years to the point that they’re truly industry-leading, and we’ve managed to keep up whilst always looking to innovate, make suggestions, and bring new solutions to the table.”
An integrated technology partnership
Lanes Group supports key utilities including water and gas. What it does is absolutely critical. If there are problems in those areas, millions of people can be affected. So while Lanes has a huge responsibility to always be ready to support those utilities at all times, Xerox has just as much of a responsibility to be in a position to support Lanes.
“It’s massively important, and everybody in our business is briefed on what Lanes does to ensure we understand that responsibility,” says Gunton. “In my career, I’ve seen lots of different structures in terms of how we work with clients. Sometimes it can be very much a supplier-client relationship where it’s very siloed and formal. What sets our relationship with Lanes Group apart is that it’s a very integrated partnership. There are several meetings every week. There are dedicated program managers, and every product area has its owner. We have very strict SLAs to adhere to and the only way to deliver what Lanes needs is through communication and mutual support.”
Streamlining inconsistencies
A perfect example of the collaborative relationship between Xerox and Lanes Group is the secure network solution Xerox put in place. Effectively, Xerox mapped out and replaced the network infrastructure of all Lanes Group sites, giving better visibility, better control, and a better user experience.
“When we first reviewed the sites, there were over 50 of them running independently. That was difficult for the IT team to manage,” says Gunton. “It led to a lot of inconsistencies. We had mixed feedback from end users. Our aim was to introduce a technology system that would give the users the ability to have a consistent experience across all sites. We worked with our partners at HPE to identify the latest Ariba access solutions available, and deployment across all sites has been very successful. It’s also improved security, giving users the ability to skip length authentication processes. The user experience is really smooth now, which is what we were after.”
Creating agility
Working as partners, not in a supplier-client capacity, has made all the difference for the two businesses. From robot process automation to take manual tasks away from humans, to the increased use of AI-driven tools, Xerox is providing Lanes with what it needs to be agile. It’s a relationship based on trust and a shared goal.
“I do appreciate the help from the stakeholders at Lanes, because they embrace the same kind of culture,” Gunton says. “Often we’ll do joint meetings where we all address the same problem or desire to innovate together. We trust each others’ skill sets and openness to really come up with a solution. Ultimately, it’s all people-driven. It’s based on having really clever people in the right places, and we’ve built up a really solid team over the years.”
The evolution Lanes Group is going through isn’t going to slow down any time soon. That means Xerox’s work won’t either. Gunton states: “Our broad priorities with Lanes also reflect the current UK landscape. Data integration and automation are the areas we’re continuing to focus on. We have to think about how we deliver that. In terms of data, there needs to be one true source. You have to be really confident in the information you have, being as accurate as possible.”
What’s key for Xerox is ensuring that Lanes Group is able to shift from being reactive to more proactive. That is its focus. “We’re already delivering technology solutions to better equip Lanes to respond in that manner. I think the next year is going to be really exciting as we continue to develop that. We believe that we will continue to put Lanes at the forefront of their industry with the solutions that we supply.”
It’s impossible not to be inspired by the energy at a DPW event. DPW Amsterdam 2024 was buzzing with that…
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It’s impossible not to be inspired by the energy at a DPW event. DPW Amsterdam 2024 was buzzing with that same energy, its attendees soaking in information and inspiration from speakers, peers, other experts. We caught up with Rujul Zaparde, Co-Founder and CEO of Zip, at the event to dive into the procurement landscape and chat about the specific qualities DPW brings to the sector.
Zaparde is the Co-Founder and CEO of Zip. At the beginning of Zip’s journey, Zaparde and his fellow founder, Lu Cheng, based the company around their own experiences as end-users of the procurement process. They took their lived confusion around having multiple intakes for a contract, for the purchase request, and all the different complicated components of the process, and created a solution.
“And so, we started Zip and created the category of intake and procurement orchestration. We’re very grateful to have been named the leader in the category,” says Zaparde, in reference to having just been named a category leader in IDC’s first ever Marketscape for Spend Orchestration.
So, as is often the case, procurement is something Zaparde fell into. In this case, he got involved with procurement specifically to solve pain points. Prior to Zip, he was a Product Manager and Cheng was an Engineering Leader, both at Airbnb; they knew very little about procurement. “We were just end-users,” he explains. The upside of this was that they were able to come into the industry fresh, without the baggage and legacy issues that can come with being in a sector for a long time.
UX first
“At Zip, we really try to take a user experience first approach,” Zaparde continues. “What we found is the highest leverage change you can make in any procurement organisation is to make it easier for your employees to actually adopt and follow whatever the right process is. If you do that, then all of finance, procurement, accounting, and even IT find that they’re suddenly swimming with the current, not against it. And you can’t do any of that unless you solve for user experience.”
Taking away problems, the way Zip does, also takes away a barrier to ambition. The theme of DPW Amsterdam 2024 was 10X, a term on the lips of many across all sectors. Once immediate issues and pain points are addressed, 10X is something businesses can aspire to, with many talks and workshops during DPW Amsterdam focusing on how to approach this.
Getting the mindset right
For Zaparde, 10X thinking is a necessity for growth. “You have to aim for 10X to even end up at something X,” he explains. “That requires ambition. I also think that when you think in terms of 10X, and your mindset is angled towards incremental change, you’re much more open to thinking of solutions that are perhaps a little more risky. It changes your perspective.”
A mindset shift needs to happen before anything else. This involves considering the needs of procurement and the wider company, having a north star in mind, and then breaking changes down to an incremental level.
“Then you can start to think about the steps you need to take to get there,” Zaparde explains. “A big component of this is bringing along your peers and stakeholders across every function that’s tangential and critical to the core procurement workflow and path.”
Innovating for good
The work Zip does is indicative of the shift towards continuous improvement and advanced technology that procurement has been going through in recent years. There are things that are possible now that weren’t possible even a year ago, thanks to the vast innovations being made. One of the hot topics right now is generative AI, something that’s opening up a world of possibilities.
“It’s the elephant in the room right now,” says Zaparde. “With the capabilities that gen AI unlocks, you can automate a lot more. That allows you to cut down a lot of the transactional and operational work that procurement and sourcing organisations are doing. Procurement is tired of the status quo. It’s been an underserved function for over 20 years, and I’m glad that’s finally changing. I feel privileged for myself and Zip to be part of the conversation, and that we’re seeing all these amazing changes happening.”
Zaparde believes we’re already seeing the benefits of the major changes that have occurred over the last couple of years in procurement. In fact, he knows this, because Zip has helped its customers save around $4.5bn of spend over the last two years, which is an astonishing statistic.
“One customer of ours, Snowflake, achieved over $300m in savings alone,” Zaparde continues. “We’ve seen tangible benefits already. The way procurement is evolving isn’t a hypothetical thing – it’s really happening.”
Fragmentation on fragmentation
The key, again, is overcoming base level issues for the sake of evolution. This is precisely what Zip provides, after all. But sometimes, the issue is at a data level. Unclean data is something that technology leaders are talking about a great deal right now, with some feeling that it holds them back from implementing new technology. Zaparde believes that businesses should be questioning why their data isn’t clean from the start, rather than worrying about trying to cleanse existing data.
“You don’t just clean your data – the real question is why is your data not clean in the first place?” he muses. “You have to have a clean entry point for it. I don’t think I’ve ever spoken to a Fortune 500 CPO that said they had clean data. I think it’s because of the upstream processes in intake and orchestration. If all the cross-functional teams – the IT review, the legal review, the finance – are being manually shepherded by the procurement operations organisation, then how can you possibly end up with clean data?
“People are keying the same information into multiple systems, which might mean they answer in similar – but different – ways. So you end up with fragmentation on fragmentation. But if you have one single door to that data, you’ll be able to drive only clean data, because it’s a funnel. If you let everyone have different swim lanes that never intersect, you won’t have clean data.”
As 2025 approaches, Zip has multiple product capabilities and features coming up that Zaparde and his team are very excited about. This includes leveraging gen AI, something we’re seeing incredible utilisation of across the sector.
For Zaparde, attending events like DPW Amsterdam to talk about what Zip does and interact with peers and clients alike is a joyous part of his job. “DPW is really accelerating the rate of change in the procurement industry. That’s very much needed, and it’s energising to see so many incredible people from the procurement world in one place. I love spending time with these forward-thinking procurement leaders at this event.”
Catching up with Mitha-Ai’s Co-Founder, Arash Saberi, we dive into the vital importance of a solid data foundation.
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Whether we’re talking about gen AI, 10X, or any other kind of advanced tech solution, data is at the core of the discussion. And when data isn’t clean or ready for the implementation of something being built on top of it, businesses can end up significantly held back. Mithra-Ai is an organisation that helps its customers to build trust in their data, which is a core issue for many.
“That sets us apart,” says Arash Saberi, Co-Founder of Mithra-AI. “We help procurement leaders and category managers create, execute, and realise their strategies. This is backed by reliable, comprehensive data, both internal and external, tailored specifically for their categories.
“Maintaining high-quality data is crucial as it influences the accuracy and reliability of AI-driven insights and recommendations. That’s where Mitha-AI comes in. Our cleansing, enrichment, and auto-classification engines ensure that procurement stakeholders, including data scientists, begin with a reliable data foundation.”
Cleaning and classifying data
Mithra-Ai is an AI-native SaaS solution, which starts off by proposing a meaningful spend hierarchy for every category. What’s key is that this is paired with an automated cleansing and classification engine. This is so important because the only way to achieve truly clean data is to make sure it enters the system clean in the first place.
“Clear visibility into categorised spending eliminates uncategorised expenses and wrong assumptions,” says Saberi. “When supplemented by relevant external data intelligence, category managers are empowered to negotiate with confidence, achieve greater savings, and monitor initiatives effectively.”
A world beyond cost savings
When launching Mithra-Ai in 2021, the company’s founders rightly foresaw that the role of procurement would evolve beyond focusing merely on cost savings, and become the central hub of every organisation. Because of that, they knew that accurate, reliable information was needed – hence the necessity for Mithra-Ai.
As procurement has shifted, the status quo is no longer good enough. It’s an exciting time for the sector, but also one of high demand in the race to adopt increasingly advanced technology. But it’s necessary for efficiency and growth.
“Tesla and Nvidia exemplify the power of embracing change over maintaining that status quo,” says Saberi. “Procurement is facing intense pressure to evolve with organisational needs. Those organisations can opt for incremental changes, which will likely slow them down, or pursue a 10X leap to maintain competitive advantage. The latter requires bold and decisive leadership from heads of procurement.”
The road to 10X thinking
The way to drive 10X thinking, Saberi believes, is through having a clear vision of your goals. Sometimes businesses, especially ones which are going through major change or those navigating outdated legacy systems, are at risk of losing sight of their goals. But having that vision is a foundational necessity, regardless of what stage you’re at.
“Set aspirations high, and question existing norms,” says Saberi. “Procurement leaders can draw inspiration from startups by fostering a culture of innovation through small-scale initiatives that can rapidly expand. Reevaluate the skills and team structure necessary for future success.”
Another important aspect to bear in mind when considering these things is the level of risk you’re willing to undertake when setting goals and aspirations. “That’s often overlooked,” Saberi continues. “Determining the acceptable level of risk is crucial. It significantly influences partner selection and the outcome of RFPs.”
Thinking big, starting small
While ambition is vital to 10X thinking and beyond, businesses must also make sure they don’t bite off more than they can chew. Launching into adopting huge volumes of advanced technology can lead to overwhelm and can make a business stall rather than evolving. A more careful approach is required.
“Think big, start small,” says Saberi. “Prioritise high-impact, low-effort initiatives over those requiring significant effort. Many transformation projects fail to deliver the expected benefits and incur high costs during the program.” This is another reason to decide on the appropriate risk level early on, in order to guide prioritisation decisions and transformation pace.
It’s an incredibly exciting time for procurement, and that includes Mithra-Ai. In a very short time, it’s developed several foundational modules for its data-driven category management solution. This includes the Collaborative Initiative Tracker that was launched during DPW Amsterdam 2024 – just one of Mithra-Ai’s inspiring undertakings as we approach 2025.
“The tracker means that procurement teams can now involve multiple stakeholders in collaboratively tracking and enhancing the impact of key initiatives, such as cost-saving measures,” says Saberi. “Exciting times lie ahead.”
DPW Amsterdam is the perfect stage for launching a solution like this. It’s an event that inspires a culture of innovation, bringing procurement professionals together to teach, learn, and shout about their latest additions to the procurement landscape.
“DPW stands out as the premier procurement tech event of the year,” says Saberi. “Practitioners can explore and engage with procuretech suppliers, showcasing valuable use cases and personal stories across multiple stages. DPW is a catalyst for ideation, creating trust and confidence in the benefits of applying cutting-edge technologies to improve business outcomes. This year’s event felt even more international than previous years. I look forward to seeing it continue to grow.”
Saberi’s main takeaway from DPW Amsterdam this year is that a solid data foundation is essential – something he was well aware of as part of Mithra-Ai. “Without it, transformation projects and new technologies will struggle to succeed,” he concludes. “In the past two years, there has been increased focus on sustainability and risk intelligence, driven by numerous new solution providers. However, during the DPW Amsterdam 2024 conference, we observed new trends coming up and, again, more focus on data quality, which works to our advantage.”
When we’re talking about technology in procurement, the importance of partnership is a major component for success. No business is…
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When we’re talking about technology in procurement, the importance of partnership is a major component for success. No business is an island, and joining forces with experts is, increasingly, the direction many move in for the sake of growth.
At DPW Amsterdam 2024, we met many businesses who were looking around at the procurement sector in search of either what direction to move in next, or who they can help. The event is one that brings people together to learn, to teach, to discover the cutting edge of procurement, and be inspired by it. So when we sat down with the CEO of Fairmarkit, Kevin Frechette, it wasn’t surprising that he brought Nick Wright, who leads bp’s Procurement Digital Garage, into the conversation.
For Frechette, one of the best things about working in the advanced procurement technology sphere is joining forces with other businesses to help them keep improving, and vice versa. “Having the chance to work with people like Nick, who are pushing the envelope when it comes to autonomous sourcing, is amazing,” he explains. “We’re fired up to be at DPW, absorbing this atmosphere.”
While it’s something of a running joke in the procurement world that most professionals in the sector don’t deliberately choose it, Wright actually did. “I went to university and thought ‘wow, I fancy a career in procurement or vendor management’. I know a lot of people don’t have that story, but I’ve been doing something I’m passionate about from the beginning. I love making deals, whether I’m buying a car, a house, or something for BP.” The Procurement Digital Garage he leads exists to look at problems being faced across procurement, and figuring out possible solutions.
For Frechette, the intention wasn’t to start a company in the procurement space, but his team quickly saw the opportunities within it. “We had this ‘aha’ moment,” he says. “It was a tough pivot. There was a lot of debate, a lot of late nights. I’m super glad we made it because we got to be in a space where people can be forgotten about, and we’re able to give them centre stage.”
The realistic approach to 10X
DPW itself exists to put procurement under the limelight. Each event is themed in a way that gets conversations flowing around the next big thing in procurement. For Amsterdam 2024, this theme was 10X – something Frechette believes isn’t achievable right off the bat.
“It’s something to strive towards,” he says. “It’s something where you work on getting a little better every single month, every quarter. You keep getting those small wins, and you build credibility. There’s no silver bullet. You just have to start the journey and learn as you go.”
For Wright, it’s about not getting caught up in the hype, but figuring out what’s realistic. “There’s a lot of hype out there, and the beauty of something like my team at the Procurement Digital Garage is to weed out that hype, because what’s right for us might not be right for someone else. Having a team that’s out there in the market, testing and figuring out what’s real, will put you in good stead.”
“There’s a leap of faith element that can be challenging to achieve, before you can really strive for 10X,” Frechette adds. “It’s like Amara’s Law: humans typically overestimate the value of technology in the short term, but underestimate it in the long term. So the hype is needed. We have to help people on that journey and sometimes, a leap of faith is needed. For the people that risk it, it’s exciting, and they’re then well positioned for the future.”
However, again, managing expectations is important. “People might be on the sidelines expecting a 10X solution,” says Wright. “But the reality is, you’re going to get 5% here, 10% – smaller pockets of improvement.”
The benefits of advanced technology are absolutely being seen at this stage, but being realistic about the future outcomes is important. “The benefits are there – not at the scale of 10X – but if you just make a start, you’ll achieve wins,” says Frechette. “You broadcast those wins across the organisation. That generates excitement, and then you can work on the next thing because you have ground swell.”
How ‘the future’ has changed
What’s interesting is that this 10X focus, this drive towards incremental wins, has reframed the way businesses plan for the road ahead. ‘The future’ used to mean having a three or five-year plan. Now, the future is only 12 months away.
“The thought process right now is ‘what can we do that’s super optimistic in just 12 months’?” says Frechette. “Then you can put in realistic time frames and set off on a sprint to get there. You have to be able to move fast. We have launches every two weeks now, and we have to be flexible with our roadmap along the way. But we always know where we’re going – we have a north star.”
“To me, that’s the only way to do it,” Wright adds. “I don’t have a crystal ball. Nobody knows what’s going to happen in two or three years. So what’s the point of creating a plan that’s going to get you to a certain point in those two or three years? You have to work on small iterations, make adjustments, change direction as necessary.”
It’s part of what makes Fairmarkit and BP an active partnership – the ability to be flexible and open up discussions at every point. It’s all about real-time feedback and trust-building, to the extent that both parties feel like they’re on the same team.
The right people in the right places
Because ultimately, it’s the human element that makes transformation happen. Having the right people in place is one of the elements that’s key to making sure implementing advanced tech for the sake of business strategy works at all. “It’s about access to talent and making sure you’ve got a capable user group that can make the most of that technology,” says Wright. “You don’t need to be a data scientist, but you do need to have the right mindset to take advantage of the tools you’ve got.”
“I agree – you have to get the right people on the bus,” adds Frechette. “You all have to be committed to going on the journey together. Prioritise where you start and where you’re going to have the most value with the lowest risk, and have people on your side who can give suggestions and ideas.”
While the much-discussed talent shortage can create challenges there, DPW as an entity proves that not only does procurement keep becoming more appealing and exciting, but where there are gaps, there are digital tools. “I’ve noticed a lot of folks under 30 who are here at DPW Amsterdam, and they’re genuinely interested in procurement,” says Wright. “We’re at a tipping point that makes me really excited about the profession I’m in.”
‘Digitalisation is just the beginning’ according to Crowdfox, a business which aims to improve procurement by bettering the ordering process…
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‘Digitalisation is just the beginning’ according to Crowdfox, a business which aims to improve procurement by bettering the ordering process while lowering costs. That tagline speaks to Crowdfox’s dedication to advancing procurement using the exciting tools the sector now has at its disposal, and this push to innovate is being driven, in part, by Martin Rademacher, Crowdfox’s CSO. We sat down with Rademacher at DPW Amsterdam 2024, the exciting vibe of the event spreading far and wide around us.
Rademacher is responsible for everything to do with Crowdfox’s customers. From sales, to marketing, to customer onboarding and success, and everything in between – that’s Rademacher’s wheelhouse. His background is in management consulting, with a focus on procurement and supply chain. So, while he started out in sales, he soon decided that procurement was the direction to move in.
“During my time as a consultant, I found procurement very interesting because it’s so versatile,” explains Rademacher. “Of course, it’s about the transactional phase with suppliers – but also you’re so connected with R&D, production, logistics, and so on. You have so many fields of application.”
10X thinking
At DPW Amsterdam, the overall theme of the two-day event was 10X. The concept of the 10X rule is around taking a goal you’ve set for yourself and multiplying it by 10. It’s an aspirational tool, coaxing all of us to aim higher. In procurement, that means innovating.
“In the last two years we’ve seen tools like ChatGPT trigger some big adaptations in the procurement world,” says Rademacher. “I think there is the opportunity now to achieve 10X in terms of efficiency gains. Especially when it comes to making better decisions, more quickly, in order to analyse data. We’re now finding out what AI can really do, and focusing on how that can help with strategy.”
For Rademacher, he believes people have the right tools to achieve 10X – it’s now about implementing those tools properly, and having the right culture.
“In the last couple of years, implementing tools has become much easier than it was a decade ago,” Rademacher continues. “They’re so well designed that they fit into large procurement systems, and can connect with other best-of-breed tools. I’d say implementation should be the focus, but it’s not that complicated anymore. AI tools especially are really intuitive. As a result, you don’t need much in the way of change management. People just intuitively cooperate with AI.”
The question of security
The big challenge, Rademacher believes, is data protection. When it comes to barriers preventing a 10X approach, concerns around data privacy are among the biggest issues. As a result, organisations have to take the necessary precautions before plunging into making major technological changes, or risk falling at the first hurdle.
“In the EU, it’s all about data protection,” says Rademacher. These concerns led to the Artificial Intelligence Act (AI Act) coming into force in the EU in August 2024. It was created in response to the rise in generative AI systems, and ensures that there’s a common regulatory framework for AI within the European Union. “Companies are very concerned about their data, but I wouldn’t call this an obstacle – more like a challenge.
“The key is making sure you have a protected environment. Start with a pilot in a limited space, for instance, and then make sure you can find a solution you can control in a safe environment that suits your operations.”
Shooting for the stars
With these measures in mind, it’s never been easier to implement new technologies and aim for that ambitious 10X goal. Certainly, advanced tools have never been more accessible, or more straightforward for businesses to educate themselves about. Even as recently as two years ago, integrating multiple elements of advanced tech – like genAI – wasn’t really possible.
“It definitely wasn’t easy to combine sources the way we can now,” says Rademacher. “Now, you can provide a much better user experience experience not only for procurement professionals, but for anyone who takes advantage of what procurement introduces to the company. Finding the supply to fulfil your demand is so much easier now. You no longer have to have difficult conversations starting with an email to your procurement professional to identify whether you’re allowed to purchase from a certain vendor, and whether they’re vetted or not. Streamlining processes like that makes that information quick and easy to identify.”
Additionally, we’re at a point with advanced technology where the tools we have access to are capable of handling more and more volumes of data at an extremely fast pace. “In consulting, for example, every project started with an analysis of the status quo of a firm,” says Rademacher. “We’d figure out who the vendors are, the categories, and the spend. Depending on the workforce, this could take one or two weeks. Now, with the tools we have access to, you can gather this information in 24 hours.”
The evolution continues
While we’re seeing many of the benefits that come with genAI and other advanced technologies already, it’s only the beginning of what we can achieve using these tools. GenAI is at a peak right now, but according to Rademacher, it might take another five years to achieve its full productivity level. “There’s also this ambitious idea going around of fully autonomous procurement, and it’ll likely take a good 10 years to reach that level of productivity,” he adds. “On the other hand, nobody is talking about robotic process automation anymore because we’re almost there with that already.”
