Author

Manas Saha

Published

8 October 2026

Estimated Read time

10Mins

Manas Saha, Head of Financial Services UK at Publicis Sapient, on why firms that build client context, adviser judgement, past decisions and compliance outcomes – held in a form that both people and AI agents can use – will compound their advantage. Firms that don’t will be renting the same intelligence as everyone else

For decades, wealth management has operated under a simple equation in which growth required proportionally more advisers, more operational capacity and more cost, so as assets under management increased, firms scaled through people, processes and organisational complexity. Artificial intelligence is now breaking that equation, and the speed at which it is happening has caught much of the industry off guard.

Earlier this year, the launch of Hazel, an AI-driven tax-planning tool from Altruist, triggered billions in stock selloffs across listed wealth managers as markets began to question whether the traditional fee model can survive in a world of near-zero-cost analytics. Whatever one makes of the scale of that reaction, it revealed something important about how investors now view the sector, because they are already repricing wealth firms for AI risk before most of those firms have worked out what AI will change inside their own operations.

The model will not be the moat

The harder truth behind that repricing is that the models themselves are heading towards commodity status. Within a couple of years every wealth manager will have access to a powerful foundation model, bought from the same small group of providers on broadly similar terms, which means the lasting advantage will sit in the layer each firm builds on top of it, made up of client context, adviser judgement, past decisions and compliance outcomes held in a form that both people and AI agents can use. Firms that build that layer will compound their advantage with every client interaction, while those that neglect it will find themselves renting the same intelligence as everyone else.

Scarcity still shapes the business

Traditional wealth firms have long operated under scarcity, with scarce expertise, scarce adviser capacity and scarce client touchpoints shaping how the business runs. Clients now expect 24/7 service and hyper-personalisation, while advisers are already overloaded with back-office work that leaves them less time for the client conversations that create value. Most firms continue to rely on workflows built around manual reviews, disconnected systems and information that sits across CRM platforms, portfolio management tools, planning software, emails and adviser notes, so important client context is often fragmented across the organisation rather than available where decisions are made. Advisers spend valuable time gathering information instead of interpreting it, reviews happen according to calendars rather than real-world conditions, and work moves between planning, portfolio management, compliance and operations through error-prone manual handoffs.

Adviser time is the lever

With demand growing and adviser supply dwindling, productivity has become a strategic lever, and if AI can free up even 30 to 50 per cent of adviser time, a firm can channel that capacity into new clients, deeper relationships or significant cost savings. Capturing that value requires AI to become more than another productivity tool bolted onto existing processes, because the first generation of AI, which focused on summarising meetings, drafting communications and automating routine tasks, left the underlying operating model largely untouched.

Three stages of autonomy

The next stage embeds AI much more deeply into business processes through AI agents that coordinate data sources, prepare recommendations and manage multi-step workflows under human supervision and guidance. For a regulated firm the question that matters most is where the boundaries sit, and in practice it helps to think in three stages.

In assistive automation, the adviser initiates the task, checks the output and remains fully responsible for the decision, with AI supporting discrete work such as summarising, extracting data or drafting content under basic usage policies and human review. With guided orchestration, AI coordinates across systems, proposes actions and prepares fuller outputs such as follow-ups or scenario analysis, while a person supervises the workflow, approves recommendations and manages exceptions within defined permissions, approval points and audit trails. A fully AI-orchestrated workflows, agents execute multi-step processes within agreed rules, monitoring portfolios, triggering actions and routing exceptions, while people set the boundaries, monitor outcomes and intervene wherever judgement is required.

Governance from the first day

That third stage is where governance becomes most demanding, and UK firms already have a framework that should shape their design from the very first day. Under the Senior Managers and Certification Regime, accountability for any activity that AI supports, recommends or executes remains with the firm and its senior managers, so every agentic workflow needs a named owner, clear audit trails, escalation routes, outcome testing, explainability, bias monitoring and board-level oversight capable of showing that AI is supporting better decisions and better client outcomes rather than simply faster processes. Governance designed this way becomes part of the operating model itself, and the ability to preserve context, explain recommendations, trace decisions and coordinate work within clearly defined boundaries is precisely what allows AI to operate safely at scale across regulated workflows.

The Intelligent Wealth Firm

In our recent white paper with PIMFA, we describe the destination as the Intelligent Wealth Firm, a continuously learning organisation in which human expertise and AI capabilities operate as an integrated system. A single client interaction may be informed by hundreds of AI-driven activities spanning research, planning, compliance, servicing and portfolio management, and every recommendation, decision and outcome feeds back into the firm’s memory, making the organisation progressively smarter over time. That memory captures historical recommendations, adviser actions, client interactions, compliance outcomes and business results, and over the years this experience becomes institutional knowledge that improves future decisions and creates an advantage that competitors, technology vendors and foundation model providers cannot easily replicate.

Who is already moving

The largest institutions are not waiting. More than 98 per cent of Morgan Stanley’s adviser teams use its internal AI assistant, and its Debrief tool, used with client consent, summarises client meetings, drafts follow-ups and logs notes into the CRM. UBS is using agents to send advisers thousands of alerts a day and to execute trades or transfer funds once an adviser has approved them, according to Reuters. That is guided orchestration in practice. On the trading side, Goldman Sachs has teamed up with Anthropic to develop agents for trading, transaction accounting and client onboarding, and Citi is preparing an AI-enabled virtual wealth management team member.

Context is what makes the technology work

Context informs technology, so the data comes first. Most of what a wealth firm knows about a client sits in unstructured form: adviser notes, emails, meeting transcripts, provider documents, suitability reports, call recordings. Conventional systems cannot read it. AI can. Agents ingest that material, connect it to the structured records in the CRM and portfolio systems, and hold the result where the next adviser or the next agent can use it.

The gains show up in metrics that operations teams already track. First time right, the share of cases that clear onboarding, suitability or review without rework, should improve significantly when AI tools, ours included, work from information that is complete from the start. Every reworked case that disappears returns capacity to the back office. Some of that capacity lowers the cost of running the operation. The rest goes back to advisers and service teams, who get more time with clients.