Another challenge is data quality. The cleanliness of an organisation’s data can make or break its use of advanced technology, which is where making the right connections with service providers comes in. “It’s a good example of when to find the right partner,” says Rademacher. “Find someone from the innovative tech space who you think you can rely on. Don’t try to do it all on your own – that’ll just hold you back more and more. Be bold; find the right partner to make the most of your data and that helps you constantly improve. There’s a lot of talent out there, a lot of solutions that are really helpful for organisations of all sizes. You’ll improve step by step.”
There’s no doubt that it’s an exciting time for procurement. The atmosphere at DPW Amsterdam 2024 was electric for that exact reason. The event, in Rademacher’s words, has “a really strong influence on the sector and enables attendees to learn about how the landscape is developing in real time”.
“The AI-driven future is already a reality for us,” he states. “We’re beyond the pilot phase with our AI tool, ChatCFX, and now we really want to drive market share. 2024 going into 2025 sees us in a good position with high user visibility, and now we’re adding ChatCFX to the game, pushing it into the European market. We’re at DPW Amsterdam to meet the players who are looking for a solution exactly like ours, making it an invaluable place to be.”
Certain procurement pain points can prove debilitating for a business, freezing it in its tracks when it’s trying to grow…
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Certain procurement pain points can prove debilitating for a business, freezing it in its tracks when it’s trying to grow and improve. This is where companies like Candex are able to step in and turn a headache into something so simple, it requires no further thought.
Danielle McQuiston is the Chief Customer Officer at Candex. She’s been with the fintech startup for five years, spending two decades prior to that working in procurement at Sanofi. Candex is a technology-based master vendor that allows customers to engage with and pay one-off or small suppliers without setting them up in their system. This means that the system doesn’t get clogged up with suppliers that are rarely or never going to be used again.
“We’re primarily used for what companies consider tail spend, and we typically deliver it as a punchout catalogue for a really simple user experience,” McQuiston explains. That ability to support lots of customers was what drew her to the role. “Coming to Candex, I was very excited about what they were doing and wanted to help as many companies as possible.”
Addressing tail spend
That ability to address tail spend in a unique way is the main thing that differentiates Candex. It’s an enormous problem for procurement professionals. The way Candex delivers it is through a digital plug-and-play solution, removing the need to be dependent on human intervention. “It’s a horizontal solution for any good or service, and it’s available in over 45 countries now,” says McQuiston. “It becomes part of the customer’s ecosystems and leverages the P2P process. It’s super compliant, and allows a lot of control.”
With this tool in place, Candex’s customers are able to gain much better control over their smaller purchases, defining what is allowed to be purchased. For many, this tool allows them to put tighter restrictions on purchases than their e-procurement systems are able to do. Additionally, Candex runs suppliers through screenings every day, which generally doesn’t happen for small, rarely-used suppliers.
“We run really detailed compliance and sanction screening against all those vendors, taking away a really daunting task from customers,” McQuiston states. “Customers probably check those suppliers once when they’re being set up, but then they never look at them again. Every day, we’re checking them, and keeping an eye on them when our customers can’t.”
Candex’s reporting is extremely detailed, and provides customers with the kind of real-time visibility they wouldn’t normally get – even in their own systems. Reports are generated weekly or monthly, including the diversity status of suppliers. This is data that a lot of clients then feed directly into their Power BI tools and data lakes, meaning they’re able to integrate it seamlessly into their other data.
Cleaning up the data
The whole purpose and aim of Candex’s tool is to make life easier for its customers, streamline its processes, and improve efficiencies. To that end, standardisation is key when it comes to business improvements, and that includes preparing data prior to implementing new technologies and processes. When it comes to ensuring a business’s data is healthy – before launching into major tech changes – accepting the necessity of making foundational change is key.
“Data cleansing processes are ugly, cumbersome, and long – and everyone has to do them,” McQuiston comments. “But you have to accept that you’re going to have to do something, if you want to get a handle on your spend. First and foremost, you need to standardise the way you name things, the way you put data in the system, and you need a really strict discipline around that. All of those things will make backend processes a lot easier.”
It’s just one of many considerations CPOs need to bear in mind when seeking out technology solutions and implementation. Modern procurement departments have a seat at the wider business table now, and what they do impacts the entire business. So when it comes to utilising solutions for the sake of the business at large, there are many factors to think about.
“As with any data or technology, it’s all about garbage in and garbage out,” says McQuiston. “Any advanced technology should be used with caution and viewed with a critical eye. You have to start with knowing what you want out of it.
“A lot of times, people put technology in place because it looks interesting, but you need to start with the problem and work backwards. If the issue is user experience, you need to make sure that whatever you’re implementing focuses on a positive UX. If the problem is unclean data, you need to make sure you’re putting in place all the foundational elements you need to make that better. Always start from the perspective of implementing a technology based on a problem, rather than the other way around.”
Improving UX in 2025
It’s a seriously dynamic time to be involved in procurement right now, as evidenced by the intense buzz around us at DPW Amsterdam as we sit with McQuiston. As we look ahead, she envisions that procurement will have an increasingly powerful impact on user experience. This is particularly important at a time when tasks are becoming increasingly automated, with less and less direct human interaction.
“We’re also seeing a pretty big leap forward in terms of best practice sharing amongst our clients,” says McQuiston, something that events like DPW also encourage. “For Candex, a big theme of 2024 has been getting our clients together to share best practices and information, helping them to develop further expertise in the field. 2025 will have more of the same, but there’s now a higher level of maturity out there in the way customers are considering tail spend. As people continue to onboard solutions, it will be interesting to see how that impacts the UX in relation to Candex. We’re always looking for ways to make our tool more user-friendly and add better functionality.”
All of this is why Candex’s customers love the company. On a base level, Candex takes a complex pain point and makes it simple. In a broader sense, the reason Candex is becoming so popular is the way it works with people. “The most common feedback we get from customers and suppliers is that we’re great to work with because we’re so flexible,” says McQuiston. “We hired a team of procurement experts, so our team is made up of people who really understand the pain of our clients, and can anticipate their fears, their needs, and cater to those.”
The buzz of DPW Amsterdam draws in the most innovative minds across the industry. They’re there to have riveting conversations…
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The buzz of DPW Amsterdam draws in the most innovative minds across the industry. They’re there to have riveting conversations with their peers, to inspire, to teach and learn in kind. And they’re there to keep an eye on an industry that doesn’t stop changing for the better.
This is a big part of the appeal for Fraser Woodhouse. Woodhouse leads the digital procurement team within Deloitte in the UK. His team historically focused on large-scale transformations, providing a backbone for suite implementation. Increasingly, however, it’s turning its attention to helping clients navigate a plethora of technology solutions. The goal is to help them build and scale, and take advantage of some of the more niche functionalities available. These are things that can be highly daunting for many customers, which is why Deloitte is there for support.
“We’re helping clients ask the big questions,” Woodhouse explains as he sits down with us at DPW Amsterdam 2024. “How do you connect the technology in a way that allows data to flow from one system to another? How do you deal with processes that are connected to solutions which all have their own release cycles? How do you approach change management? That underpins so much of where the value is going to be achieved, and a lot of the providers will be focusing on it. They just might not have the same capability that Deloitte can provide.”
For Woodhouse, getting involved with procurement was a total accident. He even left the sector at one point, but his strong foundational knowledge – and the exciting landscape procurement is enjoying right now – lured him back in. “It changes faster than I can get bored with it, that’s for sure,” he explains. “Procurement is fascinating.”
Aspiring to greatness
Especially now, with constant conversations around genAI, 10X, and beyond. Procurement is only becoming more interesting, more enticing, drawing young professionals in to fill gaps in the talent pool. 10X was actually the theme of DPW Amsterdam this year, a notion that’s on everyone’s lips. And for Woodhouse, it’s absolutely something to aspire to.
“Aiming for 10X is sensible. You just have to consider your timescale. I’d caution against running before you can walk, but a culture of experimentation is important. Running small-scale pilots can help you hone in on where you really want to see value, or where value is likely to be generated. Starting with requirements is a fundamental thing at the moment, but you shouldn’t underestimate how long that will take. And it’s a continuous consideration, because requirements change. Just keep trying to refine your solution in order to take advantage of everything that’s out there right now.”
Fotograaf: MichielTon.com
Having the wrong mindset is one of the major barriers to adopting 10X thinking. It all starts with the company’s culture, and whether that’s one of growth or not. “I imagine most of the people here at DPW Amsterdam have already made that mental shift,” says Woodhouse. “Last year, people were still trying to understand how they, as big companies, could utilise startups. That’s changed now, and it’s amazing to see companies that were startups three years ago working with all these big enterprise customers.
“They have scaled and grown in partnership with those customers. Mindset is so important, and having the wrong one will only create barriers and missed opportunities.”
Always improving, never slowing down
When it comes to the advantages that technology has brought to procurement in the last few years, the list is endless. Procurement has gone from an overlooked segment of any given organisation, to having a seat at the table and helping make major business decisions. 10X thinking – whether it goes by that name or not – has been spreading across the segment and fuelling businesses to aim higher.
“The layers of automation have really improved,” says Woodhouse. “A year or so back, there were a handful of use cases that you could truly automate, but now you can do it at a much larger scale. Another big change is around security concerns. There are more tried and tested case studies to draw upon now, and solutions are more readily available. You don’t necessarily have to be a pioneer, because someone else has already taken that first step.”
The question of data
Something else that holds businesses back, despite the innovation at their disposal, is an element that can be harder to change: poor quality data. When trying to implement advanced technology solutions, bad data can make or break their success.
“It’s always useful to focus on that and have a dedicated work stream,” Woodhouse advises. “You need someone who really understands data. I think there’s a tendency to try to boil the ocean before you even get going in your transformation, which isn’t necessarily a bad thing. Cleaning up your data before you start, and having a fresh foundation will help you make decisions on what to implement on top of that good data.
“Doing all of that is obviously hugely beneficial, but it’s going to slow you down, in many cases. There are ways around that, like embedding the cleanup of data within the new processes. Data is important – we shouldn’t underestimate that – but there are different approaches to solving the issue of poor quality data, like buying it or using genAI to restructure your data into something more powerful. Either way, you need a strategy.”
Novel thinking 101
Some businesses fall into the trap of thinking that they can’t achieve specific things because their data isn’t in the right position, but novel thinking around data can allow them to still drive forward. “You’ve just got to focus on it. You can’t assume the data’s going to fix itself,” Woodhouse adds.
Novel thinking is certainly something that can be seen at DPW events, and DPW Amsterdam 2024 was no exception. People congregated there to learn, to share stories, to inspire. For Woodhouse, the magic of the digital procurement sector right now is that everybody recognises that their journey has no end. While that may be daunting, it’s a positive thing and keeps procurement professionals striving for more.
“It’s a continuous improvement journey, and I think the best-performing organisations will recognise that, and invest in the business capability to continue that journey,” Woodhouse concludes. “That’s how you get proper value. I love hearing about how people frame problems differently, and how they approach the solutions.”
Making procurement slicker, more streamlined, is the name of the game right now – and this is precisely why Globality…
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Making procurement slicker, more streamlined, is the name of the game right now – and this is precisely why Globality exists. It’s an organisation which leverages advanced, native-built AI to make sourcing more autonomous for Fortune 500 and Global 2000 companies, meaning it has a finger on a pulse of the technology tools procurement now has access to as the industry shifts and evolves.
Keith Hausmann is the Chief Customer Officer at Globality. He has been working in procurement since the early 90s, both in industry as a service provider, and now, at a technology company. He came to Globality from Accenture, where he ran the operations business. During his first real job after college, Hausmann was also part of a training program at a major Fortune 500 company, working closely with a COO. At some point they got into a conversation about salespeople seemingly having an advantage over procurement people due to their access to information, knowledge, and training. The COO suggested that they launch a company to help support procurement. For Hausmann, it was a serendipitous entry to the industry.
“I came to Globality because I saw the business was struggling with how to scale, automate, and deliver a differentiated user experience. Ultimately, I found it really compelling, and joined about five years ago.”
Achieving 10X thinking
Hausmann admits that the concept of what procurement is has only been defined relatively recently, and he’s been in the industry long enough to have seen the shift happen and suddenly accelerate over the last few years. Now, procurement professionals are in a position where they’re able to think big, and they have the tools to support that way of thinking. One of the most-discussed topics right now is 10X, whereby businesses are setting targets for themselves that are 10 times greater than what they can realistically achieve.
“There continues to be, and always has been, so many mind-numbing manual activities that go on in procurement spaces,” says Hausmann. “We’ve built small armies of teams to handle those things. I think 10X has prompted us to take a step back and ask if there’s now technology that can uplift the role of people in the function and take on some of those automatable tasks. Whether that’s writing RFPs, discovering suppliers, or analysing proposals – these are all things that can be automated in today’s technological world. With 10X thinking, you can imagine the many, many, many things that can be automated and just go after them.
“There are barriers, of course. The biggest one is not being able to convey a compelling vision of what we want people to do in the new world. It’s not necessarily about making them go away – it’s about making their daily jobs, lives, and work more valuable. There are so many things around category thinking and strategy that don’t get done because people are spending so much time on tasks that could be automated. So I think the barrier is creating that vision and that plan to shift the operating models, roles, and the skill sets to something new and different.”
People power
Hausmann believes that if roles are reshaped and honed in response to automation, it’s less likely that there will be resistance to change because employees will know exactly what they’re doing, rather than being concerned about their future. “They have to know what they’re doing before they jump on board. It just requires a mindset change and good change management.”
Hausmann believes it’s down to the CPO to drive that change management by conveying the activities, impacts, roles, and operating model they envision. If they can paint a picture of how humans can impact things in a new way, alongside the new technology rather than against it, suddenly it’s an exciting prospect and people are keen to make a bigger impact.
CFOs and CPOs joining forces
While CPOs now have a long-deserved seat at the table to help push change business-wide, CFOs’ roles are also expanding and having an increased impact on procurement. “I think they’ve always influenced what’s going on in procurement,” says Hausmann. “CFOs are the champions of many things, but certainly improving the bottom line of the company. They’re also champions of using technology to make the organisation more resilient, more scalable, and more efficient. There was a time when people thought that the CTO or CIO would be doing that, but more often than not, the CFO is the ultimate owner of improving business impacts. More and more, we’re seeing our customers leaning on the CFO to help them make decisions about investments that have a big impact through technology and AI.
“These days, the relationship between the CFO and CPO is wildly different to what it once was, and CFOs are showing more interest in procurement as a function than ever, making a difference to the bottom line. It makes sense because, in theory, procurement controls one of the biggest cost line items in a company, besides raw headcount.”
Matching the pace of technology
The fact that we still need to focus on change management and relationships confirms that the way procurement is changing isn’t just about the technology. Far from it. However, technology is moving at an incredible pace and needs to be taken seriously. There are things that are possible now which couldn’t be done even one or two years ago.
“A few years ago, technology couldn’t write an RFX document for you,” Hausmann says. “Technology could not instantaneously bring to light the most relevant suppliers from within a customer’s supply base, or in the broader market. It couldn’t write a contract, or an SOW, or a work order. It can now. Those are things that are near and dear to my heart that were impossible 3-5 years ago.”
With these tools in mind, procurement professionals are able to think about the future in short-term stints. Five-year plans are no longer good enough when it comes to the way procurement is shifting – a year is now the maximum for putting plans in place.
“I’ve always thought that procurement, from the perspective of technological advancement and investment perspective, should sit under a broader business umbrella,” says Hausmann. “I’d guess that probably 50% of companies in the world right now have some kind of program in place to save money or improve agility by investing in technology. And speed to market is more important than ever, so sourcing can’t be a bottleneck.”
Looking ahead, Hausmann expects to see many of the unique, differentiated technology providers becoming interoperable together, because big enterprises want services that operate and scale well in combination with others.
“We’re seeing that a lot, and working with our customers on how we improve interoperability and integration,” he says. “Tools will become more seamless, more easy-to-use, more scalable. Another big thing is, and will continue to be, analytics. It’s a hot topic in procurement, and I think there are profound opportunities to be deployed. For Globality, we’ll continue to endlessly innovate on user experience, ease of use, and beyond.”
“I’m overwhelmed,” are Matthias Gutzmann’s first words when asked about DPW Amsterdam 2024. At the end of the bustling two-day…
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“I’m overwhelmed,” are Matthias Gutzmann’s first words when asked about DPW Amsterdam 2024. At the end of the bustling two-day event, we sat down with Gutzmann, the company’s founder, and Herman Knevel, DPW’s CEO, for a debrief. Gutzmann also quite rightly pointed out that the final word on summarising those 48 hours is in the hands of the sponsors and attendees, but if the countless conversations we had with said sponsors and attendees are anything to go by, it was the best DPW event yet. And Gutzmann and Knevel agree.
“I really think that’s the case,” says Gutzmann. “We almost doubled the number of exhibiting startups, we had over 120 sponsors, more startup pitches than ever, and all the feedback I’ve heard so far has been amazing. There are always things you can do better, but I’m absolutely happy.”
Across the 9th and 10th of October, DPW Amsterdam welcomed over 1,300 attendees through its doors at Beurs van Berlage, Amsterdam. Those attendees arrived from 44 countries across 32 industries, and the event itself featured 72 sessions with 140 speakers across five stages. It’s abundantly clear that people are deeply passionate about DPW.
“On day one, it was already packed at 8:30 in the morning,” Knevel states. “The energy in the room was contagious, and the numbers speak for themselves. The startups, the innovators, the corporates, the mid-market – everybody who’s here has a genuine interest in what these guys are bringing to the procurement space.”
Reconnecting with the vision
Gutzmann describes that intangible energy as “bringing a little bit of joy back to procurement”. For many years, procurement was a very ill-defined concept – almost as ill-defined as the role of CPO. The shift has been a quick one, accelerated further by the COVID-19 pandemic, and events like DPW Amsterdam are part of the reason why. CPOs having somewhere to go, to meet, to learn about the procurement landscape is vital, hence that inspiring energy that permeates every DPW event.
“A lot of people are missing that vibe,” Gutzmann continues. “It’s why I founded DPW. I was inspired by Mark Perera [Chairman of DPW], who I worked with at Vizibl, and had great technology while also being so inspiring. I realised we needed to connect founders with CPOs. I think every CPO should talk to one startup founder per week, at least. It’s important that we listen to their vision.”
Striving for 10X
The core of those visions for the 2024 event revolves around the concept of 10X, the idea being that you set targets for your business that are 10 times greater than what you think you can realistically achieve. It keeps people ambitious, always striving for greatness, and it’s especially prevalent in startup culture – hence Gutzmann’s belief that CPOs should be connecting with them more.
“Deciding on 10X for this year’s theme was serendipity,” says Knevel. “The term came along and Matthias said, ‘this is it – this is what we need in procurement’. This is what the industry needs, and we’re exploring it, diving deeper.”
“Last year’s theme was ‘Make Tech Work’, which was all about getting the basics right in order to scale,” Gutzmann continues. “This year we said, ‘how can we take it further?’ We are entering the biggest wave of AI yet. That technology is giving us the opportunity and the possibility to scale outcomes. The world around us is changing so fast, so we need to be more agile, scalable, and faster in procurement. It’s a very ambitious, maybe lofty theme, but it’s a mindset more than anything else.”
“It’s the mindset that drives innovation and speed,” Knevel adds. “That’s really important in this age of procuretech and supply chain tech.”
When it comes to honing that 10X mindset, it’s all about having a purpose in mind. A lot of the procurement professionals we spoke to at DPW Amsterdam called this a ‘north star’, which is the phase Gutzmann uses too. “That’s where it starts. There’s so much procurement can do. There are so many problems in the world, and I believe procurement can be the solution to many of those. So I think it starts with the CPO and their leadership, their vision. You also have to embrace startup innovation, be more experimental in the way you work, instigate new ways of working, and be bold in your thinking. You also have to remember it’s okay to fail.”
Growing DPW
Something that’s particularly impressive about DPW Amsterdam 2024 is that it’s actually the second of the year. Back in June, DPW ventured into the North American market with an intimate summit held in New York City, which CPOstrategy was fortunate enough to be invited to. Planning one wildly popular event a year is one thing, but venturing into a whole new part of the world with an additional one is incredibly dedicated.
“I’m a bit more conservative when planning ahead, so there probably wouldn’t be a New York event without Herman encouraging me,” says Gutzmann. “I’m glad he said ‘let’s go for it’. It was a short-term plan, but it was ultimately very successful and the right decision.”
Knevel adds: “The feedback we got from sponsors and delegates was quite impressive. They were asking for more. And it’s not just Matthias and myself – we have a great team here. This is a massive production, but we made the jump and it’s paid off.”
Inspiration for 2025
When it comes to the lessons Gutzmann and Knevel have learned in response to this event, it’s more about narrowing down the influx of ideas DPW gives them. By the time we spoke with them at the end of the Amsterdam 2024 event, their heads were spinning with inspiration.
“I have so many ideas,” says Gutzmann. “Every year we reinvent the show, so we never rest. We’re always asking what we can do better. How can we improve? I think this year we maxed out the number of sponsor stands that are possible to have. We doubled the number of under-30 attendees. There’s the potential to go a little deeper on the talent side, connecting students with the corporates and building a proper program around that.”
There was also the Tech Safari this year. The idea was to make the expo hall easier to navigate, since it was more crowded than ever this year. Members of the DPW team acted as ‘super connectors’ to help attendees find the right solutions and help startups find new customers. The aim was to simply make it easier for everyone involved to find what they’re looking for in small groups,enabling them to find who they wanted, talk to them, and ask questions. It turned out to be an amazing interactive experience for people, making sure they felt thoroughly looked after and valued.
“Plus there’s an opportunity to cater more to the corporates coming in,” Gutzmann continues. “Perhaps we will build a custom program for them around the event. Some of them are already coming in with teams and doing annual leadership meetings outside of the venue, but I think there’s scope to show them solutions and do some workshops within the event. We can also do more with day zero, where we have site events. There’s much more we can do.”
Giving CPOs what they want
As for the broader future of the event, DPW’s heart lies in Amsterdam and will continue to do so. The organisation is building its team even further and putting strategies in place for future events, allowing it to move forward. “We follow the demand of what our customers want,” Knevel says. That’s what really drives DPW and how the event is themed and set up. The organisation listens to CPOs so it can give them exactly what they need, and what will help the industry level up further and further.
“There are things we’re still developing,” says Gutzmann. “For example, the podcast studio [something introduced in its current form for 2024] is something Herman is very passionate about, so it was great to test it out here. There’s more we can do with that. We have so many ideas and it’s important to engage our amazing team on these ideas and see what they think along the way.”
“We’re ideating a lot,” Knevel adds. “And we’re asking our ecosystem what we should do more of.”
“Ultimately, we’re bringing in the voice of the customer to make sure we’re giving them what they want and need,” Gutzmann concludes. “That’s the whole purpose of DPW.”
Combining advanced technology with a people-led focus is the name of the game for Bravo Consulting Group. Bravo was founded…
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Combining advanced technology with a people-led focus is the name of the game for Bravo Consulting Group. Bravo was founded in 2007 by President and CEO Gino Degregori. He had his sights squarely set on leveraging Microsoft technologies to deliver cloud services, application modernization, and cybersecurity compliance. Bravo’s aim is to simplify how organisations create, share, and secure their intelligent information. In nearly 17 years of its existence, the business has grown into a premier Microsoft solutions provider serving the federal government, the Department of Defense, the Intelligence Community, and multiple Fortune 500 organisations.
Human-centric leadership and core values
Degregori began his career in software engineering and entrepreneurship. However, he quickly realised that his true calling was beyond just developing software and implementing Microsoft technologies. “I saw an opportunity to build an amazing organisation that provides real value to our customers through our people and innovative solutions,” Degregori explains. “While the cloud didn’t exist in 2007, development, automation, and security were already crucial.”
Degregori founded Bravo on core values that remain the cornerstone of the company today. “Our vision is to attract and create kind leaders who make an impact on our customers, partners, and communities,” he explains. “We lead with empathy, embracing kind leadership. This means prioritising the growth and wellbeing of our team members and clients. We view every interaction from a win-win perspective with a strong sense of accountability.
“It’s not just about implementing technology in your organisation; it’s about truly advancing the mission. Collaborating with great people enables us to deliver outstanding results,” he emphasises. Degregori also hosts The Kind Leader Podcast where he discusses empathetic leadership with industry leaders, embodying the values Bravo champions.
By fostering a culture of empathy and innovation, Bravohas established itself as a leader in cloud services, application modernization, and cybersecurity. Degregori’s commitment to building a people-centric organisation ensures that Bravo not only meets but exceeds the expectations of its clients, driving meaningful and impactful results.
Strategic partnership with AvePoint
Bravo’s commitment to collaborating with exceptional partners has been the cornerstone of its longstanding relationship with AvePoint. For 15 out of its nearly 17 years of existence, Bravo has partnered with AvePoint—a testament to the enduring strength and value of this collaboration. When Bravo first started, the Microsoft ecosystem was rapidly evolving, with many businesses transitioning away from legacy systems. AvePoint’s advanced SharePoint migration and administration tools played a pivotal role in this transition, enabling Bravo to assist over 100,000 users across various verticals in successfully migrating and managing their content and data.
“Our partnership with AvePoint allowed us not only to migrate vast amounts of content and data efficiently but also to reduce costs, which we passed on to our customers,” says Degregori. “It was a phenomenal opportunity to leverage AvePoint’s tools for seamless content and data migration. We recognized early on that AvePoint was poised for significant success, and from then on, our collaboration deepened, enabling us to develop even better solutions.”
This partnership is a key reason customers choose Bravo. By integrating Bravo’s expertise in the Microsoft ecosystem with AvePoint’s suite of tools, Bravo delivers a unique value proposition centred on data management, compliance, and AI-driven solutions. Customers benefit from a holistic approach that not only prepares them for new technologies but also ensures regulatory compliance, cost efficiency, and superior results.
Together, Bravo and AvePoint empower organisations to confidently navigate their digital transformation. Leveraging Microsoft’s advancements in AI and AvePoint’s robust data management tools, they offer cutting-edge solutions that address the evolving needs of modern businesses. This collaboration enables organisations to optimise their data, maintain stringent compliance standards, and harness the power of AI to drive innovation and efficiency.
Expanding horizons through collaboration
For the first decade, Bravo focused exclusively on the federal sector. Recently, Degregori made the strategic decision to expand Bravo’s services into the commercial sphere. “Our strong partnership with AvePoint was instrumental in this successful expansion,” he says. “AvePoint is a global organisation, and through our collaboration, we developed a strategy to penetrate the commercial market. We leveraged our combined services, expertise, and certified professionals at Bravo to build trust and confidence with the AvePoint commercial folks.”
The unique relationship between Bravo and AvePoint has facilitated this long-standing and successful collaboration. Degregori attributes their success to three key factors: communication, clarity, and trust.
“First, strong communication ensures continuous understanding. Second, clarity about our collective goals – focusing not just on our objectives but also on AvePoint’s – allows us to align our efforts effectively. Lastly, trust is paramount. We need to rely on each other through both successful projects and challenging ones. This mutual trust ensures we can support each other through thick and thin,” Degregori explains.
“We are always learning. When things don’t go as planned, we sit down, discuss the lessons learned, and find ways to improve. This continuous learning and mutual support strengthen our partnership and drive our shared success.”
Future growth
The future of Bravo and AvePoint is exceptionally promising as technology evolves at an unprecedented pace. Both organisations are at the forefront, leveraging the Microsoft ecosystem. With Microsoft’s substantial investments in generative AI, their reach is set to expand even further into the Fortune 500 globally.
“This momentum allows us to continuously leverage advanced tools, integrating them to deliver unparalleled value to our customers,” says Degregori. This focus on the human element—the customer—ensures that Bravo remains true to its core values.
“I am immensely grateful for the opportunity to lead an incredible organisation like Bravo and to maintain a long-term partnership with AvePoint. Ultimately, while we discuss technology and solutions, it’s all about people. We’re constantly seeking ways to connect better as partners and employers. This human-centric approach is what drives us to deliver superior solutions.”
This vision and commitment to both technological excellence and human connection make Bravo and AvePoint’s partnership not only resilient but also highly impactful for their clients. Together, they are poised to lead the way in digital transformation, ensuring that organisations are not only equipped with the latest innovations but also supported by a team that values their success.
Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals…
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Our cover star, EY’s Global Chief Data Officer Marco Vernocchi, tells Interface why data is a “team sport” and reveals the transformation journey towards realising its potential for one of the world’s largest professional services organisations.
Welcome to the latest issue of Interface magazine!
Global Chief Data Officer, Marco Vernocchi, reflects on the data transformation journey at one of the world’s largest professional services networks.
“Data is pervasive, it’s everywhere and nowhere at the same time. It’s not a physical asset, but it’s a part of every business activity every day. I joined EY in 2019 as the first Global Chief Data Officer. Our vision was to recognise data as a strategic competitive asset for the organisation. Through the efforts of leadership and the Data Office team, we’ve elevated data from a commodity utility to an asset. Our formal data strategy defined with clarity the purpose, scope, goals and timeline of how we manage data across EY. Bringing data to the centre of what we do has created a competitive asset that is transforming the way we work.”
PivotalEdge Capital
Sid Ghatak, Founder & CEO of asset management firm PivotalEdge Capital, spoked to us about the pioneering use of “data-centric AI” for trading models capable of solving the problems of trust and cost.
“I’ve always advocated data-driven decision-making throughout my career,” says Ghatak. “I knew when I started an asset management firm that it needed to be data-centric AI from the very beginning. A few early missteps in my career taught me the importance of having a stable and reliable flow of data in production systems and that became a criterion.”
LSC Communications
Piotr Topor, Director of Information Security & Governance at LSC Communications, discusses tackling the cyber skills shortage, AI, and bringing together the business and IT to create a cyber-conscious culture at a global leader in print and digital media solutions.
Topor tells Interface: “The main challenge we’re dealing with is overcoming the disconnect between cybersecurity and business goals.”
América Televisión
Interface meets again with Jose Hernandez, Chief Digital Officer at América Televisión, who reveals how the company is embracing new business models, and maintaining market leadership in Peru.
“Launching our FAST channel represents a pivotal step in diversifying our content delivery and monetisation strategies. Furthermore, aligning us with global trends while catering to the changing viewing habits of our audience,” says Hernandez.
Also in this issue of Interface, we hear from eflow about new approaches to Regtech; get the lowdown on bridging the AI skills gap from CI&T; and GCX on the best ways to navigate changing cybersecurity regulations.
This month’s cover story sees our sister brand Fintech Strategy reporting from Money20/20 Europe in Amsterdam – a pivotal event…
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This month’s cover story sees our sister brand Fintech Strategy reporting from Money20/20 Europe in Amsterdam – a pivotal event in the fintech calendar, drawing over 8,000 participants from 2,300 companies worldwide.
Welcome to the latest issue of Interface magazine!
The RAI Amsterdam Convention Centre was the location for the world’s leading fintech conference. Money20/20 Europe offered a unique blend of insightful keynotes, panel discussions, and networking opportunities that underscored the transformative power of emerging technologies in financial services. We met with SC Ventures, Lloyds Banking Group, OSB Group, AirWallex, Plaid, Paymentology, Episode Six, Mettle (Nat West Group) and more to take the pulse of the latest trends across the fintech landscape.
Under the theme of ‘Human X Machine’, Money20/20 Europe explored the relationship between humans and intelligent machines, focusing on how the partnership between artificial and human intelligence will forge a new era in finance…
Publicis Sapient: Global Banking Benchmark Study
Interface was also proud to partner with Publicis Sapient at Money20/20 Europe for the launch of its third annual Global Banking Benchmark Survey. The survey draws on the insight of over 1000 senior executives in financial services across various global markets and focuses on the goals, obstacles, and drivers of digital transformation.
We spoke with Head of Financial Services Dave Murphy about its findings. “The survey focuses on how to think about solving problems end-to-end. Banks are dealing with legacy issues and taking a customer first view into solving the challenges. The practical application of AI across the banks is a significant theme as they look to automate decision-making and deliver better credit risk models.”
At the launch event for the study, Eoghan Sheehy, Associate MD, and Grace Ge, Senior Principal, highlighted that banks are primarily focused on improving existing processes rather than introducing new ones. Data Analytics and AI are identified as key priorities for digital transformation, with a focus on internal use cases and efficiency.
Eoghan and Grace also discussed the challenges faced by banks, including regulation, competition from companies like Amazon, and the need to attract talent. They emphasised the importance for financial institutions of modernising core infrastructure and building cloud infrastructure to support ongoing digital transformation. The study also notes the prevalence of the development of custom-made tools and the prioritising of internal use cases for AI implementation. Eoghan and Grace also provided examples of repeatable use cases and discussed the success factors for Data Analytics and AI.
STO Building Group: Enabling and Empowering People
Claudia Healey, Chief Human Resources Officer at STO Building Group, spoke to Interface about the HR platform empowering its people in pursuit of a strategic vision… “Culture is the number one priority in a people business like STO Building Group (STOBG). If you’re not nurturing and inspiring your folks, well, they can just vote with their feet. They don’t have to stay. Or they could do worse, they could quit and stay. And that’s something we would never want. Meeting your people where they’re at, understanding their goals and aspirations, and how you can help them reach their potential is vital. Realising how you can really see your people and truly understand what matters to them, is an incredible priority.”
Also in this issue, AI hype has previously been followed by an AI winter, we hear from Scott Zoldi, Chief Analytics Officer at FICO who asks, ‘Is the AI bubble set to burst?’ Elsewhere, we round up the top events in tech and learn how businesses can ensure their cloud storage is more sustainable in an age of rising demand for data and AI. Cloud storage without the climate cost is possible explains Fasthosts CEO Simon Yeoman.
Our cover story this month focuses on the work of Arianne Gallagher-Welcher. As the Executive Director for the USDA Digital…
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Our cover story this month focuses on the work of Arianne Gallagher-Welcher. As the Executive Director for the USDA Digital Service, in the Office of the OCIO, her team’s mission is to drive a tech transformation at the USDA. The goal is to better serve the American people across all of its 50 states.
Welcome to the latest issue of Interface magazine!
Welcome to a new year of possibility where technology meets business at the interface of change…
“We knew that in order for us to deliver what we needed for our stakeholders, we needed to be flexible – and that has trickled down from our senior leaders.” Arianne Gallagher-Welcher, Executive Director for the USDA Digital Service reveals the strategic plan’s first goal. Above all, the aim is to deliver customer-centric IT so farmers, producers, and families can find dealing with USDA as easy as using an ATM.
BCX: Delivering insights & intelligence across the Data & AI value chain
We also sat down with Stefan Steffen,Executive Leader for Data Insights & Intelligence at BCX. He revealed how BCX is leveraging AI to strategically transform businesses and drive their growth. “Our commitment to leveraging data and AI to drive innovation harnesses the power of technology to unlock new opportunities, drive efficiency, and enhance competitiveness for our clients.”
Momentum Multiply: A culture-driven digital transformation for wellness
Multiply Inspire & Engage is a new offering from leading South African insurance provider Momentum Health Solutions. Furthermore, it is the first digital wellness rewards program in South Africa to balance mental health and physical health in pursuing holistic wellness. CIO, Ndibulele Mqoboli, discusses re-platforming, cloud migrations, and building a culture of ownership, responsibility, and continuous improvement.
Clark County: Creating collaboration for the benefit of residents
Navigating the world of local government can be a minefield of red tape, both for citizens and those working within it. Al Pitts, Deputy CIO of Clark County, talks to us about the organisation’s IT transformation. He explains why collaboration is key to support residents. “We have found our new Clark County – ‘Together for Better’ – is a great way to collaborate on new solutions.”
Also in this issue, we hear from Alibaba’s European GM Jijay Shen on why digitalisation can be a driving force for SMEs. We learn how businesses can get cybersecurity right with KnowBe4 and analyse the rise of ‘The Mobility Society’.
Doug Laney is Innovation Fellow at West Monroe and a leading Data & Analytics strategist. We caught up with the author of Infonomics and Data Juice to talk tech and how companies can measure, manage and monetise to realise the potential of their data
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Our cover story explores the rise of data and information as an asset.
Welcome to the latest issueof Interface magazine!
Interface showcases leadersaiming to take advantage of data, particularly in a new world of AI technologies where it is the fuel…
How to monetise, manage and measure data as an asset
Our cover star is pretty big in the world of analytics… We meet the guy who defined Big Data. Doug Laney is Innovation Fellow at West Monroe and a leading Data & Analytics strategist. We caught up with the author of Infonomics and Data Juice to talk tech and learn how companies can measure, manage and monetise to realise the potential of their information. In his first book Laney advised companies to stop being fixated on hindsight-oriented analytics. “It doesn’t actually move the needle on the business. In the stories I’ve compiled over the last decade, 98% have more to do with organisations using data to diagnose, predict, prescribe or automate something. It’s not about asking questions about what happened in the past.”
Canvas Worldwide: A data-driven media business
Continuing this month’s data theme, we also spoke with Alisa Ben, SVP, Head of Analytics at full-service media agency Canvas Worldwide. Data has transformed the organisation, and what its clients do. “We look holistically at the client’s business and sometimes the tools we have might be right for them, sometimes not. It’s more about helping our clients achieve their business outcomes.”
TUI Musement: from digital transformation to digital pioneer
At travel giant TUI, handling data effectively is paramount when communicating consistently and meaningfully with up to 25 million customers annually. David Garcia, CIO for TUI Musement, talks about the tech evolution driving the travel giant’s provision of experiences, transfers and tours. It’s a big part of its operational shift from local to global. “As a CIO, I’ve always been interested in how the tech innovations we drive can support the business and add value.”
Hiscox: making cybersecurity more accessible
Liz Banbury, CISO at Hiscox and president of (ISC)² London Chapter, talks to us about how cybersecurity can become a more accessible, realistic career path for almost anybody. “When I was at school, topics like computer science didn’t even exist,” Banbury explains. “In one of my first jobs, over in Hong Kong, we were still using a typewriter! A lot has changed. My key point here is that there’s a lot of cybersecurity professionals who are really good at their job. They are inspiring, and have come from all walks of life. Crucially, they don’t have a maths, computer science, or technological background at all. But they still make great cybersecurity professionals.
Portland Community College: Risk vs Speed in Cybersecurity
Reet Kaur, former Chief Information Security Officer at Portland Community College, discusses the organisation’s transition to the cloud amid a digital transformation journey. “I don’t want to work with people who just say yes all the time. I want my ideas challenged to help forge the excellence in the security programmes I help build.”
DBHDS: Cybersecurity in healthcare
The Virginia Department of Behavioral Health and Developmental Services (DBHDS) exists to create ‘a life of possibilities for all Virginians’ and transform behavioural health. Its focus is on supporting people across the entire commonwealth. It helps them get the support they need in order to take wellness and recovery into their own hands. In an area like healthcare, sensitive information is all over the place, meaning cybersecurity is a priority – and this is where Glendon Schmitz, CISO at DBHDS, comes in. “The security team exists to help the wider organisation achieve its objectives with data. We’re there to protect the business, not the other way around.”
Also in this issue, we schedule the can’t miss tech events and get the lowdown on IoT security from the Mobile Ecosystem Forum.
This month’s cover story sees us speak with Brad Veech, Head of Technology Procurement at Discover Financial Services.
Having been a leader in procurement for more than 25 years, he has been responsible for over $2 billion in spend every year, negotiating software deals ranging from $75 to over $1.5 billion on a single deal. Don’t miss his exclusive insights where he tells us all about the vital importance of expertly procuring software and highlights the hidden pitfalls associated.
“A lot of companies don’t have the resources to have technology procurement experts on staff,” Brad tells us. “I think as time goes on people and companies will realise that the technology portfolio and the spend in that portfolio is increasing so rapidly they have to find a way to manage it. Find a project that doesn’t have software in it. Everything has software embedded within it, so you’re going to have to have procurement experts that understand the unique contracts and negotiation tactics of technology.”
There are also features which include insights from the likes of Jake Kiernan, Manager at KPMG, Ashifa Jumani, Director of Procurement at TELUS and Shaz Khan, CEO and Co-Founder at Vroozi.
Amit Thawani, CIO for Consumer Data & Engagement Platforms at Wells Fargo, on the journey towards becoming a customer-centric company
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This month’s cover story reveals how a customer-centric approach to technology is helping Wells Fargo deliver stable, secure, scalable, and innovative services.
Welcome to the latest issueof Interface magazine!
It’s our biggest issue yet! The common theme this month is the focus on the creation of customer-centric technologies that offer reliable, secure and helpful user journeys from travel and banking to health and business.
Interface dives deep for insights on understanding, planning, implementing and communicating change across industries.
Amit Thawani, Chief Information Officer (CIO) for Consumer Data & Engagement Platforms (CDEP) on the technology journey at Wells Fargo: “All tech employees at Wells Fargo are tasked with working towards delivering stable, secure, scalable, and innovative services at speed that delight and satisfy our customers while unleashing the skills potential of our employees.”
TUI: Developing a technology ecosystem
Kristof Caekebeke, CIO for Product & Engagement, is a member of the leadership team that is driving the transformation of the TUI technical ecosystem which has seen Master Domain Owners taking different blocks of the ecosystem under their control to roll out across the organisation.
TUI Group
Responsible for product and engagement, Caekebeke’s focus is on building products out of the thousands of hotels, flights, experiences and cruises TUI is offering. “I’m responsible for every contact point between the customer and TUI. The websites, the mobile apps, the retail systems – any contact point we have between the customer and TUI. It’s a large team of 1,100 tech people.
A digital bank transformation journey with Banco PAN
“Until 2018 Banco PAN was very much an analogue company reliant on legacy paper processes,” recalls Leandro Marçal. Joining the bank in December 2020, to become Technology & Operations Director (CIO/COO), Marçal was tasked with accelerating a digital transformation journey.
“Banco PAN invested in innovation before I arrived,” says Marçal. “It is my team’s job to formalise the path towards becoming a digital bank. Our legacy operation was digitalising. It was an opportunity to improve the customer experience with our checking account and credit card systems.”
Pohlad Companies: The power of people
A pillar of the community in Minneapolis, Pohlad Companies is well known to Minnesotans for its influence, its charity work, and the opportunities it has created for people since the 1950s.
Alongside significant commercial real estate investments, Pohlad Companies owns a custom engineering and robotics company, a group of automotive dealerships specialising in luxury vehicles, a film production studio, and many more businesses. Famously, the Pohlad family also owns the Minnesota Twins, a Major League Baseball team.
This variety is part of what makes Rachel Lockett’s job so exciting. She’s Pohlad Companies’ CIO and has spent a decade in her current role. Lockett began her career as a programmer over 25 years ago and quickly moved into IT leadership management.
Coalfire: Embracing change in cybersecurity
If you wait for something to happen, then it’s often too late. The art of having a finger on the pulse is an essential ingredient to success. Failure to manage change and implement cybersecurity protocols could mean leaving an organisation vulnerable to hackers.
Sreeveni Kancharla, Coalfire’s first Chief Information Officer, is leading the company’s digital transformation with unwavering determination. As a cybersecurity advisor, Coalfire assists private and public sector organisations in managing threats, closing gaps, and mitigating risks. Kancharla ensures that her team stays up-to-date with the latest technologies to guard against zero-day attacks.
Uni of Kansas Health: Cybersecurity at the heart
Speed versus safety. The two topics are intrinsically linked and vital in their own individual way. But can you have both in healthcare when the risks are so great? Ultimately, there is no higher stake than saving people’s lives – it goes above everything and is why cybersecurity is so vital.
Protecting the healthcare system
“There’s nothing more important to me than patient care,” affirms Michael Meis, Associate Chief Information Security Officer at The University of Kansas Health System. “It is one of the highest callings you can imagine, to be able to help people. While the cybersecurity team and me, individually, do not directly care for patients, we enable a lot of that patient care to continue and to be able to achieve some of the goals that the health system has set to provide that healing, research, and innovation within the healthcare space.”
Also in this issue, we ask ChatGPT what the future holds for AI and learn from Zoom how businesses can leverage analytics for insights from their hybrid events.
Mike Randall, CEO at Simply Asset Finance, discusses how to build a people-first strategy that enables growth.
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As the UK economy continues to balance on the edge of a recession, employee retention is quickly being pushed to the top of CEOs’ lists. Over the past couple of years, the job market has shifted dramatically with previously unheard terms such as ‘the great resignation’, ‘quiet quitting’ and ‘hybrid working’ becoming commonplace. People are rightly prioritising their working situation and job satisfaction levels, questioning whether they believe in the organisations they are committing so much time to.
Consequently, there has been a power dynamic shift in favour of the workforce. Reportedly in the third quarter of 2022 businesses witnessed over 365,000 job-to-job resignations across the UK. In similar fashion, the phenomenon of ‘quiet quitting’ – doing the bare minimum required of a job – has become a growing concern but its rise is prompted by a growing number of employees feeling disengaged in their roles.
Against this backdrop of a highly turbulent job market, and increasingly difficult macro-economic pressures, it’s vital for CEOs to prioritise a people-first strategy to ensure healthy growth for their business in 2023. Data from Deloitte has even revealed that experts believe how engaged a workforce feels can directly correlate to overall business output, with 93% of HR and business leaders in agreement that building a sense of belonging is crucial for organisational performance.
Mike Randall, CEO at Simply Asset Finance
However, creating the right environment and recruiting, maintaining and nurturing the right talent to ensure a people first approach can be daunting. With this in mind, here are four learnings CEOs might want to consider when approaching this challenge:
1. Define your beliefs
Before CEOs and founders can hope to attract the right talent, it is critical to first distil and translate the business vision into something that can be understood by employees. Put simply, this means defining the business’ beliefs.
Some business leaders may already refer to this as an ‘employer brand’, and it can be key to not only securing better talent, but also saving a business money in the long-term. Data from LinkedIn for example, recently found that a strong employer brand can help to reduce employee turnover by as much as 28% and cost-per-hire by 50%. Defining these beliefs – or the tenets a business does and doesn’t stand for – is therefore the perfect exercise to put a vision onto paper, and clearly communicate it to its prospective talent.