Screening shows the effect clearly. A politically exposed person check can return 5,000 potential hits. An agent working with the right context, including the client’s history, source of wealth and earlier decisions, can narrow that to the ten that need a person’s judgement and record its reasoning on the rest. The time saved is direct, and it lands in the back office first.

Growth, retention and the client

Efficiency is half the case. The other half is revenue. Advisers with more time and better information can serve more clients, respond faster and see needs before a client raises them. Clients who feel known stay. A firm built around the client, not around its own systems, keeps more of the relationships it wins.

The obstacle is practical. Client data lives in many places, so the questions are how to bring it to one place, how to mine it quickly and how to consolidate the platforms underneath. We have invested significantly in all of our products to answer them. Sapient Bodhi builds and orchestrates agentic workflows with industry context-built in. Sapient Slingshot reads legacy code, documents the current-state by extracting business rules and dependencies and then migrates to a more modern codebase in an accelerated time, while taking much of the risk out by folding several platforms into fewer.

The enterprise context graph

Bringing the data together is the start. An enterprise context graph maps how clients, accounts, advisers, products, policies, decisions and outcomes relate to one another, and keeps that map current. It is the shared context layer behind our platforms, and it grows more accurate the more it is used. An agent preparing a client review can then see the holdings, and also the meeting where the client’s appetite for risk changed, the recommendation that followed and the compliance sign-off attached to it. The graph belongs to the firm. Models can be swapped as better ones arrive while what the firm has learned stays where it is.

Governing the context layer

A layer that holds client history, advice and compliance outcomes needs governing as carefully as the agents that draw on it. Access should follow permissions, so an agent sees only what the person it serves is entitled to see. Every piece of context needs a traceable source, so any recommendation can be followed back to the information behind it. Outputs need testing against client outcomes, and each workflow needs a named senior owner under the same regime. UK GDPR applies to the graph as it does to any other client record.

Where to begin

For leaders wondering where to begin, none of this requires redesigning everything at once, although it does mean starting now to build the capabilities that allow AI to operate safely and effectively across the enterprise, and three priorities stand out.

The first is to define decision rights for humans and AI

Establishing clear responsibilities for where AI can recommend, where it can execute and where human judgement remains essential, with governance, accountability and transparency designed into every workflow from the start. A useful exercise for any leadership team is to ask which activities in the business genuinely require human judgement and which persist simply because that is how they have always been done.

The second priority is to build the context layer

Connecting client information, adviser expertise, investment policies and historical decisions into a shared context that both advisers and AI agents can draw on when a decision needs to be made. This is where the durable advantage lies, and it is worth every firm asking what proprietary context, experience and decision-making knowledge it holds that a competitor, technology vendor or foundation model could not easily reproduce, since that knowledge is the raw material from which institutional memory is built. When we work with wealth firms on this through Sapient Bodhi, our enterprise agentic platform, most of the early effort goes into connecting that knowledge and building the governance around it, because the model a firm uses will keep evolving while its institutional memory keeps growing in value with every client interaction.

The third is to measure client outcomes in place of AI adoption

This sounds obvious but runs against how most AI programmes currently report their progress. It is tempting to track the number of use cases launched or the share of staff using a new tool, yet those figures say very little about whether the firm is getting better at what it does, whereas client results, decision quality, operational efficiency and improved governance are the measures that will matter to boards, to regulators assessing Consumer Duty and ultimately to clients themselves. They are also the measures that show which workflows deserve to be scaled across the organisation, so that every deployment strengthens the firm’s institutional memory and makes the whole system more capable with each client interaction.

Any wealth leader can start that work tomorrow by asking which decisions in their firm are still waiting for a meeting, a spreadsheet or a manual review, because each one of those is a place where the firm’s own memory could already be doing the work.

Learn more at publicissapient.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Fintech & Insurtech
  • InsurTech

Author

Tamsin Crossland

Published

17 September 2026

Estimated Read time

4Mins

Tamsin Crossland, Principal AI Architect at Icon Solutions, on why ethical thinking, empathy and broader perspectives, leave organisations better equipped to build AI systems that are not only intelligent, but fair, resilient and trusted

Artificial Intelligence (AI) has been making headlines as it moves from a future-facing concept to a modern reality. From fraud detection and credit scoring to customer service automation and risk modelling, AI systems are increasingly shaping real-world outcomes, particularly within the financial services industry. But as AI adoption accelerates across payments and technology, one question has become unavoidable: who is training and developing AI models, and whose perspectives are shaping their decisions?

AI is not neutral; it reflects the data it is trained on, as well as the assumptions and biases of the teams that design, test and deploy it. When those teams lack diversity, the risk of reinforcing existing biases increases. As a result, building ethical AI is as much a human challenge as it is a technical one.

Gender representation

Women remain significantly underrepresented within the technology and payments industries, a reality that often begins in school, before career choices are made. Early perceptions that computing is ‘a boy’s hobby’ continue to influence subject choices at school and university level, narrowing the pipeline of female talent entering technical and computing roles. While slow progress has been made, recruitment processes can still carry subtle gender bias, with many IT and technology placements being given directly to men over women. This is shaping who feels encouraged or excluded from pursuing careers in AI and advanced technologies.

Real change must begin earlier in the classroom. Inclusive education initiatives, visible role models and outreach programmes can actively encourage girls to pursue careers in STEM. At the industry level, companies should critically evaluate recruitment strategies and consider more ambitious gender diverse targets, such as aiming for a 50% female graduate intake. This is not about optics, but about building a workforce that better reflects the society AI systems ultimately serve.

Why diversity is critical in AI development

The case for gender diversity has become even more compelling when examining AI training and deployment. AI models are built on historical data, data that may already contain embedded biases. Without diverse perspectives in the initial phase, challenging assumptions and the biases used to create AI models, these biases can be amplified once the AI system is deployed.