2. Build a solid culture
Once these beliefs have been defined, they must be reflected, and built into a strong culture. A business’ beliefs should permeate through the whole organisation – from customer communications, to how staff are treated, to how leaders run the business. Culture should essentially be a representation of a business’ beliefs being put into practice.
Building a strong culture in a business, however, is not solely about these beliefs but also extends into how employees are equipped with the tools they need to succeed. Companies that invest in learning and development for example, have been found to benefit from a 24% higher profit margin than those that don’t, according to the Association of Talent Development. Training and development should therefore be seen as a worthwhile and necessary investment that can solidify your culture and ensure profitability, not just an unavoidable cost.
3. Invest in retention
With research from Oxford Economics estimating the average turnover per employee earning £25,000 a year to be £30,000 plus, there is an evident cost to businesses that fail to invest in retention. Tackling this will mean regularly taking the time to truly understand what makes employees tick – and more specifically, understanding their motivations, attitudes, behaviours, strengths and weaknesses.
As the past few years have evidenced, individuals are no longer deciding where they work solely based on salary, but are also thinking about employer values, flexibility, and benefits. To avoid employee churn, businesses should regularly take time to understand what drives their employees and implement retention strategies to address these drivers. Gathering and analysing employee data will play an important role here over the coming years, and should be built into a long-term strategy to optimise employee satisfaction.
4. Build for the future
A common challenge encountered by modern businesses and startups wanting to take a people first approach, can be their ability to stay committed to it. As a business grows in size and becomes successful, it can be all too easy to let external factors dictate its purpose and for it to lose sight of what it initially stood for. The reality is that when this happens, a business is in its most vulnerable state – as its beliefs become increasingly distant, and worse, employees no longer understand what it stands for.
When creating a people-first strategy its therefore important to think long-term. If there are external factors that will potentially put this strategy at risk in future, it’s crucial to identify them, and put in practical steps to mitigate them where possible. The pandemic, for example, is a prime example of an external factor that interrupted the status quo of many businesses – disrupting employees, customers and operations in general. While they can be unpredictable in nature, having a plan to get through these times can help to get you back on track and reassure talent that a solution is in place.
In this economic climate, defining beliefs, building a solid culture, and retention plan should be at the core of every business’ strategy. It’s only when these things are in place that a business can hope to attract and retain talented people that exude the same passion and values built into the heart of a business. As while a business’ growth may be defined by its leaders, it is delivered by its people who are putting that vision into practice.
Procurement is in a state of flux. Against a backdrop of economic uncertainty, the procurement landscape is volatile and requires…
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Procurement is in a state of flux.
Against a backdrop of economic uncertainty, the procurement landscape is volatile and requires agility to navigate turbulent waters. But, despite significant disruption could there still be opportunity?
Simon Whatson, Vice President of Efficio Consulting, is optimistic about the future of digital procurement and despite a challenging few years he is confident of a successful bounce back. He gives us the lowdown on the direction of travel for digital procurement in 2023.
As an executive with considerable experience in the space, we’d love to learn more about your background and how you ended up in procurement. Why was this the specialism for you and how did you get involved to begin with?
Simon Whatson (SW): “I think the one-word answer of how I came into procurement was accidental. I studied maths at university, with a year in France, before I began looking for different roles to apply for.
“Eventually, I was offered a position with a big plumbing and heating merchant with global operations. I worked in that supply chain team for two and a half years. Although it was called supply chain, a lot of the work was procurement, which involved negotiating with suppliers. It was after that stint there, that I discovered consulting and joined a boutique procurement consultancy. Now I am onto my third consultancy and I’m very happy here!
“In terms of why I’ve stayed, one of the success factors in procurement is being able to work cross-functionally. Procurement doesn’t own any of the spending that it is responsible for helping to optimise. It must work with other functions and the spend owners. I quite like the people side of that, building relationships, almost selling internally to bring teams together. That really appeals to me and is a key reason why I’ve been very happy in procurement.”
As we move into exploring procurement today in 2023. The space is filled with challenges and complexities. You only need to look at the last few years. Covid, war in Ukraine, inflation – how would you describe the world’s recent challenges and their effect on the industry and what do you feel CPOs and leaders can do to combat these issues?
SW: “I would flip it around and say that these are not so much challenges but rather opportunities for procurement. When I started my career 18 years ago, procurement was often fighting to get a voice and there were complaints that procurement was not represented at the top table, but the war in Ukraine, inflation, COVID and ESG, these are things which are now on the C-suite agenda and procurement is ideally positioned to help companies face those challenges. If you think about COVID and the war in Ukraine, procurement is in a privileged position to help with this.
“I see some procurement functions that prefer to do what they know, which focuses on the process and transactional side. However, there are also many forward-thinking CPOs and procurement professionals out there, that have really seized this opportunity of being on the C-suite agenda and drive the thinking and the solutions to some of these big challenges we’re seeing.”
Although new technology in procurement has been around for well over a decade, digitalisation has become so much more of an important topic. How would you sum up where procurement and supply chain are in terms of digital transformation today?
SW: “It’s a bit laggard, but digital transformation is difficult, and we have to recognise there are some real trailblazers. There are some firms doing some fantastic things in digital to produce better outcomes. If you contrast your experience when you’re buying something in your private life, it’s much easier than 20 years ago. You can get access to a wealth of pre-sourced things, whether it’s food, a holiday, a car, or a book. You can see reviews of what other people think of these things.
“But when you go into your workplace as a business user and you want to buy something, it doesn’t quite work like that yet. You often have to fill in a form, send it off and wait for them to come back to you. They might come back a little bit later than you were hoping and might tell you that they don’t have that part on the supply frameworks. I think people sometimes get confused about how it can be so easy to buy something as large as a car or a holiday on their sofa at home, but when they want to buy something at work, it seems to be quite cumbersome. Digital can help a lot with that, but it is incumbent on organisations and procurement functions to figure out how to recreate that customer experience that we’ve become accustomed to in our private lives.”
With a new generation of leaders growing up with technology, some might say that it could be a key driver in helping to speed the adoption in procurement along. Is this something you would agree with or what would you point to as a key driver?
SW: “I do think that it will act as one of the catalysts for further digital transformation in organisations, because if procurement doesn’t manage to recreate that customer experience that the new generation expects, then they won’t use procurement going forward and will look to bypass it.
“The analogy that I’ve used previously in this case is one of travel agents. I remember as a child, my parents were able to take us on holiday and I remember the whole process. We would walk into town to the travel agent, and look at some of the brochures of options. They often then had to phone the various airlines or resorts on our behalf. They might not be able to get through, so we’d have to come back the next day. I remember as a child being quite excited by the whole process but actually, thinking back, it was quite cumbersome. You compare that to now, with being able to review online, and you can get instant answers to your questions. It’s not a coincidence that travel agents don’t really exist anymore.”
How much of a challenge is it to not get caught leveraging technology for technologies sake? How important is it to stay true to your approach and be strategic?
SW: “We conducted a study of many procurement leaders and CPOs a few years ago, and one of the things that we found was that about 50% of procurement leaders admitted to having bought technology just on the basis of a fear of missing out, without any real understanding of the benefits that technology was going to bring. That was a real shock and a revealing find because technology is not cheap, and its implementation is quite disruptive. If you’re purchasing a system because everybody else is using it, then there could be some pretty costly mistakes. It is really important to make sure that when buying technology, it is because the benefits are fully understood.
“My advice to companies when looking to digitalise is own your data, visualise that data, and manage your knowledge. If you can focus on getting those things right in that order, and make your technology decisions to support that goal, then that’s a much better way of thinking about it rather than just jumping in and buying a piece of technology.”
It’s clear that the procurement space is an exciting, but challenging, place to be. What do you think will play a key role in the next 12 months to push the digital conversation further to take procurement to the next level?
SW: “Looking forward, one thing that procurement needs to do and continue to do is attract the best people. Ultimately, people are what makes an organisation, and it is what makes a function successful. I think procurement has often not looked for the right skills in the people that it employs. Traditionally, it’s looked for people with procurement experience and while they are valuable and required, we also need leadership potential. People who think a bit more outside the box and aren’t so process driven. A lot of what procurement has done in previous years has been process driven, so if you’re just limiting your search of people to those that have had procurement experience, you’re inevitably going to end up with a lot of people who are process driven.
“I think being bolder and recruiting people from different backgrounds with different skill sets is the way to go. If procurement can ‘own’ the ESG space, that will help with the younger generation see procurement make a difference. I think that’s one thing that will be key to success going forward.”
Check out the latest issue of CPOstrategy Magazine here.
Paul Farrow, Vice President of Hilton Hotels’ Supply Management, sits down with us to discuss how his organisation’s procurement function has evolved amid disruption on a global scale
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The hospitality industry has endured a rough ride over the past few years.
Following the COVID-19 pandemic which stopped the world in its tracks and now with millions facing a cost-of-living crisis, it’s been a period of unprecedented disruption for those involved in the space and beyond.
But it’s a challenge met head-on by Paul Farrow, Vice President of Supply Management at Hilton Hotels, and his team who have been forced to respond as the world continues to shift before their eyes.
Farrow gives us a closer look into the inner workings of his firm’s procurement function and how he has led the charge during his time with Hilton Hotels.
Could we start with you introducing yourself and talking a little about your role at Hilton Hotels?
Paul Farrow (PF): “I’m the Vice President of Hilton’s Supply Management, or HSM as we call it. I’ve been with Hilton Hotels for 12 and a half years, and my role is to head the supply chain function for our hotels across Europe, the Middle East and Africa.
“Over the past few years, Hilton has grown rapidly and has now got 7,000 hotels in over 125 countries globally. What is really exciting is Hilton Supply Management doesn’t just supply Hilton Hotels and the Hilton Engine because we also now supply our franchisees and competitive flags. While we have 7,000 hotels globally, Hilton Supply Management actually supplies close to 13,000 hotels. That’s an interesting business development for us, and a profit earner too.”
You’re greatly experienced, I bet you’ve seen supply chain management and procurement change a lot in recent years?
PF: “The past two to three years have been tremendously challenging on so many industries but I’d argue that hospitality got hit more than most as a result of the Covid pandemic. Here at Hilton, supply management was really important just to keep the business operational throughout that tough time, but I’m delighted to say we’re fully recovered now.
“Looking back, it was undoubtedly difficult, and you only have to look at the media to see that we’re now going through a period of truly unprecedented inflation. On top of the normal day job, it’s certainly been a very busy time.”
Hospitality must have been under an awful lot of pressure during the pandemic…
PF: “Most of our teams as a business and all functions have worked together far more collaboratively than ever before through the use of technology and things like Microsoft Teams and Zoom. Trying to work remotely as effectively as possible changed the way we all had to think and the way we had to do. Now we’re back in the workplace and in our offices, we’re actually looking to take advantage of that new approach.”
Inflation, rising costs, energy shortages, as well as drives towards a circular economy means it’s quite a challenging time for CSCOs and CPOs right now, isn’t it?
PF: “Those headwinds have caused and created challenges of the like that we’ve not seen before. The war in Ukraine and Russia has meant significant supply chain disruption and supply shortages of some key ingredients and raw materials. China is a significant source of materials and they’re still having real challenges to get their production to keep up with demand.
“All the local and short-term challenges are around energy and fuel pricing, so throughout the supply chain that’s been a major factor to what we’ve had to deal with. On top of that is the labour shortages. We rely heavily throughout the supply chain and within our business to utilise labour from around the world. In my region, particularly from say Eastern Europe as well as other businesses all fighting for a smaller labour pool than we had before. We are fighting with the likes of the supermarkets, Amazon’s, not just other hotel companies to capture the labour pool we need both in our properties but also within our supply chain supplies themselves.
Hilton operates a rather unique procurement function, doesn’t it?
PF: “We trade off the Hilton name because our brand strength is something that we are able to utilise and we’re very proud of, but we’ve also got additional leverage by having that group procurement model.
“We’ve got essentially two clients. We’ve got our managed estate which is when an owner chooses to partner with Hilton, they’re signing a management agreement because they want the benefit and value of the Hilton engine. That could be revenue management, how we manage onboarding clients and customers through advertising, as well as the other support we give in terms of finance, HR, marketing and sales as well as procurement.”
HSM is a profit centre and revenue driver through its group procurement model but how does this work?
PF: “Our secret sauce is our culture. It’s our people and that filters across all of our team members and indeed all of our functions. The key strategic pillars are the same for health and supply management around culture, maximising performance and so on as they are across the overall global business.
“Across our 7,000 plus hotels, the majority are actually franchised hotels because that’s the legacy of what still is the model in the US. When I joined Hilton 12 and a half years ago, the reverse is true where nearly all of our hotels in Europe, Middle East and Africa, and indeed in Asia Pacific, were and are managed. In the Europe, Middle East and Africa regions right now we’re building up close to a 50/50 split between managed, leased and franchised.”
What has pleased you most about the roll-out of the HSM?
PF: “It’s certainly not been easy because we’ve got 70 countries that sit within our region here in EMEA and Hilton’s penetration in those individual countries is very different. We may have 100 hotels in one of those markets and only one or two in specific countries. Our scale and our ability to get logistics solutions is different by market.
“Getting everyone on board to what we want to achieve to our guests and to our owners means we have to pull different levers. We have very effective brand standards. If you’re signing up to Hilton, you’re signing up to delivering against those brand standards that we believe are right for our organisation.”
What kind of feedback have you had from your clients?
PF: “Integrity is in our DNA, and we work very closely with our suppliers who we value as partners. These are long-term relationships, and we work hand in hand because we have to see that they’re successful so that we can be successful – it’s really important to what we do and we constantly look for feedback.
“With our internal and our external customers, we’ll have quarterly business reviews and so we’ll get that feedback through surveys where we are asking them to tell us what we do well and what we could do better. Our partners are now asking what additional value can you do to bring support to our organisation through ESG? So that’s what’s on the table now when it wasn’t before. But it’s not just that – it’s about the security of supply competitiveness, competitiveness of pricing, and a whole bunch of other very important things as well.”
Looking to the future, what’s on the agenda for the next few years?
PF: “We’re out there meeting and greeting people in person and there’s always new opportunities that make things exciting in what we do and how we work. Innovation’s very high on our agenda and we’re very proud of what we do in food and beverage. In non-food categories, it’s about how we support our owners and our hotel general managers to find that competitive edge and do the next big thing ahead of our competitors.”
Anything else important to know?
PF: “One thing we’ve been able to take full advantage of is how we’ve been able to grow our business by bolting on new customers. I think it’s fantastic that our competitors choose to use Hilton Supply Management because they benchmarked what our capabilities are and how competitive we are.
“Another key part of the agenda is environmental, social and governance (ESG) sustainability. Responsible sourcing and everything that sits within that is front and centre of what we do. Within that you’ve got human rights, animal welfare, single use plastics as well as general responsible sourcing like managing food waste. The list is very long, but they’re all very important.”
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Here are 10 of the most important leadership skills that CEOs need to demonstrate in 2023.
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In today’s world, a CEO needs to be lots of things to different people. The importance of having the leadership skill to being able to lead through unprecedented disruption was highlighted by the COVID-19 pandemic and helped to define what makes a good CEO.
Here are 10 of the most important leadership skills that CEOs need to demonstrate in 2023.
1. Clear communication
Communicating effectively with employees is one of the most vital skills any leader can have. By adopting a transparent mindset, it leaves little room for miscommunication or misunderstandings. But rather than just being eloquent, CEOs should deliver meaningful content too. A CEO needs to be able to communicate the essence of the business strategy and the methodology for achieving it.
2. Strong talent management strategy
People are the most important component of all businesses. CEOs who are able to recruit and retain key employees have a greater chance of increasing productivity and efficiency. After recruiting good people, the key to retaining them is by harnessing a positive work environment that empowers employees to succeed.
3. Decision-making
As a leader, thinking strategically to make effective decisions is vital to the success of an organisation. Making decisions is a key part of leadership as well as having the conviction to stand by decisions or agility to adapt when those decisions don’t have the required outcome. While all decisions might not be favourable, making unpopular but necessary calls are important characteristics of a good leader.
4. Negotiation
Negotiation is a fundamental part of being a CEO. In a top leadership position, almost every business conversation will be a negotiation. Good negotiations are important to an organisation because they will ultimately result in better relationships, both with staff inside the company and externally. An effective leader will also help find the best long-term solution by finding the right balance and offering value where both parties feel like they ‘win’.
5. Creativity and innovation
Being quick-thinking and ready to explore new options are great skills of a CEO. Creative leadership can lead to finding innovative solutions in the face of challenging and changing situations. It means in the midst of disruption, of which it has been increasingly prevalent, leaders can still find answers for their teams. Creative CEOs are those who take risks and empower employees to drop outdated and overused practices to innovate and try new things that could lead to greater efficiency.
6. Agility
Without agility over the past few years, businesses would have failed. CEOs were forced to embrace remote working following the advent of the COVID-19 pandemic whether they liked it or not. Now, faced against a potential recession, these macroeconomic events are unavoidable and have to be managed carefully. Effective leaders will have their fingers on the pulse and ready to respond to changes.
7. Strategic forecasting
Creating a clear path forward is essential to achieving uninterrupted success. The ability to look into the future and identify trends and issues to then react to is vital. Good CEOs are able to plan strategically and make informed decisions to set goals and plan for the future easily.
8. Delegation
CEOs can’t do everything. A leader tends to be pulled in a number of different ways every day and it is impossible to be on top of everything. This means the importance of bringing in a team of people who are trusted and skilled in their respective areas of expertise. Successful CEOs are expert delegators because they recognise the value of teamwork and elevating those around them.
9. Approachability
An approachable CEO who welcomes conversation and is an active listener will help employees feel at ease raising issues or concerns. This approach will help build strong relationships with staff and customers and encourage a healthy culture which is beneficial to employee retention. Leaders with strong, trusting and authentic relationships with their teams know that investing time in building these bonds which makes them more effective as a leader and creates a foundation for success.
10. Growth mindset
If a CEO arms themselves with a growth mindset it allows them to meet challenges head-on and evolve. This shines a light on improving through effort, learning and persistence. As others may back down in the face of adversity and upheaval, successful CEOs will strive to move forward with confidence. Those with a growth mindset are unlikely to be swayed as they have the tools needed to reframe challenges as opportunities to grow.
In McKinsey’s latest report ‘Actions the best CEOs are taking in 2023’, we examine three of the biggest trends on the c-level agenda
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Anyone can sail a ship when things are going well. But it takes a strong, robust and characterful CEO to steer a business through choppy waters and out the other side.
In McKinsey’s latest report ‘Actions the best CEOs are taking in 2023’, the research and advisory firm uncovered which trends are set to have the biggest impact on how CEOs lead their business throughout the year.
McKinsey’s CEO Excellence Survey surveyed 200 of the best corporate CEOs of the past 15 years. This was completed by whittling down a list of all the current and former CEOs of the 1,000 largest public companies during that timeframe. The list was subsequently filtered based on tenure, including only those who had completed at least six years in the role. From there, the CEOs were continuously shortlisted until the best 200 were determined.
Each CEO was asked to identify the top three trends that are set to determine how leaders tackle the future. Here is an insight into those findings.
1. Actions to deal with digital disruption
CEOs are targeting digital trends in three key ways: developing advanced analytics, enhancing cybersecurity and automating work. OpenAI’s launch of ChatGPT has accelerated the demand of companies looking to embrace advanced analytics for a competitive advantage. Improving cybersecurity is another key action for CEOs with the importance of guarding against external threats paramount amid strengthening and more mature cyberattacks. Lastly, automating work is another key priority to scale efficiency and eliminate boring and manual tasks which free up people’s time.
2. Actions to deal with the risk of high inflation and economic downturn
One CEO who is worried about economic uncertainty told McKinsey: “Act early to lower costs and protect the balance sheet so that you are stronger and leaner when the economy begins to turn more favourably.” McKinsey found that companies that outperformed the 2008 financial crisis cut operating costs by 1% before the downturn while the others expanded costs by the same percentage. The best performers reduced their debt by $1 for every $1 of book capital before the downturn. This can be done by reducing operating expenses, redesigning products and services as well as reassessing strategic and economic assumptions.
3. Actions to deal with the escalation of geopolitical risk
According to McKinsey, there are three actions to help manage the escalation of global and national crises. CEOs are targeting building robust compliance capabilities, creating resilience in supplier networks and investing in monitoring and response capabilities. These actions come following the challenges presented by COVID-19, the war in Ukraine and now inflation concerns. Many firms are choosing to build their trade compliance organisations and improve how they screen different customers and companies. While a defensive approach is the way forward for many, some companies see the turbulent times as an opportunity.
What does today’s CEO need to do to accelerate an organisation’s digital transformation journey?
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Digital transformation journeys are no one-size-suits-all. There is no singular way to welcome a new wave of technology into operations.
Since the turn of the century, digitalisation has had an increasingly influential impact on the way CEOs make decisions. Today’s world is full of disruption and potential risk. And with technology growing in complexity it can be challenging to lead such a revolution against a backdrop of economic uncertainty.
Embracing digital
According to KPMG 2022 CEO Outlook, which draws on the perspectives of 1,325 global CEOs across 11 markets, 72% of CEOs agree they have an aggressive digital investment strategy intended to secure first-mover or fast-follower status.
Advancing digitalisation and connectivity across the business is tied (along with attracting and retaining talent) as the top operational priority to achieve growth over the next three years. This digital transformation focus could be driven as a result of increasingly flexible working conditions and greater focus on cybersecurity threats.
However, the prospect of recession is threatening to halt digital transformation in the short-term. KPMG research found that four out of five CEOs note their businesses are pausing or reducing their digital transformation strategies to prepare for the anticipated recession.
This is reinforced further when 70% say they need to be quicker to shift investment to digital opportunities and divest in those areas where they face digital obsolescence.
When a company’s digital transformation ambition is mismatched to its readiness, it is the CEO’s responsibility to close the gap. According to Deloitte, in order to do this successfully, the CEO must assess the current level of organisational readiness for change.
This covers four key pillars that are mixed together to work out an organisation’s overall readiness: leadership, culture, structure and capabilities.
How CEOs can close the gap
Leadership: CEOs need to ensure their c-suite and other key executives are motivated and equipped to execute the vision. CEOs interviewed by Deloitte in a recent study emphasised the importance of the leadership team supporting the transformation vision and having a positive attitude and willingness to transform.
Culture: A large potential barrier to readiness in the organisation is down to culture. Low cultural readiness takes the form of bureaucratic, reactive and risk-averse ways of working that are at against the collaborative, proactive learning mindset needed for ambitious transformation.
Structure: If a company hopes to operate differently, it could mean the need for organising in an alternative way. CEOs will often need to lead the reorganisation of teams, assignment of new roles, revision of incentives, strategies to collapse organisational hierarchies or layers to increase agility.