Gender-diverse teams bring a range of different perspectives and backgrounds. For instance, women bring valuable and often underappreciated perspectives to the process. Emotional intelligence and empathy, frequently described as ‘soft skills’, are increasingly critical as AI systems move beyond efficiency tools and into decision-making roles that affect people’s lives. The ability to anticipate unintended consequences, question whether an outcome feels equitable, and consider how different groups may experience an automated decision is essential to responsible deployment.

Diverse teams are better positioned to interrogate training data, stress-test algorithms and identify blind spots before products are launched. Research consistently shows that gender diverse teams outperform male-dominated ones in problem-solving and innovation, generating 45% of total revenue from innovation, compared to 26% by less diverse teams. In the context of AI, diversity becomes a form of governance, a mechanism for reducing risk and strengthening accountability.

Encouragingly, women are already leading important conversations within AI ethics forums and industry working groups, such as Women4Ethical AI, AI4ALL and Women in AI (WAI). They are contributing to debates around transparency, fairness, explainability and responsible use. However, discussion alone is not enough, and now it is time for action. The next step is embedding these principles into product development cycles, procurement standards and executive decision-making processes.

Ethical AI cannot be treated as a retrospective compliance exercise, but as an integral part of the initial development phase.

Shaping the future of ethical AI

As AI continues to shape the future of payments and technology, responsible representation must be viewed as a strategic imperative. Without gender diversity, the risk of reinforcing systemic bias grows. With diverse teams guided by ethical thinking, empathy and broader perspectives, organisations are better equipped to build AI systems that are not only intelligent, but fair, resilient and trusted.

Learn more at iconsolutions.com

  • Artificial Intelligence in FinTech
  • Data & AI
  • Digital Payments

Author

Dr Abdulrahman Kerim

Published

27 August 2026

Estimated Read time

4Mins

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

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.

            Learn more at superprof.co.uk

            • Data & AI
            • Digital Strategy

            Author

            Matthew Biboud-Lubeck

            Published

            26 August 2026

            Estimated Read time

            6Mins

            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

            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.

            Learn more at amperity.com

            • Data & AI

            Author

            Peter Pugh-Jones

            Published

            24 August 2026

            Estimated Read time

            5Mins

            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

            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.

            Getting the foundations right

            As AI adoption continues, the conversation is starting to shift.

            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?

            Learn more at confluent.io

            • Artificial Intelligence in FinTech
            • Data & AI

            Author

            Frank Jaquez

            Published

            Estimated Read time

            4Mins

            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

            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.

            Learn more at skillsoft.com

            • Data & AI
            • Digital Strategy

            Author

            Simon Ritter

            Published

            21 August 2026

            Estimated Read time

            5Mins

            Simon Ritter, Deputy CTO at Azul, on how the Java ecosystem is adapting (quickly) to the needs of AI applications

            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.​

            • Data & AI
            • Digital Strategy

            Author

            Oz Nicco-Annan

            Published

            18 August 2026

            Estimated Read time

            5Mins

            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

            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. 

            Learn more at infrapartners.llc

            • Data & AI
            • Digital Strategy

            Author

            Cien Solon

            Published

            17 August 2026

            Estimated Read time

            6Mins

            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

            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.

            Learn more at launchlemonade.app

            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.

            • Artificial Intelligence in FinTech
            • Data & AI
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Dave Murphy

            Published

            10 August 2026

            Estimated Read time

            6Mins

            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

            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

            • Artificial Intelligence in FinTech
            • Digital Strategy
            • Fintech & Insurtech

            Author

            Robbie Tilleard

            Published

            3 August 2026

            Estimated Read time

            4Mins

            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

            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.

            Learn more at lorikeetcx.ai

            • Artificial Intelligence in FinTech
            • Cybersecurity in FinTech
            • Data & AI
            • Digital Strategy

            Author

            Errol Rodericks

            Published

            29 July 2026

            Estimated Read time

            5Mins

            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

            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.

            Learn more at denodo.com

            • Artificial Intelligence in FinTech
            • Data & AI
            • InsurTech

            Author

            Hugh Scantlebury

            Published

            22 July 2026

            Estimated Read time

            5Mins

            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

            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.

            Learn more at aqilla.com

            • Artificial Intelligence in FinTech
            • Data & AI

            Author

            Peter Pugh-Jones

            Published

            12 June 2026

            Estimated Read time

            5Mins

            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

            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. 

            Learn more at confluent.io

            • Data & AI
            • Digital Strategy

            Author

            Chris Tredwell & Charis Thomas

            Published

            28 May 2026

            Estimated Read time

            5Mins

            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

            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.

            Learn more at aqilla.com

            • Artificial Intelligence in FinTech
            • Data & AI

            Author

            Tom Parsons

            Published

            18 May 2026

            Estimated Read time

            5Mins

            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

            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.

            Learn more at goodenergy.co.uk

            • Data & AI
            • Digital Strategy
            • Sustainability Technology

            Author

            Dvir Hoffman

            Published

            27 April 2026

            Estimated Read time

            6Mins

            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

            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.

            Learn more at commbox.io

            • Data & AI
            • Digital Strategy

            Author

            Lee Nolan

            Published

            23 April 2026

            Estimated Read time

            5Mins

            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

            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.

            Learn more at hitachivantara.com

            • Data & AI
            • Digital Strategy

            Author

            Mark Talbot

            Published

            22 April 2026

            Estimated Read time

            5Mins

            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

            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.

            Learn more at appian.com

            • Artificial Intelligence in FinTech
            • Data & AI
            • Digital Strategy
            • Fintech & Insurtech
            • People & Culture

            Author

            Daniel Ehnhage

            Published

            20 April 2026

            Estimated Read time

            5Mins

            Daniel Ehnhage, Head of AI Transformation at Unit4, on why those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

            As the head of AI transformation, it might sound counterintuitive to suggest that artificial intelligence is not the most important part of my work. It makes a significant contribution to the radical change we are looking to achieve, but the technology itself is only about 10% of the solution. A significant part of the planning and investment must be based around addressing issues like the integration of siloed information systems, the building of the organisational capability required to adopt AI safely and finding the right business case. The key is to understand that adopting AI is not only about improving existing processes – it’s about gradually reshaping how we work in a sustainable way. The goal should be phased, practical improvements that build maturity over time.

            This can be daunting for any organisation that has well-established operating practices. It requires a deliberate shift from problem‑solving to rethinking how value is created across the organisation. AI is far more capable and can empower your teams to find new solutions such as gathering more intelligence about market opportunities to improve productivity and decision making. The focus should be on enabling internal teams to work smarter through safe, responsible AI adoption. If your organisation is prepared to embark on such change, you must recognise AI becomes most powerful when you bring the right data together. Today, many organisations, including ours, are still maturing in this area. Successful adopters of AI prioritise building data readiness step by step so AI can create real value without overpromising.

            Obviously, the role of AI transformation then becomes much broader with the added challenge of having to implement change without disrupting existing business performance. Consequently, there are some key areas where organisations must focus their attention, beyond ensuring they pick the right AI tool…

            Structural Change – Put the AI Board in Place

              AI transformation evolves how organisations work. It does not replace everything we humans do today. The goal is to focus on practical, high‑value use cases that improve productivity, quality, and employee experience without creating disruption. A widely debated expression of this change is the concern that AI will replace human employees. Personally, I think this lacks imagination around the positive impact that AI can have on a workplace. Yes, it may reduce the number of repetitive, mundane tasks, but more importantly it will create new ways of working and collaborating.

              However, given how rapidly the technology is moving it is critical your organisation puts the right safeguards in place and agrees policies about ethical usage. That requires adoption of a cross-functional AI Board to provide a framework for embracing AI which will manage the impact of the structural change. This provides focus for your organisation’s approach to AI. The goal should be to agree which tools offer the most benefit for your teams and concentrate on exploiting use cases that will deliver the most benefit.

              The AI Board should be responsible for establishing the governance structure to help the IT and cybersecurity teams to ensure the use of AI is not creating new vulnerabilities. It should provide clarity and safeguards so employees can use AI confidently and responsibly. Our goal is to enable safe experimentation – not restrict innovation.

              People Change – Enabling Collaboration and Experimentation

              The ambition should be to get employees excited about the potential of AI to open up new ways of working that can lead to rewarding opportunities and exciting new challenges. Indeed, it is widely accepted that helping your people to accommodate the change is the biggest challenge you will face, taking up about 70% of the time required to implement the technology. This is because successful implementations depend on collaboration between distinct teams, which in turn depends on breaking down barriers, both for individuals and teams.

              For example, imagine being able to use AI to analyse data from diverse systems such as customer service, product development and marketing to identify new opportunities to support customers.

              Integrating these data sources could be seen as interfering with distinct job functions, so for all employees it is critical to educate them on what will be expected of them and a good start point is explaining how they will be measured. It could include simple measures such as demonstrating usage of AI tools, but if an organisation wants employees to adopt the technology it is also important to empower them through training.

              With the right support, employees will want to experiment, which should also enable them to understand use cases for AI in their work and the competencies they need to develop. This can be achieved through opportunities for cross-functional teams to explore new ways of working and innovating and should be encouraged by senior leaders. It is crucial they set the right tone, support initiatives, celebrate successes and listen to employee feedback.

              Business Change – Building the Right Business Case

              The business case is not just about saving money to solve a specific problem. It is too easy to look at the saved hours and productivity gains from adopting AI as the sum total of the investment costs you must deal with. There are a number of internally focused requirements that you must build into your thinking about AI transformation. There will be costs around the integration work to enable AI to access data from disparate systems. Competence development must be a top priority. Time must also be allocated to the process of change management and how it may disrupt existing business processes. Security must be a top consideration. These are all internally focused tasks, but you must look at them if you are to capitalise effectively on your AI investment.

              It is tempting to become overly excited by the potential of AI as a technology, and certainly it will bring dramatic change to organisations in the years to come, but having experienced the factors necessary for successful transformations, it is absolutely critical senior leadership teams approach AI-enabled change with cool heads and clarity on what they want to achieve. Above all, they must remember success is not dependent on the implementation of the technology, but predominantly on bringing employees with them on the journey. Many commentators talk about the rise of AI-first organisations. Those that put people and capability at the centre of their AI strategy will unlock far greater and more sustainable value than those led by technology alone.

              Learn more at unit4.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Andrew McLernon

              Published

              15 April 2026

              Estimated Read time

              6Mins

              Andrew McLernon, CEO and co-founder at Interlink, on why culture is the real disruptor

              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.

              Technology should create space for humans to think more deeply, collaborate more creatively and make better decisions. If AI adoption results only in faster outputs without improved thinking, we’ve missed the opportunity.

              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.

              Learn more at weareinterlink.com

              • Data & AI
              • Digital Strategy
              • People & Culture

              Author

              Todd Moore

              Published

              14 April 2026

              Estimated Read time

              4Mins

              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

              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 Report examines 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. 

              Read the Report

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy
              • Fintech & Insurtech

              Author

              Jamil Jiva

              Published

              8 April 2026

              Estimated Read time

              5Mins

              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

              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.

              Learn more at linedata.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy
              • Fintech & Insurtech
              • Neobanking

              Author

              Richard Doherty

              Published

              Estimated Read time

              6Mins

              Richard Doherty, Head of Wealth & Asset Management, Publicis Sapient, on how asset managers must redesign their enterprise for AI-driven decision intelligence

              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.

              Learn more at publicissapient.com

              • Artificial Intelligence in FinTech
              • Blockchain & Crypto
              • Data & AI
              • Digital Strategy
              • Fintech & Insurtech

              Author

              Martijn Gribnauis

              Published

              23 March 2026

              Estimated Read time

              5Mins

              Martijn Gribnauis, Chief Customer Success Officer at Quant, on why Agentic AI will redefine financial services

              A recent Google Cloud survey showed that only 13% of finance organisations are currently using agentic artificial intelligence. This number needs to, and will rise when you consider that 88% of financial leaders are seeing ROI from generative AI already. Agentic is the next and most advanced evolution of artificial intelligence the world has ever seen. 