Capabilities: CEOs need to equip their organisation with four key capabilities to harness digital for a superior capacity for change. These are nimbleness, scalability, stability and optionality which are often enabled or supercharged by digital technologies which are critical factors for competing in an increasingly disrupted world.
For now, one of the CEOs most important roles when steering the ship through disruption is to be ahead of the latest trends and tackle change head-on. By embracing a new digital future that will provide the company with long-lasting benefits, it will help create a brighter and future-proofed firm for years to come even after the CEO is gone.
Expert analysis of the tech trends set to make waves this year
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Digital transformation is a continuing journey of change with no set final destination. This makes predicting tomorrow a challenge when no one has a crystal ball to hand.
After a difficult few years for most businesses following a disruptive pandemic and now battling a cost-of-living crisis, many enterprises are increasingly leveraging new types of technology to gain an edge in a disruptive world.
With this in mind, here are what experts predict for the next 12 months…
1. Process Mining
Sam Attias, Director of Product Marketing at Celonis, expects to see a rise in the adoption of process mining as it evolves to incorporate automation capabilities. He says process mining has traditionally been “a data science done in isolation” which helps companies identify hidden inefficiencies by extracting data and visually representing it.
“It is now evolving to become more prescriptive than descriptive and will empower businesses to simulate new methods and processes in order to estimate success and error rates, as well as recommend actions before issues actually occur,” says Attias. “It will fix inefficiencies in real-time through automation and execution management.”
2. The evolution of social robots
Gabriel Aguiar Noury, Robotics Product Manager at Canonical, anticipates social robots to return this year. After companies such as Sony introduced robots like Poiq, Aguiar Noury believes it “sets the stage” for a new wave of social robots.
“Powered by natural language generation models like GPT-3, robots can create new dialogue systems,” he says. “This will improve the robot’s interactivity with humans, allowing robots to answer any question.
“Social robots will also build narratives and rich personalities, making interaction with users more meaningful. GPT-3 also powers Dall-E, an image generator. Combined, these types of technologies will enable robots not only to tell but show dynamic stories.”
3. The rebirth of new data-powered business applications
Christian Kleinerman, Senior Vice President of Product at Snowflake, says there is the beginning of a “renaissance” in software development. He believes developers will bring their applications to central combined sources of data instead of the “traditional approach” of copying data into applications.
“Every single application category, whether it’s horizontal or specific to an industry vertical, will be reinvented by the emergence of new data-powered applications,” affirms Kleinerman. “This rise of data-powered applications will represent massive opportunities for all different types of developers, whether they’re working on a brand-new idea for an application and a business based on that app, or they’re looking for how to expand their existing software operations.”
4. Application development will become a two-way conversation
Adrien Treuille, Head of Streamlit at Snowflake, believes application development will become a two-way conversation between producers and consumers. It is his belief that the advent of easy-to-use low-code or no-code platforms are already “simplifying the building” and sharing of interactive applications for tech-savvy and business users.
“Based on that foundation, the next emerging shift will be a blurring of the lines between two previously distinct roles — the application producer and the consumer of that software.”
He adds that application development will become a collaborative workflow where consumers can weigh in on the work producers are doing in real-time. “Taking this one step further, we’re heading towards a future where app development platforms have mechanisms to gather app requirements from consumers before the producer has even started creating that software.”
5. The Metaverse
Paul Hardy, EMEA Innovation Officer at ServiceNow, says he expects business leaders to adopt technologies such as the metaverse in 2023. The aim of this is to help cultivate and maintain employee engagement as businesses continue working in hybrid environments, in an increasingly challenging macro environment.
“Given the current economic climate, adoption of the metaverse may be slow, but in the future, a network of 3D virtual worlds will be used to foster meaningful social connections, creating new experiences for employees and reinforcing positive culture within organisations,” he says. “Hybrid work has made employee engagement more challenging, as it can be difficult to communicate when employees are not together in the same room.
“Leaders have begun to see the benefit of hosting traditional training and development sessions using VR and AI-enhanced coaching. In the next few years, we will see more workplaces go a step beyond this, for example, offering employees the chance to earn recognition in the form of tokens they can spend in the real or virtual world, gamifying the experience.”
6. The year of ESG?
Cathy Mauzaize, Vice President, EMEA South, at ServiceNow, believes 2023 could be the year that environmental, social and corporate governance (ESG) is vital to every company’s strategy.
“Failure to engage appropriate investment in ESG strategies could plunge any organisation into a crisis,” she says. “Legislation must be respected and so must the expectations of employees, investors and your ecosystem of partners and customers.
“ESG is not just a tick box, one and done, it’s a new way of business that will see us through 2023 and beyond.”
7. Macro Trends and Redeploying Budgets for Efficiency
Ulrik Nehammer, President, EMEA at ServiceNow, says organisations are facing an incredibly complex and volatile macro environment. Nehammer explains as the world is gripped by soaring inflation, intelligent digital investments can be a huge deflationary force.
“Business leaders are already shifting investment focus to technologies that will deliver outcomes faster,” he says. “Going into 2023, technology will become increasingly central to business success – in fact, 95% of CEOs are already pursuing a digital-first strategy according to IDC’s CEO survey, as digital companies deliver revenue growth far faster than non-digital ones.”
8. Organisations will have adopted a NaaS strategy
David Hughes, Aruba’s Chief Product and Technology Officer, believes that by the end of 2023, 20% of organisations will have adopted a network-as-a-service (NaaS) strategy.
“With tightening economic conditions, IT requires flexibility in how network infrastructure is acquired, deployed, and operated to enable network teams to deliver business outcomes rather than just managing devices,” he says. “Migration to a NaaS framework enables IT to accelerate network modernisation yet stay within budget, IT resource, and schedule constraints.
“In addition, adopting a NaaS strategy will help organisations meet sustainability objectives since leading NaaS suppliers have adopted carbon-neutral and recycling manufacturing strategies.”
9. Think like a seasonal business
According to Patrick Bossman, Product Manager at MariaDB corporation, he anticipates 2023 to be the year that the ability to “scale out on command” is going to be at the fore of companies’ thoughts.
“Organisations will need the infrastructure in place to grow on command and scale back once demand lowers,” he says. “The winners in 2023 will be those who understand that all business is seasonal, and all companies need to be ready for fluctuating demand.”
10. Digital platforms need to adapt to avoid falling victim to subscription fatigue
Demed L’Her, Chief Technology Officer at DigitalRoute, suggests what the subscription market is going to look like in 2023 and how businesses can avoid falling victim to ‘subscription fatigue’. L’Her says there has been a significant drop in demand since the pandemic.
“Insider’s latest research shows that as of August, nearly a third (30%) of people reported cancelling an online subscription service in the past six months,” he reveals. “This is largely due to the rising cost of living experienced globally that is leaving households with reduced budgets for luxuries like digital subscriptions. Despite this, the subscription market is far from dead, with most people retaining some despite tightened budgets.
“However, considering the ongoing economic challenges, businesses need to consider adapting if they are to be retained by customers in the long term. The key to this is ensuring that the product adds value to the life of the customer.”
11. Waking up to browser security
Jonathan Lee, Senior Product Manager at Menlo Security, points to the web browser being the biggest attack surface and suggests the industry is “waking up” to the fact of where people spend the most time.
“Vendors are now looking at ways to add security controls directly inside the browser,” explains Lee. “Traditionally, this was done either as a separate endpoint agent or at the network edge, using a firewall or secure web gateway. The big players, Google and Microsoft, are also in on the act, providing built-in controls inside Chrome and Edge to secure at a browser level rather than the network edge.
“But browser attacks are increasing, with attackers exploiting new and old vulnerabilities, and developing new attack methods like HTML Smuggling. Remote browser isolation is becoming one of the key principles of Zero Trust security where no device or user – not even the browser – can be trusted.”
12. The year of quantum-readiness
Tim Callan, Chief Experience Officer at Sectigo, predicts that 2023 will be the year of quantum-readiness. He believes that as a result of the standardisation of new quantum-safe algorithms expected to be in place by 2024, this year will be a year of action for government bodies, technology vendors, and enterprise IT leaders to prepare for the deployment.
“In 2022, the US National Institute of Standards and Technologies (NIST) selected a set of post-quantum algorithms for the industry to standardise on as we move toward our quantum-safe future,” says Callan.
“In 2023, standards bodies like the IETF and many others must work to incorporate these algorithms into their own guidelines to enable secure functional interoperability across broad sets of software, hardware, and digital services. Providers of these hardware, software, and service products must follow the relevant guidelines as they are developed and begin preparing their technology, manufacturing, delivery, and service models to accommodate updated standards and the new algorithms.”
13. AI: fewer keywords, greater understanding
AI expert Dr Pieter Buteneers, Director of AI and Machine Learning at Sinch, expects artificial intelligence to continue to transition away from keywords and move towards an increased level of understanding.
“Language-agnostic AI, already existent within certain AI and chatbot platforms, will understand hundreds of languages — and even interchange them within a single search or conversation — because it’s not learning language like you or I would,” he says. “This advanced AI instead focuses on meaning, and attaches code to words accordingly, so language is more of a finishing touch than the crux of a conversation or search query.
“Language-agnostic AI will power stronger search results — both from external (the internet) and internal (a company database) sources — and less robotic chatbot conversations, enabling companies to lean on automation to reduce resources and strain on staff and truly trust their AI.”
14. Rise in digital twin technology in the enterprise
John Hill, CEO and Founder of Silico, recognises the growing influence digital twin technology is having in the market. Hill predicts that in the next 20 years, there will be a digital twin of every complex enterprise in the world and anticipates the next generation of decision-makers will routinely use forward-looking simulations and scenario analytics to plan and optimise their business outcomes.
“Digital twin technology is one of the fastest-growing facets of industry 4.0 and while we’re still at the dawn of digital twin technology,” he explains. “Digital twins will have huge implications for unlocking our ability to plan and manage the complex organisations so crucial for our continued economic progress and underpin the next generation of Intelligent Enterprise Automation.”
15. Broader tech security
With an exponential amount of data at companies’ fingertips, Tricentis CEO, Kevin Thompson says the need for investment in secure solutions is paramount.
“The general public has become more aware of the access companies have to their personal data, leading to the impending end of third-party cookies, and other similar restrictions on data sharing,” he explains. “However, security issues still persist. The persisting influx of new data across channels and servers introduces greater risk of infiltration by bad actors, especially for enterprise software organisations that have applications in need of consistent testing and updates. The potential for damage increases as iterations are being made with the expanding attack surface.
“Now, the reality is a matter of when, not if, your organisation will be the target of an attack. To combat this rising security concern, organisations will need to integrate security within the development process from the very beginning. Integrating security and compliance testing at the upfront will greatly reduce risk and prevent disruptions.”
16. Increased cyber resilience
Michael Adams, CISO at Zoom, expects an increased focus on cyber resilience over the next 12 months. “While protecting organisations against cyber threats will always be a core focus area for security programs, we can expect an increased focus on cyber resilience, which expands beyond protection to include recovery and continuity in the event of a cyber incident,” explains Adams.
“It’s not only investing resources in protecting against cyber threats; it’s investing in the people, processes, and technology to mitigate impact and continue operations in the event of a cyber incident.”
17. Ransomware threats
As data leaks become increasingly common place in the industry, companies face a very real threat of ransomware. Michal Salat, Threat Intelligence Director at Avast, believes the time is now for businesses to protect themselves or face recovery fees costing millions of dollars.
“Ransomware attacks themselves are already an individual’s and businesses’ nightmare. This year, we saw cybergangs threatening to publicly publish their targets’ data if a ransom isn’t paid, and we expect this trend to only grow in 2023,” says Salat. “This puts people’s personal memories at risk and poses a double risk for businesses. Both the loss of sensitive files, plus a data breach, can have severe consequences for their business and reputation.”
18. Intensified supply chain attacks
Dirk Schrader, VP of security research at Netwrix, believes supply chain attacks are set to increase in the coming year. “Modern organisations rely on complex supply chains, including small and medium businesses (SMBs) and managed service providers (MSPs),” he says.
“Adversaries will increasingly target these suppliers rather than the larger enterprises knowing that they provide a path into multiple partners and customers. To address this threat, organisations of all sizes, while conducting a risk assessment, need to take into account the vulnerabilities of all third-party software or firmware.”
19. A greater need to manage volatility
Paul Milloy, Business Consultant at Intradiem, stresses the importance of managing volatility in an ever-moving market. Milloy believes bosses can utilise data through automation to foresee potential problems before they become issues.
“No one likes surprises. Whilst Ben Franklin suggested nothing can be said to be certain, except death and taxes, businesses will want to automate as many of their processes as possible to help manage volatility in 2023,” he explains. “Data breeds intelligence, and intelligence breeds insight. Managers can use the data available from workforce automation tools to help them manage peaks and troughs better to avoid unexpected resource bottlenecks.”
20. A human AI co-pilot will still be needed
Artem Kroupenev, VP of Strategy at Augury, predicts that within the next few years, every profession will be enhanced with hybrid intelligence, and have an AI co-pilot which will operate alongside human workers to deliver more accurate and nuanced work at a much faster pace.
“These co-pilots are already being deployed with clear use cases in mind to support specific roles and operational needs, like AI-driven solutions that enable reliability engineers to ensure production uptime, safety and sustainability through predictive maintenance,” he says. “However, in 2023, we will see these co-pilots become more accurate, more trusted and more ingrained across the enterprise.
“Executives will better understand the value of AI co-pilots to make critical business decisions, and as a key competitive differentiator, and will drive faster implementation across their operations. The AI co-pilot technology will be more widespread next year, and trust and acceptance will increase as people see the benefits unfold.”
21. Building the right workplace culture
Harnessing a positive workplace culture is no easy task but in 2023 with remote and hybrid working now the norm, it brings with it new challenges. Tony McCandless, Chief Technology Officer at SS&C Blue Prism, is well aware of the role organisational culture can play in any digital transformation journey.
“Workers are the heart of an organisation, so without their buy in, no digital transformation initiative stands a chance of success,” explains McCandless. “Workers drive home business objectives, and when it comes to digital transformation, they are the ones using, implementing, and sometimes building automations. Curiosity, innovation, and the willingness to take risks are essential ingredients to transformative digitalisation.
“Businesses are increasingly recognising that their workers play an instrumental role in determining whether digitalisation initiatives are successful. Fostering the right work environment will be a key focus point for the year ahead – not only to cultivate buy-in but also to improve talent retention and acquisition, as labor supply issues are predicted to continue into 2023 and beyond.”
22. Cloud cover to soften recession concerns
Amid a cost-of-living crisis and concerns over any potential recession as a result, Daniel Thomasson, VP of Engineering and R&D at Keysight Technologies, says more companies will shift data intensive tasks to the cloud to reduce infrastructure and operational costs.
“Moving applications to the cloud will also help organisations deliver greater data-driven customer experiences,” he affirms. “For example, advanced simulation and test data management capabilities such as real-time feature extraction and encryption will enable use of a secure cloud-based data mesh that will accelerate and deepen customer insights through new algorithms operating on a richer data set. In the year ahead, expect the cloud to be a surprising boom for companies as they navigate economic uncertainty.”
23. IoT devices to scale globally
Dr Raullen Chai, CEO and Co-Founder of IoTeX, recognises a growing trend in the usage of IoT devices worldwide and believes connectivity will increase significantly.
“For decades, Big Tech has monopolised user data, but with the advent of Web3, we will see more and more businesses and smart device makers beginning to integrate blockchain for device connectivity as it enables people to also monetise their data in many different ways, including in marketing data pools, medical research pools and more,” he explains. “We will see a growth in decentralised applications that allow users to earn a modest additional revenue from everyday activities, such as walking, sleeping, riding a bike or taking the bus instead of driving, or driving safely in exchange for rewards.
“Living healthy lifestyles will also become more popular via decentralised applications for smart devices, especially smart watches and other health wearables.”
The digital landscape is changing day by day. Ideas like the metaverse that once seemed a futuristic fantasy are now…
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The digital landscape is changing day by day. Ideas like the metaverse that once seemed a futuristic fantasy are now coming to fruition and embedding themselves into our daily lives. The thinking might be there, but is our technology really ready to go meta? Domains and hosting provider, Fasthosts, spoke to the experts to find out…
How the metaverse works
The metaverse is best defined as a virtual 3D universe which combines many virtual places. It allows users to meet, collaborate, play games and interact in virtual environments. It’s usually viewed and accessed from the outside as a mixture of virtual reality (VR), (think of someone in their front room wearing a headset and frantically waving nunchucks around) and augmented reality (AR), but it’s so much more than this…
These technologies are just the external entry points to the metaverse and provide the visuals which allow users to explore and interact with the environment within the metaverse.
This is the ‘front-end’ if you like, which is also reinforced by artificial intelligence and 3D reconstruction. These additional technologies help to provide realistic objects in environments, computer-controlled actions and also avatars for games and other metaverse projects.
So, what stands in the way of this fantastical 3D universe? Here are the six key challenges:
Technology
The most important piece of technology, on which the metaverse is based, is the blockchain. The blockchain is essentially a chain of blocks that contain specific information. They’re a combination of computers linked to each other instead of a central server which means that the whole network is decentralised. This provides the infrastructure for the development of metaverse projects, storage of data and also allows them the capability to be compatible with Web3. Web3 is an upgraded version of the internet which will allow integration of virtual and augmented reality into people’s everyday lives.
Sounds like a lot, right? And it involves a great deal of tech that is alien to the vast majority of us. So, is technology a barrier to widespread metaverse adoption?
Jonothan Hunt, Senior Creative Technologist at Wunderman Thompson, says the tech just isn’t there. Yet.
“Technology’s readiness for the mass adoption of the metaverse depends on how you define the metaverse, but if we’re talking about the future vision that the big tech players are sharing, then not yet. The infrastructure that powers the internet and our devices isn’t ready for such experiences. The best we have right now in terms of shared/simulated spaces are generally very expensive and powered entirely in the cloud, such as big computers like the Nvidia Omniverse, cloud streaming, or games. These rely heavily on instancing and localised grouping. Consumer hardware, especially XR, is still not ready for casual daily use and still not really democratised.
“The technology for this will look like an evolution of the systems above, meaning more distributed infrastructure, better access and updated hardware. Web3 also presents a challenge in and of itself, and questions remain over to what extent big tech will adopt it going forward.”
Storage
Blockchain is the ‘back-end’, where the magic happens, if you will. It’s this that will be the key to the development and growth of the metaverse. There are a lot of elements that make up the blockchain and reinforce its benefits and uses such as storage capabilities, data security and smart contracts.
Due to its decentralised nature, the blockchain has far more storage capacity than the centralised storage systems we have in place today. With data on the metaverse being stored in exabytes, the blockchain works by making use of unutilised hard disk space across the network, which avoids users within the metaverse running out of storage space worldwide.
In terms that might be a bit more relatable, an exabyte is a billion gigabytes. That’s a huge amount of storage, and that doesn’t just exist in the cloud – it’s got to go somewhere – and physical storage servers mean land is taken up, and energy is used. Hunt says: “How long’s a piece of string? The whole of the metaverse will one day be housed in servers and data centres, but the amount or size needed to house all of this storage will beentirely dependent on just how mass adopted the metaverse becomes. Big corporations in the space are starting to build huge data centres – such as Meta purchasing a $1.1 billion campus in Toledo, Spain to house their new Meta lab and data centre – but the storage space is not the only concern. These energy-guzzlers need to stay cool! And what about people and brands who need reliable web hosting for events, gaming or even just meeting up with pals across the world, all that information – albeit virtual – still needs a place to go.
“The current rising cost of electricity worldwide could cause problems for the growth of data centres, and the housing of the metaverse as a whole. However, without knowing the true size of its adoption, it is extremely difficult to truly determine the needed usage. Could we one day see an entire island devoted to data centre storage? Purely for the purposes of holding the metaverse? It seems a little ‘1984’, but who knows?”
Identity
Although the blockchain provides instantaneous verification of transactions with identity through digital wallets, our physical form will be represented by avatars that visually reflect who we are, and how we want to be seen.
The founder of Saxo Bank and the chairman of the Concordium Foundation, Lars Seier Christensen, argues, “I think that if you use an underlying blockchain-based solution where ID is required at the entry point, it is actually very simple and automatically available for relevant purposes. It is also very secure and transparent, in that it would link any transactions or interactions where ID is required to a trackable record on the blockchain.”
Once identity is established, it is true that it could potentially become easier to assess creditworthiness of parties for purchasing and borrowing in the metaverse due to the digital identity and storage of each individual’s data and transactions on the blockchain. However, although it sounds exciting, there must be considerations into how it could impact privacy, and how this amount of data will be recorded on the blockchain.
Security
There are also huge security benefits to this set up. The decentralised blockchain helps to eradicate third-party involvement and data breaches, such as theft and file manipulation, thanks to its powerful data processing and use of validation nodes. Both of these are responsible for verifying and recording transactions on the blockchain. This will be reassuring to many, given the widespread concerns around data privacy and user protection in the metaverse.
To access the blockchain all we will need is an internet connection and a device, such as a laptop or smartphone, this is what makes it so great as it will be so readily available. However, to support the blockchain, we’re relying on a whole different set of technologies. Akash Kayar, CEO of web3-focused software development company Leeway Hertz, had this to say on the readiness of the current technology available: “The metaverse is not yet completely mature in terms of development. Tech experts are researching strategies and
testing the various technologies to develop ideas that provide the world with more feasible and intriguing metaverse projects.
“Projects like Decentraland, Axie Infinity, and Sandbox are popular contemporary live metaverse projects. People behind these projects made perfect use of notable metaverse technologies, from blockchain and cryptos to NFTs.
“As envisioned by top tech futurists, many new technologies will empower the metaverse in the future, which will support the development of a range of prolific use cases that will improve the ability of the metaverse towards offering real-life functionalities. In a nutshell, the metaverse is expected to bring extreme opportunities for enterprises and common users. Hence, it will shape the digital future.”
Currency & Payments
Whilst it’s only considered legal tender in two countries, cryptocurrency is currently a reality and there is a strong likelihood that it will eventually be mass adopted. However, the metaverse is arguably not yet at the same maturity level, meaning cryptocurrency may have to wait before it can finally fully take off.
Golden Bitcoin symbol and finance graph screen. Horizontal composition with copy space. Focused image.
There is no doubt that cryptocurrency and the metaverse will go hand-in-hand as the former will become the tender of the latter with many of the current metaverse platforms each wielding its native currency. For example Decentraland uses $MANA for payments and purchases. However, with the volatility of crypto currencies and the recent collapse of trading platform FTX indicating security lapses, we may not yet be ready for the switch to decentralised payments.
Energy
Some of the world’s largest data centres can each contain many tens of thousands of IT devices which require more than 100 megawatts of power capacity – this is enough to power around 80,000 U.S. households (U.S. DOE 2020) and is equivalent to $1.35bn running cost per data centre with the cost of a megawatt hour averaging $150.
According to Nitin Parekh of Hitachi Energy, the amount of power which takes to process Bitcoin is higher than you might expect: “Bitcoin consumes around 110 Terawatt Hours per year. This is around 0.5% of global electricity generation. This estimate considers combined computational power used to mine bitcoin and process transactions.” With this estimate, we can calculate that the annual energy cost of Bitcoin is around $16.5bn.