              Agentic AI is not on the way. It is here and already reshaping how forward-leaning financial institutions operate. In 2026, for IT and finance leaders to build an insurmountable competitive lead they must deploy agentic AI in every area where it can safely and effectively create value. The institutions that hesitate will find their business models under threat from familiar competitors and newcomers alike.

              Reinvention of Core Processes

              Agentic AI is poised to reinvent core financial processes. Bookkeeping, record maintenance, and period-end close are nearing complete automation. Month-end processes that once required late-night, stress-filled marathons will evolve into continuous, largely automated cycles. IT teams will no longer spend evenings on high alert waiting for failures. 

              This shift also frees IT leaders, finance teams, and operations functions from monotonous repetitive tasks. Instead of focusing on system uptime and manual reconciliation, they will collaborate with the C-suite on strategic initiatives that drive growth and revenue. 

              Understanding Why Adoption Is So Low

              Despite the promise of Agentic AI, there is understandable caution. Some 80% of organisations have reported ‘risky behaviour’ from AI agents, and in the world of finance that is an alarming number. Finance is one of the most regulated, risk-averse sectors in the world. The fear of losing control remains the primary reason so few in the industry have embraced Agentic AI.

              Loss of control and fear of catastrophic error

              Financial leaders fear that an autonomous system could go ‘off script’, mis-route payments, misinterpret rules, or inadvertently cause compliance breaches. In finance, even small errors can trigger major financial or regulatory consequences.

              Security and data privacy concerns

              Large AI models require huge quantities of sensitive data. Organisations worry about breaches, cyber-attacks, or manipulation. An AI agent with improperly configured permissions could, in theory, execute fraudulent transactions or expose confidential customer information.

              Bias and fairness risks

              If AI agents make decisions using incomplete or fragmented data, they risk perpetuating or amplifying bias. At scale, biased decision-making can undermine customer trust and expose firms to legal and regulatory challenges.

              Regulatory ambiguity and audit difficulty

              Regulators are still determining how to govern agentic AI. Some organisations fear that early adoption could unintentionally violate rules or create future audit vulnerabilities.

              These fears are legitimate, but not insurmountable.

              Tackling the Adoption Barriers: A Practical Blueprint for Finance Leaders

              To capitalise on Agentic AI’s immense potential, leaders must take a structured approach grounded in business value, security, and trust.

              1. Start With Clear, Measurable ROI and Efficiency Gains

              In finance, adoption accelerates when decision-makers see proof of value.

              Start by automating repetitive processes. Agentic AI can handle tasks like data entry, reconciliation, invoice matching, and initial fraud checks faster and more accurately than humans. This leads to reduced operational overhead as automation lowers labour costs, shortens processing times, and reduces error rates. Demonstrating these savings through case studies or internal pilots is critical to changing minds. 

              AI agents can enable revenue growth by analysing huge data sets to identify new investment opportunities, optimise trading strategies, and generate personalised product recommendations. Each of these capabilities directly impacts top-line growth.

              2. Strengthen Risk Management and Compliance Through AI

              Agentic AI will improve risk management when deployed responsibly. This starts with real-time fraud detection. AI agents can monitor transactions continuously, identifying patterns that suggest fraud long before traditional systems would detect an anomaly.

              Continuous monitoring is also incredibly helpful when it comes to compliance. AI agents excel at ensuring adherence to KYC and AML regulations. They can automatically maintain audit trails, identify missing documentation, flag anomalies, and escalate issues instantly.

              Enhanced stress testing and scenario modelling can both be completed via Agentic AI. It can simulate complex market environments more dynamically than legacy tools, providing deeper insights into vulnerabilities and improving resilience. When showcased and presented in this context, agentic AI becomes a risk-reduction tool in the eyes of decision makers. 

              3. Directly Address Security and Trust Concerns

              Trust is the cornerstone of adoption. Implement enterprise-grade security architecture that includes encryption, secure APIs, strict access controls, and continuous monitoring of agent behaviour. And, use explainable and transparent AI systems (XAI) so your finance teams understand the reasoning behind decisions. XAI helps provide interpretable outputs that support auditability and regulatory compliance.

              Start small with a controlled, low-risk pilot. A proof-of-concept in a non-critical workflow helps teams understand the technology, gather evidence, and build internal support before scaling. Produce numbers based reporting that speaks the language of the people who make the decisions. Show, don’t just tell them how agentic will move the business forward.

              4. Highlight the Competitive Advantage

              Agentic AI adoption is not just an efficiency upgrade. It is a competitive imperative. AI agents create faster innovation cycles by accelerating product development, service delivery, and operational improvements.

              They also provide superior customer experience. From instant account servicing to personalised financial recommendations, Agentic AI delivers the speed, personalisation, and convenience customers expect. Plus, it scales exponentially. No matter how many people call in at the same time, an agentic agent will answer immediately. Agentic AI reduces up to 86% of time spent in complex workflows that were traditionally handled only by people. This will be huge in getting ahead of your competition. 

              5. Build Momentum Through Internal Champions

              Adoption increases when respected leaders advocate from within. Mid-level managers, AI-literate staff, or members of the C-suite who understand the technology can serve as champions. Use them and their beliefs to drive alignment, communicate benefits, and counter misconceptions. The more people from different departments and levels of the organisation that talk up the technology, the more likely you are to get buy-in. 

              Your Time is Now

              Agentic AI will redefine financial services. The organisations that act today will build capabilities, insights, and competitive advantages that late adopters will not be able to replicate. Finance leaders must begin asking where agentic AI can support their business, where it can remove friction, where it can unlock growth, and where it can transform operations. The firms that act now will lead the industry. Those that hesitate will not get the chance to catch up.

              The only remaining question for finance organisations is not whether agentic AI will change the industry, but how quickly they choose to deploy it.

              Learn more at quant.ai

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Payments
              • Digital Strategy

              Author

              Dr. Yvonne Bernard

              Published

              18 March 2026

              Estimated Read time

              5Mins

              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

              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.