However, some bigger corporations are slowly moving towards renewable energy to power their projects in this space, with Google signing close to $2bn worth of wind and solar investments in order to power its data centres in the future and become greener. Amazon has also followed in their footsteps and have become the world’s largest corporate purchaser of renewable energy.
They may have plenty of time yet to get their green processes in place, with Mark Zuckerberg recently predicting it will take nearly a decade for the metaverse to be created: “I don’t think it’s really going to be huge until the second half of this decade at the earliest.”
About Fasthosts
Fasthosts has been a leading technology provider since 1999, offering secure UK data centres, 24/7 support and a highly successful reseller channel. Fasthosts provides everything web professionals need to power and manage their online space, including domains, web hosting, business-class email, dedicated servers, and a next-generation cloud platform. For more information, head to www.fasthosts.co.uk
John MClure, CISO at Sinclair Group – a diversified media company and America’s leading provider of local sports and news – talks about the evolution of cybersecurity and the cultural shift placing it at the forefront of business change
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This month’s cover story explores how Sinclair Broadcast Group is embracing the evolution of cybersecurity and placing the role of the CISO at the forefront of business transformation.
Welcome to the latest issueof Interface magazine!
Communication, secure and at speed, is a vital component of the transformation journey for both the modern enterprise and its relationship with stakeholders, be they customers or partners. Putting the right building blocks in place to deliver successful change management is at the heart of the inspiring stories in the latest issue of Interface.
Our cover star John McClure progressed from a career in the military and work as a consultant in the intelligence industry to fight a new kind of foe… As CISO for Sinclair Broadcast Group, a diversified media company and America’s leading provider of local sports and news, he talks about the evolution of cybersecurity, the battle to meet the rising velocity and sophistication of cyber-attacks and the cultural shift of the role of CISO placing it at the forefront of business change.
“Sinclair is unique in terms of its different business units and how it operates. It’s my job as CISO leading our cyber team not to be an obstacle for the business; we’re here to help it move faster to keep up with market forces, and to move safely. We’re here to engineer solutions that work for the enterprise but also help us maintain a positive security posture.”
State of Florida: digital government services
We also hear from CIO Jamie Grant who is leading the State of Florida’s Digital Service (FL[DS]) on its charge to transform and modernise the way government is accessed and consumed. He is building a team of talented, goal-oriented and customer-obsessed individuals to drive a digital transformation with innovation at its heart. “Leadership is really about developing the team and investing in the people. And it turns out that when you get their backs, they appreciate it and then you can achieve anything.”
ResultsCX: putting people first
Jamie Vernon, SVP for IT & Infrastructure at AI-powered customer experience solution specialist ResultsCX, discusses what drives customer care in the 21st century, and the part technology has to play.
“We are the custodians of our customers’ customers,” says Vernon. “In this increasingly tenuous relationship with their customers, they trust us. My leadership takes that responsibility very seriously, and charges each of us with doing everything we can to provide a perfect call, or email, or chat, every time, thousands of times a minute, around the clock and around the calendar.”
Jamie Vernon, SVP for IT & Infrastructure at AI-powered customer experience solution specialist ResultsCX, discusses what drives customer care in the 21st century, and the part technology has to play.
“We are the custodians of our customers’ customers,” says Vernon. “In this increasingly tenuous relationship with their customers, they trust us. My leadership takes that responsibility very seriously, and charges each of us with doing everything we can to provide a perfect call, or email, or chat, every time, thousands of times a minute, around the clock and around the calendar.”
Also this month, Sarita Singh, Regional Head & Managing Director for Stripe in Southeast Asia, talks about how the fast-growing payments platform is driving financial inclusion across Asia and supporting SMEs with end-to-end services putting users first, and we get expert advice for the modern CEO from the University of Oxford’s Saïd Business School.
Our cover story this month investigates how Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across Consumer Data & Engagement Platforms, and her team are executing Wells Fargo’s strategy to promote personalised customer engagement across all consumer banking channels
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This month’s cover story follows Wells Fargo’s journey to deliver personalised customer engagement across all its consumer banking channels.
Welcome to the latest issue of Interface magazine!
Partnerships of all kinds are a key ingredient for organisations intent on achieving their goals… Whether that’s with customers, internal stakeholders or strategic allies across a crowded marketplace, Interface explores the route to success these relationships can help navigate.
Our cover story this month investigates the strategy behind Wells Fargo’s ongoing drive to promote personalised customer engagement across all consumer banking channels.
Fleur Twohig, Executive Vice President, leading Personalisation & Experimentation across the bank’s Consumer Data & Engagement Platforms, explains her commitment to creating a holistic approach to engaging customers in personalised one-to-one conversations that support them on their financial journeys.
“We need to be there for everyone across the spectrum – for both the good and the challenging times. Reaching that goal is a key opportunity for Wells Fargo and I have the pleasure of partnering with our cross-functional teams to help determine the strategic path forward…”
IBM: consolidating growth to drive value
We hear from Kate Woolley, General Manager of IBM Ecosystem, who reveals how the tech leader is making it easier for partners and clients to do business with IBM and succeed. “Honing our corporate strategy around open hybrid cloud and artificial intelligence (AI) and connecting partners to the technical training resources they need to co-create and drive more wins, we are transforming the IBM Ecosystem to be a growth engine for the company and its partners.”
Kate Woolley, IBM
America Televisión: bringing audiences together across platforms
Jose Hernandez, Chief Digital Officer at America Televisión, explains how Peru’s leading TV network is aggregating services to bring audiences together for omni-channel opportunities across its platforms. “Time is the currency with which our audiences pay us, so we need to be constantly improving our offering both through content and user experiences.”
Portland Public Schools: levelling the playing field through technology
Derrick Brown and Don Wolf, tech leaders at Portland Public Schools, talk about modernising the classroom, dismantling systemic racism and the power of teamwork.
Also in this issue, we hear from Lenovo on how high-performance computing (HPC) is driving AI research and report again from London Tech Week where an expert panel examined how tech, fuelled by data, is playing a critical role in solving some of the world’s hardest hitting issues, ranging from supply chain disruptions through to cybersecurity fears.
Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.
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Robotics play a huge role in the manufacturing landscape today. A growing number of businesses use manufacturing robots to automate repetitive tasks, reduce errors, and enable their employees to focus on innovation and efficiency, causing the entire sector’s impressive growth.
According to data presented by AksjeBloggen.com, the global market value of conventional and advanced robotics in the manufacturing industry is expected to continue rising and hit $18.6bn in 2021, a 40% increase in three years.
Market Value Jumped by $5.4B in Three Years
Robots have numerous roles in manufacturing. They are mainly used for high-volume, repetitive processes where their speed and accuracy offer tremendous advantages. Other manufacturing automation solutions include robots used to help people with more complex tasks, like lifting, holding, and moving heavy pieces.
Companies turn to robotics process automation to cut manufacturing costs, solve the shortage of skilled labor and keep their cost advantage in the market.
In 2018, the global market value of conventional and advanced robotics in the manufacturing industry amounted to $13.2bn, revealed the BCG survey. In 2019, this figure rose to $14.8bn and continued growing. Statistics show the market value of manufacturing robots hit $16.6bn in 2020. This figure is expected to jump by $2bn and hit $18.6bn in 2021.
Conventional robots, like giant industrial robots used in the car industry, are set to reach $14.9bn value this year, up from $12bn in 2018.
The market value of advanced manufacturing robots, which have a superior perception, adaptability, and mobility, tripled in the last three years and is expected to hit $3.7bn in 2021. Combined with big data analytics, advanced manufacturing robots allow companies to make intelligent decisions based on real-time data, which leads to lower costs and faster turnaround times.
The BCG survey also showed most manufacturers believe advanced robotic systems will have a massive role in the factory of the future and plan to increase their use. More than 70% of respondents defined robotics as a significant productivity driver in production and logistics.
European and Asian Companies Lead in the Use of Advanced Manufacturing Robots
Analyzed by regions, European and Asian companies lead in the use of advanced robots, while manufacturers from North America lag behind. However, the survey showed 80% of respondents from the US plan to implement advanced robotics in the next few years.
The survey also revealed that manufacturers in emerging markets, especially China and India, are more enthusiastic about using advanced robots than those in industrialized countries. These companies may be looking to automation as a way to overcome a skilled labor shortage and improve their ability to compete in international markets.
Germany had the largest robot density in the manufacturing industry among European countries, with 346 installations per 10,000 employees in 2019. Sweden, Denmark, and Italy followed with 277, 243, and 212 installations per 10,000 employees, respectively.
Statistics also show that companies in the transportation and logistics and technology sector lead in implementing advanced robotics, with 54% and 53% of manufacturers who already use such solutions. The automotive industry and consumer goods sector follow with 49% and 44% share, respectively.
Manufacturers in the engineered products, process, and health care industries lag behind, with 42%, 41%, and 30% of companies that use advanced manufacturing robots. However, around 85% of manufacturers in these sectors plan to start using advanced robotic systems by 2022.
Gurpreet Purewal, Associate Vice President, Business Development, iResearch Services, explores how organisations can overcome the challenges presented by AI in 2021.
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2020 has been a year of tumultuous change and 2021 isn’t set to slow down. Technology has been the saving grace of the waves of turbulence this year, and next year as the use of technology continues to boom, we will see new systems and processes emerge and others join forces to make a bigger impact. From assistive technology to biometrics, ‘agritech’ and the rise in self-driving vehicles, tech acceleration will be here to stay, with COVID-19 seemingly just the catalyst for what’s to come. Of course, the increased use of technology will also bring its challenges, from cybersecurity and white-collar crime to the need to instil trust in not just those investing in the technology, but those using it, and artificial intelligence (AI) will be at the heart of this.
1. Instilling a longer-term vision
New AI and automation innovations have led to additional challenges such as big data requirements for the value of these new technologies to be effectively shown. For future technology to learn from the challenges already faced, a comprehensive technology backbone needs to be built and businesses need to take stock and begin rolling out priority technologies that can be continuously deployed and developed.
Furthermore, organisations must have a longer-term vision of implementation rather than the need for immediacy and short-term gains. Ultimately, these technologies aim to create more intelligence in the business to better serve their customers. As a result, new groups of business stakeholders will be created to implement change, including technologists, business strategists, product specialists and others to cohesively work through these challenges, but these groups will need to be carefully managed to ensure a consistent and coherent approach and long-term vision is achieved.
2. Overcoming the data challenge
AI and automation continue to be at the forefront of business strategy. The biggest challenge, however, is that automation is still in its infancy, in the form of bots, which have limited capabilities without being layered with AI and machine learning. For these to work cohesively, businesses need huge pools of data. AI can only begin to understand trends and nuances by having this data to begin with, which is a real challenge. Only some of the largest organisations with huge data sets have been able to reap the rewards, so other smaller businesses will need to watch closely and learn from the bigger players in order to overcome the data challenge.
3. Controlling compliance and governance
One of the critical challenges of increased AI adoption is technology governance. Businesses are acutely aware that these issues must be addressed but orchestrating such change can lead to huge costs, which can spiral out of control. For example, cloud governance should be high on the agenda; the cloud offers new architecture and platforms for business agility and innovation, but who has ownership once cloud infrastructures are implemented? What is added and what isn’t?
AI and automation can make a huge difference to compliance, data quality and security. The rules of the compliance game are always changing, and technology should enable companies not just to comply with ever-evolving regulatory requirements, but to leverage their data and analytics across the business to show breadth and depth of insight and knowledge of the workings of their business, inside and out.
In the past, companies struggled to get access and oversight over the right data across their business to comply with the vast quantities of MI needed for regulatory reporting. Now they are expected to not only collate the correct data but to be able to analyse it efficiently and effectively for regulatory reporting purposes and strategic business planning. There are no longer the time-honoured excuses of not having enough information, or data gaps from reliance on third parties, for example, so organisations need to ensure they are adhering to regulatory requirements in 2021.
4. Eliminating bias
AI governance is business-critical, not just for regulatory compliance and cybersecurity, but also in diversity and equity. There are fears that AI programming will lead to natural bias based on the type of programmer and the current datasets available and used. For example, most computer scientists are predominantly male and Caucasian, which can lead to conscious/unconscious bias, and datasets can be unrepresentative leading to discriminatory feedback loops.
Gender bias in AI programming has been a hot topic for some years and has come to the fore in 2020 again within wider conversations on diversity. By only having narrow representation within AI programmers, it will lead to their own bias being programmed into systems, which will have huge implications on how AI interprets data, not just now but far into the future. As a result, new roles will emerge to try and prevent these biases and build a more equitable future, alongside new regulations being driven by companies and specialist technology firms.
5. Balancing humans with AI
As AI and automation come into play, workforces fear employee levels will diminish, as roles become redundant. There is also inherent suspicion of AI among consumers and certain business sectors. But this fear is over-estimated, and, according to leading academics and business leaders, unfounded. While technology can take away specific jobs, it also creates them. In responding to change and uncertainty, technology can be a force for good and source of considerable opportunity, leading to, in the longer-term, more jobs for humans with specialist skillsets.
Automation is an example of helping people to do their jobs better, speeding up business processes and taking care of the time-intensive, repetitive tasks that could be completed far quicker by using technology. There remain just as many tasks within the workforce and the wider economy that cannot be automated, where a human being is required.
Businesses need to review and put initiatives in place to upskill and augment workforces. Reflecting this, a survey on the future of work found that 67% of businesses plan to invest in robotic process automation, 68% in machine learning, and 80% investing in perhaps more mainstream business process management software. There is clearly an appetite to invest strongly in this technology, so organisations must work hard to achieve harmony between humans and technology to make the investment successful.
6. Putting customers first
There is growing recognition of the difference AI can make in providing better service and creating more meaningful interactions with customers. Another recent report examining empathy in AI saw 68% of survey respondents declare they trust a human more than AI to approve bank loans. Furthermore, 69% felt they were more likely to tell the truth to a human than AI, yet 48% of those surveyed see the potential for improved customer service and interactions with the use of AI technologies.
2020 has taught us about uncertainty and risk as a catalyst for digital disruption, technological innovation and more human interactions with colleagues and clients, despite face-to-face interaction no longer being an option. 2021 will see continued development across businesses to address the changing world of work and the evolving needs of customers and stakeholders in fast-moving, transitional markets. The firms that look forward, think fast and embrace agility of both technology and strategy, anticipating further challenges and opportunities through better take-up of technology, will reap the benefits.
With virtually all companies looking at AI, what are some of the key risks they need to consider before implementation?
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Today virtually all companies are forced to innovate and many are excited about AI. Yet since implementation cuts across organisational boundaries, shifting to an AI-driven strategy requires new thinking about managing risks, both internally and externally. This blog will cover “the seven sins of enterprise AI strategies”, which are governance issues at the board and executive levels that block companies from moving ahead with AI. by By Jeremy Barnes, Element AI
1- Disowning the AI strategy
This is probably the most important sin. In this case, a CEO and board will say that AI is a priority, but delegate it to a different department or an innovation lab. However, success is not based on whether or not a company uses an innovation lab—it’s whether they are truly invested in it. The bottom line is that the CEO and board need to actively lead an AI strategy.
2- Ignoring the unknowns
This happens when companies say they believe in AI, but don’t reach a level of proficiency where it’s possible to identify, characterise and model the threats that emerge with new advances. Even if it is decided not to go all-in on AI innovation, it’s still important that there is a hypothesis for how to address AI within a company and an early warning system so the decision can be re-evaluated early enough to act. Being a fast follower requires as much organizational preparation and lead time as leadership.
3- Not enabling the culture
The ability to implement AI is about an experimentation mindset. That and an openness to failure need to be adopted across the company. Organisations need to keep in mind that AI doesn’t respect organisational boundaries. Most companies want high-impact, low-risk solutions that could simply lead to optimising, rather than advancing new value streams. It is hard to accept increased risk in exchange for impact but it will come as part of the continuous cultural enablement of an experimental mindset.
4- Starting with the solution
This is the most common sin. It’s important to be able to understand the specific problems you’re trying to solve, because AI is unlikely to be a solution for all of them, and especially not blindly implementing a horizontal AI platform. Have the conversation at board level to ensure that an overarching AI strategy, and not simply quick-fix solutions, is the priority.
5- Lose risk, keep reward
As mentioned in the third sin, it is natural for companies to want to implement AI without any risk. But there is no reward without risk. A vendor motivated to decrease risk will also decrease innovation and ultimately impact by making successes small and failures non-existent. AI creates differentiation only for companies that are willing to learn from both their successes and their failures. A company that doesn’t effectively balance risk in AI will ultimately increase its risk of disruption.
6- Vintage accounting
Attempting to fit AI into traditional financial governance structures causes problems. It doesn’t fit nicely into budget categories and it’s hard to value the output. The link between what you put in and what you get out can be less tangible or predictable, which often makes it harder to square with existing plans or structures. Model the rate of return on AI activities and all data-related activities. This demands that these activities affect profit (not just loss) and assets (not just liabilities).
7- Treating data as a commodity
The final sin concerns data and its treatment as a commodity. Data is fundamental to AI. If data is poorly handled, it can lead to negative impacts on decision-making. Data should be treated as an asset. The stronger, deeper and more accurate the dataset, the better models that you can train and more intelligent insights you can generate. But, at the same time, when personally identifiable information is stored about customers, it can be stolen, risking heavy penalties in some jurisdictions. You need to build towards data from a use case rather than invest blindly in data centralisation projects. So, now you know what not to do. Here are some of the simple things that you can do to move ahead. First, talk to your board about how long it will take to become an AI innovator, modelling it out, rather than simply discussing it conceptually.
Second, prepare for change and put in place monitoring. AI shifts all the time, so you’ll want to regularly check in to adjust and pivot your strategy. It’s important to develop a basic skill set so you can redo planning exercises with your board. Third, model out risks in both action and inaction. But don’t model them in a traditional approach, which is to push risk down to different business units and then compensate those units for reducing risk rather than managing trade-offs. Instead, view those trade-offs in terms of risks and rewards, and start to think about how you are accounting for the assets and liabilities of AI. Ultimately, you want to start to model what is the actual rate of return for all these activities that you are doing. Then benchmark it against what you see in other companies from across the industry, and that will give you a good picture of the current situation and where to go.
Understanding what it isn’t is just as important as understanding what it is, says Jim Logan who has nearly three decades of experience in financial services and technology…
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I’ve been working in the financial services space for close to thirty years now. I’ve seen many trends and technologies emerge. Some take hold, several are just a flash in the pan. Regardless of how long a concept sticks around, one thing remains: Terminology plays a material role in shaping perceptions. In a world where messaging tends to over complicate things, too many acronyms and too many buzzwords all work against what should be the primary objective: clearly illustrating value. I’ve found this to be equally true when it comes to artificial intelligence or ‘AI’.
Generally speaking, the word artificial doesn’t readily call to mind a positive image, does it? By definition, the word “artificial” has listed meanings of, “insincere or affected” and “made by humans as opposed to happening naturally.” It is the second part of this definition I’d like to explore a bit further.
Artificial Intelligence is, in fact, created by humans. And it isn’t a new fad or concept. Many don’t realize that the term was first coined by John McCarthy, Ph.D. and Stanford computer and cognitive scientist, back in 1955. AI has continued to evolve as a material concept, with practical applications across many industries, ever since.
For financial service professionals, particularly those of us involved with fighting financial crime and preventing money laundering, AI can have tremendous impact and practical application. Before we dive a bit deeper, I feel it’s important to first understand what AI isn’t.
AI is not intended to simply be a digital worker, certainly not within financial services and fighting financial crime. Yes, AI can automate various functions. We’re all familiar with the concept of ‘bots’ and virtual assistants. However, those are rudimentary examples of robotic process automation. True AI is human led and a continuous, instantaneous learning process that drives tangible value. AI is not merely a play to cut costs or replace human capital. Rather, AI enhances the bottom line by keeping compliance staff costs flat in the immediate term and enables our human experts to more appropriately manage their time, by focusing talent on investigations that matter the most.
One of the most valuable aspects of AI, in the context of anti money laundering and compliance, is the speed by which it can be deployed. We’re talking about time to market and time to value in a matter of weeks. Not months, not multiple quarters – simply weeks. But I don’t mean a generic, black box concept. I’m specifically referring to a highly precise, tailored AI solution that has extensive proof points and, more importantly, far-reaching global regulatory approval.
AI shouldn’t simply be an extension of legacy rules-based routines, nor a way to further automate the process of scoring or risk weighted alert suppression. That simply dilutes the true value of AI, and does not maximize the cost and efficiency benefits.
The cost of compliance continues to grow at a staggering pace, particularly for financial institutions and insurance companies. Equally of concern, the impact of fines for non-compliance has also skyrocketed in the last decade. Specifically to the tune of $8.4 billion last year across North America alone.
What if you could literally solve every single name screen, sanction, and transaction alert? What if you could achieve this without sacrificing any aspect of control and security? What if you could increase the throughput, efficiency and accuracy of your compliance operations without adding a single dollar of staff expense to your budget?
Let’s stop talking in terms of what if and have a meaningful conversation regarding how. I’m helping clients achieve all of these measures today and that is from a perspective proven in production. Here at Silent Eight we’re a team founded by engineers and data scientists, solving real world challenges in the anti money laundering and financial compliance market.
Artificial Intelligence isn’t scary…it isn’t a black box…and it isn’t the futuristic world of tomorrow – it is the here and now, and it’s battle tried and tested.
Temenos, the banking software company, partners with Microsoft to offer AI-driven Financial Crime Mitigation solution to help banks combat surge cybercrime during Covid-19 outbreak.
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Temenos, the banking software company, announced today a joint effort with Microsoft to enable access to its AI-powered, Financial Crime Mitigation (FCM) SaaS solution to allow banks to protect both their customers and their organization from financial crime increase during the pandemic, particularly as banks have moved to remote working to protect their staff. Temenos AI-powered, Financial Crime Mitigation SaaS solution based on Microsoft’s fast, scalable and secure Azure cloud platform can be deployed within weeks.
Temenos and Microsoft are opening up access to banks for a 14-day trial, available until 30 of June. As part of the collaboration with Microsoft, Temenos is offering system access and online tutorials for users to familiarize themselves with navigation of the system and learn how it can support them in a revised operating landscape. Temenos unveiled the open access initiative of its FCM software at its virtual event Temenos Community Forum Online, 29-30 April.
Temenos FCM provides enterprise-wide financial crime protection for a highly regulated and fast-changing environment. It allows banks’ operators to respond to alerts and collaborate with team members while working remotely. Throughout the Covid-19 crisis, Temenos customers from Tier 1 banks to regional banks and neobanks have continued to benefit from Temenos FCM’s comprehensive coverage regardless of the fact that their teams are working remotely.
Financial regulators worldwide and organizations such as the European Central Bank are warning that the Covid-19 pandemic may result in an increase in financial crime and other misconduct due to market disruptions, reduced staff, and other factors, as has been the case during past global crises. Opportunistic fraudsters and criminals are adapting their methods of targeting people and countries in distress as new threat vectors open up.