              Learn more at hornetsecurity.com

              • Cybersecurity
              • Cybersecurity in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Dr Megha Kumar

              Published

              17 March 2026

              Estimated Read time

              6Mins

              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

              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.

              Learn more at cyxcel.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Fayola-Maria Jack

              Published

              16 March 2026

              Estimated Read time

              6Mins

              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

              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’. 

              Learn more at resolutiion.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Dan Brightmore

              Published

              9 March 2026

              Estimated Read time

              5Mins

              Visa is leading the AI race in payments, according to Evident’s AI Index for Payments, a major new ranking of…

              Visa is leading the AI race in payments, according to Evident’s AI Index for Payments, a major new ranking of AI adoption within the industry. 

              The Index shows industry stalwarts Visa and Mastercard outpacing their peers and delivering tangible AI outcomes thanks to early investments in talent and innovation.

              Behind them, PayPal (3rd), American Express (4th), Stripe (5th) and Block (6th) emerge as the challengers. They outperformed the Index average, but are yet to match the leaders’ scale of deployment and outcome disclosure.

              AI Moving from Experimentation to Deployment

              Over the past two years, the 12 payments companies in the Index have publicly documented nearly 100 AI use cases. Underscoring how rapidly AI has moved from experimentation to deployment across core payment workflows. It’s a landscape defined by constantly evolving fraud threats and rising customer expectations for faultless, high-speed processing. Evident notes that nearly a third of these use cases disclose measurable outcomes, including efficiency gains, risk reduction and revenue uplift.

              “Payments firms adopted AI out of necessity long before many other industries – their business models demanded it. Companies who invested early – like Visa and Mastercard – have gained a clear advantage over their peers, both in AI capabilities and the value their deployments are realising.” Alexandra Mousavizadeh, Co-Founder and Co-CEO of Evident.

              Talent, Innovation, Leadership and Transparency

              The Evident AI Index for Payments provides the most comprehensive independent benchmark of AI maturity across the industry. It is based on publicly available data around four pillars critical to successful AI deployment: Talent, Innovation, Leadership and Transparency.

              According to Evident, Visa’s lead is based on consistent performance across the four pillars. And because it demonstrates the clearest evidence that AI is institutionalised across its core transaction network. Visa and Mastercard show maturity in areas such as fraud detection, cybersecurity and network-level risk reduction. Visa stands out for the scale and measurable impact of a handful of large, multi-year deployments focused on the integrity and security of its entire ecosystem.

              “Mastercard shows strong evidence of scaled deployment and quantified performance improvements. Particularly in areas like fraud detection and AML tracing,” continued Mousavizadeh. “But what sets Visa apart is the degree to which the company is demonstrating impact at scale over multiple years. From applications of AI across its operations and network. It signals a shift from individual use cases to AI as institutional capability.

              “What the Index also reveals is the importance of consistent innovation to maintain competitive advantage. With relatively nascent industry players like Stripe and Block performing well – and showing their AI potential reflected in their valuations – the Index leaders cannot afford to drop off the pace.”

              AI Impact on Show, but ROI Reporting Scarce 

              Firms in the top half of the Index account for nearly 80% of use case disclosures (with the top three providing a significant 54%). Highlighting the link between AI maturity and the ability to scale deployment.

              Visa performed strongly in this regard. For instance, its latest threat report disclosed advanced AI/ML blocked nearly 85% more fraud compared to one year prior. Similarly, when Mastercard incorporated Gen AI technology into its Decision Intelligence solution, initial modelling showed AI enhancements improved fraud detection rates from an average of 20% to as high as 300% in some instances.

              However, Evident notes that no payments company has disclosed realised or projected ROI across all enterprise or group-wide AI activities. 

              “The Index leaders are locked in a tight race at a point when the thinking around corporate AI adoption is shifting – away from chasing the biggest models to building technologies that solve real operational problems efficiently,” commented Annabel Ayles, Co-Founder and Co-CEO of Evident. “Against this backdrop, the absence of ROI disclosure – or any group targets for AI ROI – is increasingly conspicuous. Currently, 1-in-5 banks now report on group-level AI returns. However, payments firms have yet to quantify the aggregate impact of their AI investments. To keep justifying this expenditure, the market will sooner or later demand clearer evidence of value.”

              A Hotbed of AI Talent

              The Index also reveals that the average payments company has over 30% more AI-focused workers than other financial institutions, despite substantially smaller employee numbers. 

              The three major card networks – Visa, Mastercard and American Express – account for nearly half (48%) of the payments industry’s AI talent stack. PayPal is currently the biggest employer, accounting for nearly a fifth (18%) of that AI talent.

              PayPal’s AI talent has allowed it to build proprietary models tightly integrated with its data and workflows. Consequently, it accounts for nearly a quarter (24%) of the 98 AI use cases documented by its peers over the past two years – 1.7x as many AI applications as detailed by Visa or Mastercard.

              “AI maturity is no longer defined by talent volume alone, and the Index leaders combine AI development, data engineering and product capabilities in ways that allow them to move rapidly from model experimentation to production deployment,” concluded Ayles.

              The Evident AI Index Methodology

              The Evident AI Payments Index ranks the AI maturity of 12 of the largest payment networks and processors across the globe. These 12 entities were chosen by aggregating the largest payment companies, with a minimum of $2B in annual revenue. 

              It is an independent, ‘outside-in’ assessment based exclusively on publicly available information. Each company was assessed against 60+ individual indicators, organised into four pillars critical to successful AI deployment at scale: Talent (45% weighting), Innovation (30%), Leadership (15%) and Transparency of Responsible AI activity (10%).

              Data is gathered through a combination of extensive manual research and proprietary machine learning tools that extract key data points from company reporting and public disclosures (including press releases, investor relations materials, group-level website pages, group-level social media accounts, and media interviews with senior leadership), as well as a range of third-party data platforms.

              Further information on the methodology of the Index can be found at evidentinsights.com

              • Artificial Intelligence in FinTech
              • Digital Payments
              • Neobanking

              Author

              Adam Spearing

              Published

              5 March 2026

              Estimated Read time

              5Mins

              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

              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.