The Financial Actions Task Force (FATF), the global standard setter for combating money laundering and terrorism financing, warns businesses to remain vigilant for emerging money laundering and terrorist financing risks as criminals may seek to exploit gaps and weaknesses in Anti-Money Laundering/Combating the Financing of Terrorism (AML/CFT) systems under the assumption that resources are focused elsewhere. Fraudsters have already been very quick to adapt well-known fraud schemes to target individual citizens, businesses and public organizations. These include various types of adapted versions of telephone fraud schemes, supply scams and decontamination scams.
Jean-Michel Hilsenkopf, Chief Operating Officer, Temenos, said:“We are proud to be able to offer our cloud-native and AI technology to support banks in the fight against financial crime, which has increased as a result of the pandemic. As a strategic global banking software partner of Microsoft, we are pleased to join efforts to deliver Temenos Financial Crime Mitigation as SaaS on Microsoft Azure’s resilient, secure and proven cloud platform. We are committed to providing robust and up-to-date sanction screening, AML, KYC and fraud management protection combined with powerful AI-driven transaction monitoring and sanction screening to help banks worldwide.”
Marianne Janik, Country General Manager, Microsoft Switzerland, said: “We have been pioneering with Temenos in the cloud for a decade. We are proud to join forces to help banks use the power of Temenos’ market-leading Financial Crime Mitigation solution based on our secure, scalable and resilient global Azure cloud platform to combat financial crime surge due to Covid-19.”
More than 200 banks use Temenos FCM SaaS solution, which covers watch-list screening, anti-money laundering, fraud prevention – suspicious activity prevention – and KYC, delivering industry-leading levels of detection and false positives of under 2% vs industry average of 7% and above. Temenos FCM can be deployed as a standalone, or integrated into any banking or payments platform including cloud-native, cloud-agnostic Temenos Transact and Temenos Infinity. It provides unrivalled levels of detection and resilience against financial crime and Total Cost of Ownership (TCO) savings of more than 50%. Temenos FCM provides banks with the next generation of AI-driven FCM capabilities that can run on any public cloud, as a service or on premise.
The global developer of artificial intelligence solutions is releasing a free search platform to help clinical and scientific researchers find answers and patterns in research papers
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Information on COVID-19 is evolving fast and this AI-powered platform leverages a semantic search model that will allow users to quickly connect disparate information. The platform can execute searches based on specific inquiries, along with critical paragraphs copied from a relevant paper. Unlike keyword searches, the queries do not need to be specifically structured, and actually perform better in longer form. This initial version is configured to work with the COVID-19 Open Research Dataset (CORD-19) corpus. Element AI is looking for users and organizations from various groups to test the platform and suggest other data sets and features that could best fit their needs.
The group’s Element AI is looking to work with include:
Clinical researchers who need to incorporate many phenomena to make a rich model of the pandemic and its impacts.
Government, Public Safety and Public Health authorities looking to find best practices across different countries.
Pharmaceutical companies working on new therapies or vaccine trials, as well as identifying existing therapies that could provide immediate help.
-Scientific researchers and data scientists who are working on novel ways to connect research across the body of knowledge already available for COVID-19.
“Research data and reports are being published at an unprecedented pace as organizations scale up their efforts to respond to COVID-19. We want to contribute, and this free platform is our way to help the community locate and gather knowledge to find answers and patterns,” said Jean-François (JF) Gagné, CEO and Co-founder of Element AI. “We encourage the scientific and healthcare community to use this free platform and engage with our team to quickly ramp up and collaboratively meet the needs of the people working to slow down and contain COVID-19. We hope that their feedback and collaboration will help us quickly add features and datasets on top of what we already have made available” added Gagné.
The COVID-19 platform leverages technology from the Element AI Knowledge Scout product, which uses natural language techniques to tap into structured and unstructured sources of information. The first version will be progressively updated in coming weeks as additional datasets emerge. The site can be accessed at: https://www.elementai.com/covid-research.
Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA Srikar Reddy, Managing Director and Chief…
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Mauro Guillén Zandman, Professor of International Management, The Wharton School, University of Pennsylvania, USA
Srikar Reddy, Managing Director and Chief Executive Officer, Sonata Software Limited and Sonata Information Technology Limited
Artificial intelligence (AI) relies on big data and machine learning for myriad applications, from autonomous vehicles to algorithmic trading, and from clinical decision support systems to data mining. The availability of large amounts of data is essential to the development of AI. But the scandal over the use of personal and social data by Facebook and Cambridge Analytica has brought ethical considerations to the fore. And it’s just the beginning. As AI applications require ever greater amounts of data to help machines learn and perform tasks hitherto reserved for humans, companies are facing increasing public scrutiny, at least in some parts of the world. Tesla and Uber have scaled down their efforts to develop autonomous vehicles in the wake of widely reported accidents. How do we ensure the ethical and responsible use of AI? How do we bring more awareness about such responsibility, in the absence of a global standard on AI?
The ethical standards for assessing AI and its associated technologies are still in their infancy. Companies need to initiate internal discussion as well as external debate with their key stakeholders about how to avoid being caught up in difficult situations.
Consider the difference between deontological and teleological ethical standards. The former focuses on the intention and the means, while the latter on the ends and outcomes. For instance, in the case of autonomous vehicles, the end of an error-free transportation system that is also efficient and friendly towards the environment might be enough to justify large-scale data collection about driving under different conditions and also, experimentation based on AI applications.
By contrast, clinical interventions and especially medical trials are hard to justify on teleological grounds. Given the horrific history of medical experimentation on unsuspecting human subjects, companies and AI researchers alike would be wise to employ a deontological approach that judges the ethics of their activities on the basis of the intention and the means rather than the ends.
Another useful yardstick is the so-called golden rule of ethics, which invites you to treat others in the way you would like to be treated. The difficulty in applying this principle to the burgeoning field of AI lies in the gulf separating the billions of people whose data are being accumulated and analyzed from the billions of potential beneficiaries. The data simply aggregates in ways that make the direct application of the golden rule largely irrelevant.
Consider one last set of ethical standards: cultural relativism versus universalism. The former invites us to evaluate practices through the lens of the values and norms of a given culture, while the latter urges everyone to live up to a mutually agreed standard. This comparison helps explain, for example, the current clash between the European conception of data privacy and the American one, which is shaping the global competitive landscape for companies such as Google and Facebook, among many others. Emerging markets such as China and India have for years proposed to let cultural relativism be the guiding principle, as they feel it gives them an edge, especially by avoiding unnecessary regulations that might slow their development as technological powerhouses.
Ethical standards are likely to become as important at shaping global competition as technological standards have been since the 1980s. Given the stakes and the thirst for data that AI involves, it will likely require companies to ask very tough questions as to every detail of what they do to get ahead. In the course of the work we are doing with our global clients, we are looking at the role of ethics in implementing AI. The way industry and society addresses these issues will be crucial to the adoption of AI in the digital world.
However, for AI to deliver on its promise, it will require predictability and trust. These two are interrelated. Predictable treatment of the complex issues that AI throws up, such as accountability and permitted uses of data, will encourage investment in and use of AI. Similarly, progress with AI requires consumers to trust the technology, its impact on them, and how it uses their data. Predictable and transparent treatment facilitates this trust.
Intelligent machines are enabling high-level cognitive processes such as thinking, perceiving, learning, problem-solving and decision-making. AI presents opportunities to complement and supplement human intelligence and enrich the way industry and governments operate.
However, the possibility of creating cognitive machines with AI raises multiple ethical issues that need careful consideration. What are the implications of a cognitive machine making independent decisions? Should it even be allowed? How do we hold them accountable for outcomes? Do we need to control, regulate and monitor their learning?
A robust legal framework will be needed to deal with those issues too complex or fast-changing to be addressed adequately by legislation. But the political and legal process alone will not be enough. For trust to flourish, an ethical code will be equally important.
The government should encourage discussion around the ethics of AI, and ensure all relevant parties are involved. Bringing together the private sector, consumer groups and academia would allow the development of an ethical code that keeps up with technological, social and political developments.
Government efforts should be collaborative with existing efforts to research and discuss ethics in AI. There are many such initiatives which could be encouraged, including at the Alan Turing Institute, the Leverhulme Centre for the Future of Intelligence, the World Economic Forum Centre for the Fourth Industrial Revolution, the Royal Society, and the Partnership on Artificial Intelligence to Benefit People and Society.
But these opportunities come with associated ethical challenges:
Decision-making and liability: As AI use increases, it will become more difficult to apportion responsibility for decisions. If mistakes are made which cause harm, who should bear the risk?
Transparency: When complex machine learning systems are used to make significant decisions, it may be difficult to unpick the causes behind a specific course of action. Clear explanations for machine reasoning are necessary to determine accountability.
Bias: Machine learning systems can entrench existing bias in decision-making systems. Care must be taken to ensure that AI evolves to be non-discriminatory.
Human values: Without programming, AI systems have no default values or “common sense”. The British Standards Institute BS 8611 standard on the “ethical design and application of robots and robotic systems” provides some useful guidance: “Robots should not be designed solely or primarily to kill or harm humans. Humans, not robots, are the responsible agents; it should be possible to find out who is responsible for any robot and its behaviour.”
Data protection and IP: The potential of AI is rooted in access to large data sets. What happens when an AI system is trained on one data set, then applies learnings to a new data set?
Responsible AI ensures attention to moral principles and values, to ensure that fundamental human ethics are not compromised. There have been several recent allegations of businesses exploiting AI unethically. However, Amazon, Google, Facebook, IBM and Microsoft have established a non-profit partnership to formulate best practices on artificial intelligence technologies, advance the public’s understanding, and to serve as a platform about artificial intelligence.
Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of…
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Peltarion, leading AI innovator and creator of an operational deep learning platform, today announced the findings of a survey of AI decision-makers examining what they see as the impact of the skills shortage, and suggestions on how to overcome it. The research, ‘AI Decision-Makers Report: The human factor behind deep learning’, presents the findings of a survey of 350 IT leaders in the UK and Nordics with direct responsibility for shepherding AI at companies with more than 1,000 employees.
The
report finds that many AI decision-makers are concerned about the business
impact of the deep learning skills shortage. 84% of respondents said their
company leaders worry about the business risks of not investing in deep
learning, with 83% saying that a lack of deep learning skills is already
impacting their ability to compete in the market. These companies are exclusively
focusing on recruiting data scientists (71% of AI decision-makers are actively
recruiting to plug the deep learning skills gap), and this is already impacting
their ability to progress with AI projects:
Almost half (49%) say the skills shortage is causing delays to projects
44% believe the need for specialist skills is a major barrier to further investment in deep learning
However, almost half (45%) say they are struggling to hire because they don’t have a mature AI program already in place
“This
report shows that companies can’t afford to wait for data science talent to
come to them to progress their AI projects. The fact is, many organisations are
already starting to lose their competitive edge by waiting for specialised data
scientists. The current approach, which relies on hiring an isolated team of
data scientists to work on deep learning projects, is delaying projects and
putting strain on the talent companies do have,” explains Luka Crnkovic-Friis,
Co-Founder and CEO at Peltarion. “In order to solve the deep learning skills
gap, we need to make use of transferrable talent that can be found right under
companies’ noses. Deep learning will only reach its true potential if we get
more people from different areas of the business using it, taking pressure off
data scientists and allowing projects to progress.”
Less
than half (48%) of respondents said they currently employ data scientists who
can create deep learning models, compared to 94% that have data scientists who
can create other machine learning models. This shortage is having a direct
impact on teams: 93% of AI decision-makers say their data scientists are
over-worked to some extent because they believe there is no one else who can
share the workload. However, with the right tools, others can make a serious
impact on AI projects.
“Organisations
need to move projects forward by bringing on existing domain experts and
investing in tools that will help them input into AI projects. This will reduce
the strain on data scientists and lower deep learning’s barrier to
entry,” concludes Crnkovic-Friis. “We need to make deep learning more
affordable and accessible to all by reducing its complexity. By
operationalising deep learning to make it more scalable, affordable and
understandable, organisations can put themselves on the fast track and use deep
learning to optimise processes, create new products and add direct value to the
business.”
AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5…
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AI is no longer science-fiction writers dream, it’s being implemented in industries all over the world. We look at 5 examples of how AI is revolutionising the retail experience Written by: Dale Benton
Marks and
Spencer
In early 2019, M&S announced a new
Technology Transformation Program, one that will allow M&S to become a
digital-first business and deliver key improvements in customer experience. As
part of this transformation, M&S has partnered with Microsoft to investigate
and test the capabilities of technology and artificial intelligence in a retail
environment. M&S will look to integrate machine learning, computer vision
and AI across every endpoint – both in its stores and behind the scenes. Every
surface, screen and scanner in its stores will create data – and enable
employees to act upon it. Every M&S store worldwide will be able to track,
manage and replenish stock levels in real time – and deal with unexpected
events.
The John Lewis Partnership is currently
partaking in a three-year trial, deploying robots to one of its farms, which
grows produce for its Waitrose & Partners brand. The robots, named Tom, Dick and Harry, are delivered
in partnership with the Small Robot Company. Each will be equipped with a
camera and AI technology to gather topographical data, while autonomously
obtaining accurate, plant-by-plant data in order to enable higher farming
efficiency. The data will also be used
to develop further machine learning capabilities. The trial will also provide
the John Lewis Partnership’s Room Y innovation team with valuable insight to
support innovation and inform how robotics and Artificial Intelligence (AI)
could be used further in other areas of the business.
One of the biggest retail companies in the world has been piloting and implementing artificial intelligence solutions across its stores for a number of years. As part of a technology program, called Missed Scan Detection, Walmart has deployed AI-equipped cameras in more than 1,000 of its stores. These cameras, developed in part with Everseen, tracks and analyses activities at both self-checkout registers and those manned by Walmart employees. If an item isn’t scanned at checkout, the cameras will detect the and notify a checkout attendant of the problem. The AI technology allows Walmart to monitor its inventory product quantities, but also significantly reduce theft across its stores.
Amazon Go represents a whole n era of shipping.
The concept is simple, walk into an Amazon Go store, pick up whatever you want
and walk back out. The idea is to create
a “Just Walk Out” experience. Described as the “most advanced shopping
technology”, customers simply download the Amazon Go app. Powerful machine
learning and AI technology automatically detects when products are taken from
or returned to the shelves, keeping track of them all in a virtual cart. Once
customers leave, Amazon will collate all of the data and produce a receipt and
charge the customer’s Amazon account.
One of the UK’s largest food retailers with more
than 120,000 colleagues in 494 stores serving over 11 million customers every
week, Morrisons turned its attention to AI with JDA Software. Looking to vastly
improve the customer experience, Morrisons looked at reducing queues at
checkouts, and improving on-shelf availability. Morrisons
invested in Blue Yonder – a Demand Forecast & Replenishment solution from JDA,
which uses Artificial Intelligence (AI) technology to improve demand planning
and reinvigorate replenishment based on customer behaviour in every store. Over
a 12-month period, Morrisons was able to generate up to 30% reduction in shelf
gaps and a 2-3 day reduction in stockholding in-store. AI technology has also
enabled Morrisons to close the execution gap, optimizing availability while
reducing wastage, enhancing shelf presentation and meeting stockholding
targets.
By Craig Summers, Managing Director, Manhattan Associates Customer experience can be make or break for retailers. In fact, recent research…
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By Craig Summers, Managing Director, Manhattan Associates
Customer experience can be make or break for retailers. In fact, recent research shows that flawed customer experiences could be costing British retailers up to £102 billion in lost sales each year. This shouldn’t be news to retailers; the modern consumer demands a connected, consistent experience that is personalised to them, whether it’s online or instore. The same research found that running out of stock in-store was the biggest contributor to lost revenue, with 79 per cent of consumers saying they would not return to make a purchase if they found their desired item was out of stock. This frustration is only amplified if an out of stock product is marketed to the consumer.
Personalisation isn’t anything new but if the basics aren’t right, retailers risk not delivering on customer experience. Many retailers still aren’t getting it right – and, explains Craig Summers, Managing Director, Manhattan Associates, inept personalisation is affecting the bottom line.
Misplaced Personalisation
The way in which retailers can engage with customers has changed radically over the past decade, from social media onwards. Add in the compelling appealing of Artificial Intelligence (AI) and the promise of incredibly accurate and timely promotional offers, and personalisation has become a foundation of any retail strategy. Yet while the marketing activity is becoming ever more sophisticated, personalisation cannot be delivered by marketing alone.
Without integrating marketing activity to the core operation, retailers risk repelling rather than engaging customers. Product offers that are out of stock in the customer’s size. Promotions not on offer at the local store. Incentives to buy an item the customer has already purchased – not a problem for a standard food or household item, incredibly annoying if it’s an expensive mountain bike or cashmere jumper. Customers are becoming increasingly familiar with ostensibly personalised offers that fail to deliver a great experience.
What is the thinking behind a promotion that cannot be purchased by the customer? Why set such high expectations when they cannot be met? Enticing a customer to click through an emailed offer may be the measure of marketing success – but when that customer is unable to make a purchase because the desired item is not available in his or her size, that is at least one lost sale and a bottom line retail failure.
Complete Experience
Are retailers listening to what their customers want from personalisation? Great personalised offers will not deliver any value if they are not linked to the rest of the business. Smart technologies, such as AI, without any doubt have a role to play in delivering personalisation – but they are not the foundation. The foundation is getting the basics right. It is ensuring that when a customer wants to buy a product – online or instore – it is available. It is about providing Store Associates with the ability to track stock anywhere in the supply chain, reserve it for a customer to try on instore or have it sent direct to their destination of choice. It is about combining stock availability information with customer insight to make intelligent suggestions, both instore and via marketing promotions.
Bottom line success is, essentially, about the quality of the interaction. And that means considering not just the accuracy of the promotional offer but the complete customer experience. What is achievable today? What can be done well? If a product is being promoted to an individual, is it available in the right size? Is it available locally, or only in flagship outlets? It is these disconnected experiences that are fundamentally undermining customer experience and brand value.
Conclusion
The future of customer personalisation is incredibly exciting. AI promises the ability to predict a customer’s desires before the customer. Fabulous. But only fabulous if that product is available to buy, at a time and place to suit that individual. Right now personalisation is about the retailer; it is about being clever with promotions. It needs to be about the customer; it needs to be about delivering the quality of experience that drives sales.
Retailers need to go back to basics: use technology to recreate the ‘corner shop model’ of the past, at scale. By creating a truly immersive experience for their customers, retailers can find a way to make personalisation profitable again.
The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with…
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The uptake of artificial intelligence by industry will drastically change the UK job market in the coming years – with 133 million new jobs expected to be created globally.
In the UK alone, up to a third of jobs will be automated or likely to change as a result of the emergence of AI – impacting 10.5 million workers.
Ollie Sexton, Principal at Robert Walters comments:
“As businesses become ever more reliant on AI, there is an increasing amount of pressure on the processes of data capture and integration. As a result, we have seen an unprecedented number of roles being created with data skill-set at their core.
“Our job force cannot afford to not get to grips with data and digitalisation. Since 2015 the volume of data created worldwide has more than doubled – increasing (on average) by 28% year-on-year.
“Now is the perfect time to start honing UK talent for the next generation of AI-influenced jobs. If you look at the statistics in this report we can see that demand is already rife, what we are at risk of is a shortage of talent and skills.”
Demand for Data Professionals
IT professionals dedicated to data management appear to be the fastest growing area within large or global entities, with volumes increasing ten-fold in three years – an increase in vacancies of 160% since 2015.
More generally speaking, data roles across the board have increased by 80% since 2015 – with key areas of growth including data scientists and engineers.
What has been the most interesting to see is the emergence of data scientist as a mainstream profession – with job vacancies increasing by a staggering 110% year-on-year. The same trend can be seen with data engineers, averaging 86% year-on-year job growth.
Professional Services Hiring Rapidly
The rise of cybercrime has resulted in professional services – particularly within banking and financial services – hiring aggressively for information security professionals since 2016, however since then volumes have held steady.
Within professional services, vacancies for data analysts (+19.5%), data manager (+64.2%), data scientist (+28.8), and data engineer (+62%) have all increased year-on-year.
Top Industries Investing in AI
Agriculture
Business Support
Customer Experience
Energy
Healthcare
Intellectual Property
IT Service Management
Manufacturing
Technical Support
Retail
Software Development
Tom Chambers, Manager – Advanced Analytics and Engineering at Robert Walters comments:
“The uptake of AI across multiple industries is bringing about rapid change, but with that opportunity.
“Particularly, we are seeing retail, professional services and technology industries’ strive to develop digital products and services that are digitally engaging, secure and instantaneous for the customer – leading to huge waves of recruitment of professionals who are skilled in implementing, monitoring and gaining the desired output from facial recognition, check-out free retail and computer vision, among other automation technologies.
“Similarly, experimental AI is making huge breakthroughs in the healthcare industry, with the power to replace the need for human, expert diagnoses.
“What we are seeing is from those businesses that are prepared to invest heavily in AI and data analytics, is they are already outperforming their competitors – and so demand for talent in this area shows no signs of wavering.”
In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence…
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In a world awash with a seemingly never-ending list of technology buzzwords such as automation, machine learning and Artificial Intelligence (AI) to name a few, AI is one such technology that is moving away from simple hype and stepping closer to reality in procurement.
Here, CPOstrategy looks at 5 ways in which AI is being utilised in procurement…
Procurement, by its very nature, is tasked with handling huge quantities of spend and with spend comes spend data. Often described by leading CPOs as a repetitive task, understanding and sorting that spend data is now being achieved through the implementation of AI.
Through the use of AI, procurement teams can remove human error, increase efficiency and realise greater value from spend data.
Chatbots
One of the biggest ways in which AI is being implemented around the world is in the customer interaction space. In telcos, for example, customer support can now be handled via a highly developed AI chatbot that uses legacy data and context to provide real-time, and unique, solutions for customers.
In procurement, chatbots follow a similar path for both internal and external customers. With tailored and context-aware interactions, chatbots create an omni-channel user experience for all stakeholders in the procurement ecosystem.
Supplier risk identification
Procurement and risk go hand in hand and one of the biggest risks is identifying and working with the right partner. Working in partnerships, which ultimately proves to be a failure, can be extremely costly and so AI is now being used to reduce the risk of failure.
Machine Learning technology, powered by AI, captures and analyses large quantities of supplier data, including their spend patterns and any contract issues that have emerged in previous partnerships, and creates a clearer picture of a supplier in order for the procurement teams to be able to identify whether this particular partner is right for them – without spending a penny.
Benchmarking efficiency
Benchmarking is key to any organisation’s ambition to measure and continuously improve its processes, procedures and policies. In procurement, organisations such as CIPS are used as examples of best practice in which procurement functions all over the world can benchmark against and identify any gaps.
Similar to supplier risk identification, AI can be implemented within ERP systems to analyse the entirety of data that passes through procurement and present this key data in easy to digest formats.
Examples include data classification, cluster analysis and semantic data management to help identify untapped potential or outliers in which procurement teams can improve their processes.
Purchase order processing/Approving purchasing
Procurement has evolved from its traditional role as simply managing spend into a strategic driver for a number of organisations all around the world.
As the role of the CPO has changed, technology such as AI has been implemented to free up their time from the menial tasks (such as PO processing and approving purchases), allowing them to spend more time in areas of growth.
AI software can be used to automatically review POs and match them to Goods Receipt Notes as well as combining with Robotics Process Automation (RPA) to capture, match and approve purchases through the use of contextual data. This contextual data allows AI to identify and make decisions based on past behaviour.
By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company Now, more than ever, agility is the currency…
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By Robert Douglas, Europe Planning Director at Adaptive Insights, a Workday company
Now,
more than ever, agility is the currency of success. And while agility may be
about responding intelligently to the changing nature of the marketplace, those
responses must be rooted in a plan. Today, many organizations leverage newer
technologies in the cloud for planning, having moved away from manual
spreadsheets. And while the cloud offers greater collaboration and the ability
to easily combine both historical and real-time data, it’s just the beginning. Digital
transformation is changing and will continue to change the definition of best
practice planning in organisations. As such, the next step for business planning
revolves around two key areas—advancements in AI and machine learning, and
increased automation.
The power of ‘what if’
What-if scenarios are already incredibly
powerful for strategic decision-makers. Organisations can model different
versions of the future based on historical information and predictive analytics
before choosing the best path forward. Consolidating executional data within
organisations is the first step in capitalising on future AI opportunities. However,
there is a lot more to come. In fact, compared with what AI is going to make
possible, scenario planning is still in its infancy.
Today’s scenario planning is a good proof of
concept, but as long as humans are driving the creative process—it relies on
people to ask the right questions of the right data—what-if planning is going
to be constrained by available resources. The most advanced decision-making
today is typically supported by a few best-estimate scenarios—maybe four or
five at most. However, in truth, there are many more possible futures to
potentially prepare for, and what looks like best practice now is going to seem
vastly limited in scope before too long.
As the volume and variety of available data
grows, and access to that data gets easier, AI and machine learning algorithms
will make it possible to drill down, consolidate, and leverage incredibly
granular information at the highest levels.
AI and machine
learning use cases
To consider how these AI and machine learning
algorithms will work, let’s look at a use case of a CEO aiming to achieve a 40
percent growth target over a two-year period and wants to model what that looks
like to present at the annual executive offsite. AI and machine
learning-enabled planning could help to quickly and automatically find the
optimal growth path, while accommodating any conditions and assumptions on the fly.
Essentially, the planning system could measure
historical performance and recommend a market segment mix strategy, along with
the associated budget increases in the specific marketing and sales activities
needed to support it. If they then decide they need to cap growth in sales to
smaller businesses in order to also expand into enterprises and international
markets—while also maintaining expenses at a certain increase—an alternative,
optimised model could be quickly created without any manual lifting.
A future with machine
learning
The future of business planning is not just
about thinking bigger—it is about making better decisions and operationalising
them faster. That’s where machine learning comes in. Increased automation,
driven by algorithms, is going to blur the boundaries between planning,
execution, and analysis until planning cycle times have all but evaporated.
Planners will be able to ask deep, complex strategy questions and see the results modelled in real time. As the data becomes more trusted, they will be able to make significant, informed, “just-in-time” decisions, confident in the patterns surfaced in the data. And as the line between planning and transactions systems begins to blur and disappear, plans will automatically cascade down to operational departments—even down to individual workflows—in real time.
‘Strategy’ will become the province of human-driven
innovation while planning becomes an organic, ongoing exercise of continuous
improvement inextricably linked to the transactional systems that execute
plans.
Leading the change
Today finance acts as the central junction within business planning and is, therefore, a natural steward for change, helping normalise new habits and behaviours for the rest of the organisation. As such, there is a strong case to be made for finance teams to double down on their new position as stewards of change by acting as transformation leaders—both for existing processes, and for future, unknown developments.
Finance’s role will change significantly in
order to leverage technology developments in the data-driven, AI future.
Driving collaboration with business partners, breaking down data silos, and
embracing new technologies and processes to keep pace with today’s rapidly
changing business environment will be key. The result will be an augmented,
intelligent planning process that delivers true business agility.
Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion…
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Everyone wants to implement Artificial Intelligence (AI) and
Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7
trillion in GDP by globally 2030, and as this market grows, AI and BI will
shift from industry buzzwords, to key market differentiators, before eventually
becoming the new normal in the corporate landscape.
Yet
bringing AI and BI on board is a big leap if it’s your first major data
project. Stibo Systems’ Claus Jensen, Head of Emerging Technology, comments
on the role of MDM as a vital foundation to implement emerging data technology.
Most CEOs
don’t trust their own data.*
Let that
sink in for a moment.
Almost
every business is looking to data solutions to fuel the next phase of growth
and innovation. AI and BI are firmly on the agenda, yet a report by Forbes
Insights and KPMG found 84% of CEOs are concerned with the quality of the data
they’re basing their decisions on.
That’s a
significant disconnect. Businesses at board level want to implement ‘next
generation’ data projects, but don’t trust
the data that will be fed into them. For CDOs and other data leads, this
presents a difficult situation. They need to meet demand for cutting-edge data
projects, knowing that there is a certain level of mistrust in the data at
their disposal.
For many
CDOs, that mistrust isn’t limited to the CEO. Think about the data you are
currently processing: how confident are you that it’s being accurately sourced,
entered, saved, stored, copied and presented? How well do you know that data
journey once it leaves your sphere of control? Are you certain that a
single source of truth is being maintained?
The
data gold rush
It may only
be major data breaches that make the headlines, but in the global gold rush for
data, too many businesses fail to accurately extract, store and interpret data.
Mistakes
are made at every stage in the process – in fact, so bad are we at processing
data, a report by Royal Mail Data Services claims that around 6% of annual
revenue is lost through poor quality data.
It’s
equally bleak in the US, where Gartner’s Data Quality Market Survey puts the
average cost to US business at $15 million per year.
Despite
this, we’re rapidly moving the conversation from data capture to artificial
intelligence (AI), business intelligence (BI) and connected devices (IoT) – and
for good reason.
Putting
aside the issue of bad data (we’ll come back to that), businesses now have
access to more data than they can handle – according to SAS’ Business
Intelligence and Analytics Capabilities Report, 60% of business leaders
struggle to convert data into actionable insights, and 91% of companies feel
that they are incapable to doing it quickly enough to make useful
changes.
Business
Intelligence and Analytics Capabilities Report
In large
businesses, where data streams are blended from many sources, machine learning
can help data scientists monitor figures to flag outliers, irregularities and
noteworthy patterns.
Once
flagged, business leaders can use BI to bring those patterns to life, helping
pave the way for the most appropriate, and profitable, action.
Stibo Systems’ Head of
Emerging Technology, Claus Jensen, believes it’s only a matter of time before
we see AI regularly used within business product features – with machine
learning automating tasks thanks to effective data interpretation.
Jensen and
his team are working at the forefront of data: building master data management
solutions in conjunction with AI and BI. “We’re entering into a new era of data
analytics,” says Jensen. “Data scientists aren’t going away, but they can do
more and more high-level work as certain use cases are solved by AI.”
One of
these use cases is machine learning-based auto classification. “For retailers
onboarding thousands and thousands of new products every month, it’s really
time consuming for them to have the vendor categorise the product into the
vendor taxonomy.
“Machine
learning can automate this based on product description and image.”
Running
before we can walk
As exciting
as this sounds, businesses eager to install new uses for data often face
significant challenges: their data isn’t watertight, or it’s siloed, often
both.
In a piece
penned for the Financial Times, Professor of Economics at Stanford Graduate School
of Business, Paul Oyer, wrote: “Smart managers now know that algorithms are as
good as the data you train them on.” In other words, AI (and analytics for that
matter) can only ever be as good as the date you feed it.
Which
brings us back to the question of trust. What needs to happen for CEOs to trust
their own data?
While
there’s no single answer to this question, a master data management (MDM)
solution is a good place to start.
“You can
think of MDM as the foundation, a layer, that provides a single source of the
truth for data,” explains Jensen. “Analytics and machine learning is only
useful if the data you’re working on is accurate. That’s where MDM comes in; it
ensures information presented, and actions taken, are based on fact and
reality.
“Otherwise,
business analytics is just a nice and colourful way to look at bad data, and
what’s the point in that?”
In today’s market expectations are growing and the stakes are high, with one mistake potentially costing a retailer their reputation….
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In today’s market expectations are growing and the stakes are
high, with one mistake potentially costing a retailer their reputation. Due to
this level of risk, brands find reducing their hands on approach to processes
difficult, but what they don’t realise is that technology such as Artificial
Intelligence and Machine Learning could prove to be their hero, not their
villain. Entrusting their data and brand values to such technologies may seem
like a scary step, but as David Griffiths, Senior Product Marketing &
Strategy Manager, Adjuno, discusses, it’s one that will free up
retail teams to add value and cut costs.
In AI
should we trust?
There is a great deal of obstacles to overcome when it comes to the stigma attached to AI. A key challenge facing the progression of this technology is that individuals simply do not trust it. The fear of the unknown is one concern that pops up most commonly, with people battling a perceived perception that those who use this technology will lack control.
But a new age
of retail is approaching and there is now an even greater need for brands to
define their processes in order to keep up. Consumers want to receive products
that are of a high-quality and they want to receive them now. These
expectations are taking us beyond the traditional methods of retailing and
leading us into a world immersed in technology, a world that benefits from the
helping hand of AI.
Informing
key decisions
With AI,
retailers will be able to gain valuable insights in warehouse management,
logistics and supply chain management, and make more informed and proactive
decisions. This technology makes it easier to analyse huge volumes of data in
an efficient fashion, helping to detect patterns and providing an endless loop
of forecasting. Using this knowledge to identify factors and issues impacting
the performance of the supply chain, such as weather events, retailers will be
able to take a forward-thinking approach to decision-making. An approach that
will lead to reduced costs and delays.
By extending
human efficiency in terms of reach, quality and speed, this technology can also
help to eliminate the more mundane and routine work that’s faced by employees
across the retail spectrum. From tackling flow management by assessing key
products to ensuring there is enough stock available to improving production
planning, a more informed use of time will help equip brands to face every
consumer request and demand.
This is
particularly important for those brands whose product line extends further than
apparel wear, and steps into the realm of hardware. With diversity comes a need
for more proof points and in turn, an extended volume of data. Retailers will
be battling to work across an even greater number of suppliers and distribution
centres, and accommodating the expectations of a larger customer base.
Considering this, it is fundamental that every last bit of data is refined and
utilised to streamline processes. AI is providing retailers with a platform to
do this, offering the potential for significant changes across the entire
product journey.
A data
conundrum
The benefits of
using AI to consolidate data are endless. Traditionally, teams have relied on
spreadsheets to collate information, hindering their ability to forward plan.
With AI this is no longer the case, a much more accurate picture of the hero
products, sizes and colours likely to sell, can be achieved by looking at
multiple scenarios in real time and pulling them together.
This doesn’t
mean that AI will replace creative buying teams. AI doesn’t forecast trends, it
can’t predict what consumers will be buying in 2020, it can only report on the
product lines. It can however help buying teams assess partners, analyse stock
patterns, track costs, enable capacity planning and help optimise shipments.
This data is invaluable to teams, especially for any new buyers who may need
extra guidance.
Conclusion
AI is set to
transform the retail scene as we know it. But in order to make implementation a
success, there shouldn’t just be a focus on the evolution of data management,
there must be an evolution of mindsets too. After all, if a retailer fails to
jump on board with AI and embrace a new era of change, then their customers
will be the ones who suffer.
Companies that use voice plus touch interactions with their products and services are actually seen as less trustworthy and less…
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Companies that use voice plus touch interactions
with their products and services are actually seen as less trustworthy and less
engaging by their users, according to new research from emlyon business school.
The research, conducted by Margherita Pagani,
Director of the AIM Research Center on AI in Value Creation and Professor of
Digital Marketing at emlyon business school, and colleagues from ESSCA School
of Management and Florida State University, College of Business, analysed the
impact and differences between ‘touch’ interaction and ‘touch and voice’
interaction on personal consumer engagement and brand trust.
The researchers created two separate experiments,
focused on a utilitarian product and then a hedonic product, both of which had
over 90 participants belonging to generation Y, which is commonly equipped with
the latest smartphones and frequently use them for business interactions. For
both experiments, participants had to interact with the brand using their
smartphone including a phone call to the company to ask a specific set of
questions.
One group was required to interact with the brand
through the smartphone using a touch-only interaction, and the other used both
touch and voice interaction – either Apple’s Siri or OK Google. After
interacting with the company, participants were asked to rate their customer
experience. The participant’s answers were then measured to evaluate personal
engagement with the tasks, their level of trust with the brand and their
privacy concerns.
The researchers found that participants who used
the touch-only interaction experienced a much higher level of personal
engagement with the brand compared to those who used the touch plus voice
interaction.
Prof. Pagani says,
“Many companies have introduced new AI products
that use voice-activation such as Amazon’s Alexa, Google’s Home Assistant or
Apple’s Siri. These have been introduced in order to increase customer
experiential engagement, stimulate the interaction and collect more data that
allow to better personalise the experience through machine learning.
However, our study shows that in the initial phase of adoption, adding
voice recognition actually has the opposite desired effect. Even if voice may
be considered as a way to develop a much more natural interaction, the level of
cognitive efforts required to the brain using two sensory modes (voice and touch)
are higher. Therefore, consumers find it harder to completely engage with the
product and can easily be distracted”.
The researchers also found that participants who
used the touch-only interaction felt as though they had more control over the
information they shared and therefore had greater confidence in the brand.
Users stated that they found it much simpler to input information using only
one sensory method; touch.
“The lack of familiarity with how these digital
voice interactions actually work is likely to be the reason as to why consumers
are less trusting of brands that use both touch and voice. Whilst the use of
touch also garners much more control for a consumer, as opposed to voice”.
The
study, published in the ‘Journal of Interactive Marketing’ is the first of its
kind to explore the effects of new voice-based interface modes on marketing
relationships. Whilst technology multiplies the way that consumers can interact
with brands, this study shows that too much interaction can actually harm a company,
and offers managers guidance on how to increase personal engagement and brand
trust.
This month’s cover features Gary Steen, TalkTalk’s
Managing Director of Technology, Change, and Security, Gary Steen regarding the
telco’s commitment to thinking, and acting, differently in a highly competitive
marketplace…
TalkTalk is an established telecommunications company that fosters a youthful, pioneering spirit. “I like to think of TalkTalk as a mature start-up,” says Managing Director of Technology, Change and Security, Gary Steen. “We are mature in terms of being in the FTSE 250, with over four million customers, relying on our services every day through our essential, critical national infrastructure. But that said, I definitely think we start our day thinking as a start-up would. What can we do differently? How do we beat the competition? How do we attract great talent? We’ve got to come at this in a different way if we are going to succeed in the marketplace. We are mature, but we think like a start-up.”
Elsewhere we speak to Natalia
Graves, VP Head of Procurement at Veeam Software who reveals the secrets to a
successful procurement transformation. Graves
was tasked with looking at the automating, simplifying, and accelerating of
Veeam’s procurement and travel processes and systems around them, including
evaluating and rolling out a company-wide source-to-pay platform. “It has been
an incredible journey,” she tells us from her office in Boston, Massachusetts.
We also feature exclusive interviews with PTI Consulting and cloud specialists
CSI.
Plus,
we reveal 5 of the biggest AI companies in fintech and list the best events and
conferences around.
IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual…
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IPsoft has introduced 1Bank, the first conversational banking solution featuring virtual agent Amelia. It has been rated the top virtual agent in conversational AI by Everest Group.
Chetan Dube, CEO at IPsoft, commented: “With 1Bank we provide the most humanlike digital experience in the marketplace, built from the knowledge we’ve gained serving six of the world’s leading banks with conversational AI. We are giving banks the possibility of providing customers with their own personal banker around the clock.”
1Bank answers FAQs, but also resolves complex customer needs, by understanding customer intent. It can also switch context, mid-conversation. Its machine learning Learning (ML) abilities also mean that 1Bank can improve over time.
Some of the tasks 1Bank can carry out are:
advising on unpaid bills, proactively informing customers of an incoming bill and communicating any insufficient funds, making a money transfer and asking if the customer wants to set up payment for the bills when they are due.
recommending and setting up recurring payments, making payments from different accounts, opening and closing accounts.
helping customers locate transactions.
assisting with individual and potentially fraudulent charges on credit cards and disputing them, getting a new pin, getting a balance transfer or applying for a new credit card.
creating travel alerts after a customer made an airline purchase and proactively recommending the next step, such as, when traveling to exchange and withdrawing cash.
1Bank can integrate with existing tools and interfaces, and it can be added to existing applications to help customers quickly access the information and service they need. This includes mobile apps, desktop or kiosk apps, website modules, or within consumer chat applications, such as Facebook Messenger and Amazon Echo.
It is a measure of how much we take sophisticated technology for granted that the appearance of a pop-up chatbot…
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It is a measure of how much we take sophisticated technology
for granted that the appearance of a pop-up chatbot screen, asking questions
and providing sensible responses, is no longer considered remarkable.
Chatbots today inhabit websites, intranets, apps, and social
media platforms, and have become so ubiquitous as to become almost invisible.
Interacting with a text screen is a natural activity, and most users don’t seem
to care much about whether the other side of the conversation is a human or a
bundle of code.
From a corporate perspective, chatbots can be a win/win.
Increasingly reliable in their responses and cheap to operate, they are
available night and day and are instantly scalable. Whether your site or app
has one visitor a day or thousands, the bot is always eager to help.
We’ve come a long way from the disastrous early attempts at
providing AI assistance – remember Microsoft’s paperclip? – but what a good
chatbot does today is much the same as that much-loathed animated character:
identify what a user is trying to do, and offer appropriate help.
Artifical
intelligence
You will have heard of the Turing Test, which held that if a
computer could provide responses that were indistinguishable from those of a
human, the machine had to be considered intelligent. Are we there yet?
Despite some well-publicised claims, the answer is still,
probably, no. In 2014 a program called Eugene Goostman successfully tricked
Turing Test judges into believing it was a 13-year-old Ukrainian boy. This
controversial victory is still a failure, though, because nobody expects or
wants to find an adolescent behind a real-world help screen.
More relevant is the Loebner Prize for the most convincing
chatbot. This awards bronze medals each year to the best contenders, but has
never made a silver (text) or gold (audio visual) award – the equivalent of a
Turing Test pass.
A glance at the best entries from the 2018 competition shows
why. Eleven bots were asked 20 questions, winning two points for a human-like
answer and one for a plausible response. Out of a maximum of 40 points, the
winner scored 27 and the lowest just 12.
Even simple questions can make the tech fall over. The
winner, a chatbot called Tutor by Ron C Lee, answered “Do you know how to
make toast?” with “No, we haven’t”.
Chatbot limitations
While there remain limits on what a chatbot can convincingly
do, this need not be a problem if it is deployed in the right way. Recent
research from Penn State University found that while many appreciate an
apparently empathetic response from a bot, those who believe machines are
actually capable of consciousness do not.
“The majority of people do not believe in machine
emotion, so took expressions of empathy and sympathy as courtesies,” said researcher
Bingjie Liu. “However, people who think it’s possible that machines could
have emotions had negative reactions from the chatbots.”
The answer is only to use them for things they are good at,
says James Williams, who leads the development of advanced chatbots with
Nottingham-based software company MHR. While chatbots are now common in
consumer interfaces, he notes, there is much potential in the enterprise space.
Business bots
When applied within the company’s flagship human resources (HR) software, Williams says the conversational interface is an excellent way to simplify common transactions. “You’ll hear us talk a lot about reducing friction,” he says, which means anything that slows down a routine interaction.
An example is an employee submitting an expenses claim,
which MHR’s Talksuite does through an AI-driven chatbot. “Taking a picture
of a receipt is a natural thing to do, and the AI will recognise the image,
understanding the content as well as the context. Bots are really good for
processes with lots of rules or lots of steps, and here it just asks a few
questions and saves the employee a lot of hassle. Less friction.”
Knowing when not to deploy a bot can be just as valuable. Williams recounts one client which had deployed a complex chatbot for its newly joining employees, known in HR circles as the onboarding process. “The chatbot went through everything plus the kitchen sink, so the employee was there for 20 minutes or more being interrogated by a machine. It was just awful. A web-based form is a much better interface in this situation.”
His final advice is to consider the image the bot projects.
“Any personality in a chatbot tends to come accidentally, unlike a website
or an app. If you let software developers write the conversation, you might end
up with a bot that’s actually a bit of a dick. People make judgements on things
like language and punctuation. It’s fine to be personable and friendly, but it
should be clear when the user is talking to a bot and when any transition to a
human interaction takes place.”
Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by…
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Quest Solution Inc, provides supply chain and artificial intelligence (AI) based machine vision solutions. It has been awarded a project by a leading supply chain and logistics provider in the US. The release doesn’t detail who the leading supply chain provider is, but it does reveal that the project is valued at around $US7 million.
A patent
that will allow for a robot to live at your home and handle your deliveries has
been filed by Amazon. The patent outlines plans for a robot that will
completely transform last mile delivery capabilities, even potentially
delivering packages in the early hours between 2am and 6am.
Back to AI, NFI Industries and Transplace are paying attention to
this technology through partnerships with firms that add AI capabilities to
transportation and distribution. Both companies have announced a partnership
with Noodle.ai
with the goal of enhancing logistics services and technology capabilities.
In a video interview with CNBC, Lance
Fritz, the CEO of Union Pacific, is concerned that supply chain
disruption won’t return to normal. He believes the biggest concern lies in
trade and that the challenges with China should be resolved as soon as
possible.
In an interview with Sky News, Peter
Schwarzenbauer, BMW board member responsible for Mini and Rolls
Royce, has said that the firm will need to think about moving production from
the UK in the event of a no-deal Brexit. Remaining would be too costly for the
organisation and some production would move to countries like Austria. Toyota
shares similar concerns with Johan van Zyl, head of Toyota’s European
operations, telling the BBC that Brexit hurdles would ‘undermine Toyota’s
competitiveness’.
Blockchain remains an interesting solution for many in the supply chain and
Blockchain Labs for Open Collaboration (BLOC) has recently started working with
NYK, a Japanese shopping company, and BHP, a mining company, to establish a
sustainable biofuel supply chain using BLOC’s blockchain fuel assurance
platform.
Also in the news: HighJump,
a global supply chain solutions provider, awarded five women in its Top Women
Leaders in Supply Chain awards; Cryptobriefings
Kiana Danial examines whether VeChain can deliver a supply chain solution; Apple
releases a supply chain document that reveals how iPhone, airpods and other
products are all zero waste; and SIGTTO GM, Andrew Clifton, looks to the LNG
supply chain.