              Learn more at servicenow.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Adonis Celestine

              Published

              3 March 2026

              Estimated Read time

              5Mins

              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

              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.

              Learn more at applause.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Tom Lanaway

              Published

              2 March 2026

              Estimated Read time

              4Mins

              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.

              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. 

              Learn more at connective3.com

              • AI in Procurement
              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy
              • People & Culture

              Author

              Dan Brightmore

              Published

              Estimated Read time

              2Mins

              Hampshire Trust Bank (HTB) is using artificial intelligence (AI) to act faster on customer concerns. It is empowering its teams…

              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.

              Learn more at netcall.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Payments
              • Digital Strategy
              • Fintech & Insurtech
              • InsurTech

              Author

              Gregory Mostyn

              Published

              24 February 2026

              Estimated Read time

              5Mins

              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

              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. 

              Learn more at wexler.ai

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy
              • Fintech & Insurtech

              Author

              Adrian Wood

              Published

              19 February 2026

              Estimated Read time

              5Mins

              Adrian Wood, Strategic Business Development & Offer Marketing Director at DELMIA

              The era of trial-and-error manufacturing is over. By integrating NVIDIA’s Physical AI into DELMIA’s Virtual Twin technology, Dassault Systèmes is moving the industry from static automation to autonomous software-defined systems that “learn” the laws of physics before the first part is made.

              Revolutionising Manufacturing with Agile AI-Driven Production

              Manufacturing is reaching a breaking point. Rigid production and logistics systems slow setup, ramp-up and scaling. Meanwhile deterministic automation struggles with real-world change, from new variants to unplanned constraints. The future is agile, software-defined production built on modular autonomous equipment, proven virtually and deployed with confidence.

              Dassault Systèmes and NVIDIA are building the industrial AI foundation to make that future real. DELMIA contributes the virtual twin of production systems. A semantically rich model of production that connects design intent to real-world execution across engineering, manufacturing and supply chain. NVIDIA contributes physical AI and accelerated computing to simulate robotics-grade physics and perception at scale. Together, we can virtualise and orchestrate autonomous production systems. Then manufacturers can prove changes virtually and make them real faster, with less risk and rework.

              This collaboration establishes a shared industrial AI architecture. This grounds artificial intelligence in the laws of physics and validated scientific knowledge. The integration of NVIDIA Omniverse physical AI libraries into the DELMIA Virtual Twin of global production systems represents a major step forward. It allows manufacturers to design, simulate and operate complex systems with a new level of confidence and precision. Not just incremental improvements; this partnership establishes a mission-critical system of record for industrial AI that powers a new way of working.

              Virtual Twins: The Cornerstone of Modern Manufacturing

              For years, manufacturers have optimised production lines in the physical world. While effective, this approach is often slow, resource-intensive and constrained by the cost of experimentation in live operations. Virtual twin technology changes this dynamic. A virtual twin is a science-based model of a system that goes beyond visualisation, enabling realistic validation of how operations should run before changes are made in the real world.

              DELMIA empowers companies to create comprehensive virtual twins of their entire operational ecosystem. This includes everything from individual machines and robotic workcells to full factory floor layouts and global supply chains. Within this virtual environment, manufacturers can:

              • Simulate and validate production processes before a single piece of equipment is installed.
              • Optimise workflows for maximum throughput and efficiency.
              • Identify potential bottlenecks and safety hazards without disrupting ongoing operations.
              • Train operators and maintenance crews in a risk-free setting.

              The virtual twin orchestrates design, engineering, production and supply chain in one environment so decisions can be tested, trusted and reused. This capability alone delivers significant value, but its impact grows when combined with physical AI.

              Integrating AI for Autonomous Production

              The partnership with NVIDIA brings physical AI into DELMIA virtual twins. NVIDIA Omniverse provides a platform for developing and operating 3D simulations and industrial digitalisation applications using OpenUSD-based interoperability. Combined with DELMIA’s production semantics, manufacturers can test autonomous behaviour in realistic conditions before deployment.

              This is the shift from ‘mirroring reality’ to ‘proving change’. AI models accelerated by NVIDIA computing can evaluate scenarios across production constraints, resources and variability. They can help teams reduce commissioning surprises, improve flow and validate how production should respond to change, from new variants to disruptions.

              The result is the emergence of software-defined production systems. These are factories and operations where decisions remain human-led, but are continuously supported by AI that recommends, tests and validates options in the virtual twin before changes are deployed. This creates a feedback loop where the virtual world is used to validate better outcomes for the real world.

              A Practical Application: The OMRON Collaboration with DELMIA & NVIDIA Drive Real-World Success

              To understand the real-world impact of this technology, consider the collaboration with OMRON, a global leader in industrial automation. OMRON recognizes that addressing the growing complexity of modern manufacturing requires a move toward fully autonomous and digitally validated production systems.

              By combining DELMIA’s Virtual Twin of Production Systems, NVIDIA physical AI, and OMRON automation technologies, manufacturers can move from design to deployment with greater confidence. When a manufacturer introduces a new product variant or packaging change, automation often fails in small but costly ways, such as automation-grasping reliability, orientation on conveyors or downstream flow stability. Instead of trial-and-error changes on the line, teams can validate process logic, layout constraints and operating rules in the DELMIA virtual twin, then simulate realistic robot and material behaviour using NVIDIA’s AI before deployment. The result is faster adaptation and less physical rework.

              The Top 3 Broader Impacts on Manufacturing

              This fusion of virtual twin technology and industrial AI has far-reaching implications for the entire manufacturing sector including:

              1. Unlocking New Efficiencies: Software-defined production systems can continuously identify operational improvements that are difficult to see through manual oversight alone, improving throughput, uptime and overall performance while reducing avoidable losses.
              2. Advancing Sustainability Goals: By simulating processes in the virtual world, companies can minimize physical prototyping and reduce waste. AI-driven optimization within the DELMIA virtual twin helps manufacturers fine-tune their operations to consume less energy and use fewer raw materials, directly contributing to their sustainability commitments.
              3. Fostering Continuous Innovation: When the risk and cost associated with testing new ideas are lowered, innovation flourishes. Manufacturers can experiment with novel factory layouts, new automation strategies and different production workflows within the safety of the virtual twin. This agility allows them to adapt quickly to changing market demands and stay ahead of the competition.

              The partnership between Dassault Systèmes and NVIDIA is about more than just combining two powerful technologies. It’s about establishing a new, scientifically validated foundation for industrial AI. By integrating NVIDIA’s physical AI libraries into DELMIA, we are empowering manufacturers to build the autonomous, efficient and sustainable factories of tomorrow, today.

              • Data & AI
              • Digital Strategy
              • Digital Supply Chain

              Author

              Kevin Janzen

              Published

              12 February 2026

              Estimated Read time

              5Mins

              Kevin Janzen, CEO of Gaming & EdTech AI Studio at Globant, on how AI will change the way games are made and expand the market

              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.

              Learn more at globant.com/studio/games

              • Data & AI
              • Digital Strategy
              • People & Culture

              Author

              JP Cavanna

              Published

              11 February 2026

              Estimated Read time

              5Mins

              JP Cavanna, Director of Cybersecurity at Six Degrees, on balancing the risks and benefits of AI in cyber defence strategies

              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.

              Learn more at Six Degrees

              • Artificial Intelligence in FinTech
              • Cybersecurity
              • Cybersecurity in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Dan Brightmore

              Published

              9 February 2026

              Estimated Read time

              2Mins

              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…

              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 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 hiring filters 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 across 50 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.

              • Data & AI
              • Digital Strategy

              Author

              Dan Brightmore

              Published

              27 January 2026

              Estimated Read time

              6Mins

              Some Europe & Middle East CIOs anticipate up to 178% ROI on AI investments, with further efficiencies expected as Agentic AI scales

              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 our StoryHub.

              • Data & AI
              • Digital Strategy

              Author

              Ash Gawthorp

              Published

              22 January 2026

              Estimated Read time

              6Mins

              Ash Gawthorp, CTO and Co-founder of Ten10, on building the right foundations to shape the AI era in the UK

              A recent study shows that UK businesses expect to increase their AI investment by an average of 40 percent over the next two years, following an average spend of £15.94 million this year. With investment surging, the UK is clearly in the fast lane, but the question is whether that momentum will convert into real, durable strength.

              This rapid acceleration places the UK at a pivotal moment in its ambition to lead in artificial intelligence. Investment is rising, government focus is strengthening, and organisations across every sector are exploring AI at pace, creating a sense of real momentum. However, anyone who has experienced previous technology cycles will recognise the familiar tension that emerges during periods of rapid progress and optimism. Breakthroughs often attract significant attention and capital before entering a more grounded, sustainable phase.

              The pressure today is not on AI as a whole. Instead, it is focused on a specific path, where belief in ever-larger transformer models delivering general intelligence continues to grow. This progress has been remarkable, but it represents only one path within a much broader AI landscape. As excitement reaches its peak, the market will inevitably stabilise. The long-term value will come through robust engineering, strong talent pipelines, and successful deployment in real-world environments.

              The task now is to use this moment wisely. Long-term success depends on building deep capability at home, rather than relying on hype or outsourcing key foundations to external providers that sit outside our oversight and control.

              The Limits of Scale as Strategy

              A significant share of today’s investment is based on the assumption that increasing compute and model size will inevitably lead to artificial general intelligence (AGI). Transformer architectures have delivered extraordinary capability and accelerated progress in ways few predicted. They remain powerful systems for prediction and pattern recognition across language, images and other data.

              However, scale is not a guarantee of general reasoning or broad intelligence. Many researchers believe that transformative progress may require developments beyond today’s dominant architecture. If that proves correct, the markets surrounding large closed models will experience a natural cooling. This would be an adjustment based on speculative expectation, not a failure of AI as a discipline. The industry would then shift toward approaches that prize clarity, modularity and measurable outcomes. Engineering discipline and architectural flexibility will matter far more than sheer size.

              One Architecture Cannot Become a National Dependency

              AI will continue to advance. The question for the UK is whether it builds capability that can evolve alongside that progress, or whether it locks itself to a narrow set of global platforms. A handful of model providers currently influence pricing, model behaviour and development cycles. When enterprises rely entirely on opaque APIs, they inherit changes without knowing why outputs shift, how models adapt or when pricing dynamics move. That introduces fragility that grows over time.

              Some experimental use cases can tolerate opacity, but critical public services and regulated industries cannot. Lending, diagnostics, fraud detection and other high-stakes applications demand clarity over how decisions are formed and how logic stands up to scrutiny. In those environments, transparency and auditability shift from abstract ideals to essential operational requirements.

              If the UK intends to embed AI deeply into essential systems, it must champion architectures that allow observability, explainability, control and replacement. Dependence on decisions made offshore is not a foundation for long-term strength.

              Specialised Agents Reflect How Sustainable Systems Evolve

              A practical and resilient approach to AI is already taking shape. Rather than depending on a single model to handle every task, organisations are assembling systems made up of specialised components. This mirrors the way effective teams work, where roles are defined, responsibilities are clear, and handovers are structured. One model transcribes speech, another classifies information, and a third retrieves or summarises content. Each performs a focused function that can be observed, validated and improved.

              This modular design makes systems easier to maintain and evolve. New components can be adopted without rewriting entire frameworks. If performance changes or drift appears, individual parts can be evaluated or replaced without widespread disruption. This reflects long-standing engineering principles that value clarity, observability and the ability to substitute components when better options emerge.

              Financial efficiency supports this approach as well. Running powerful frontier models for every interaction introduces cost and latency that scale quickly. Task-specific agents can often deliver the same outcome faster and more economically. Across thousands of interactions, the savings and performance gains become significant.

              Engineering as the Anchor of Trustworthy AI

              As AI becomes embedded in real systems, success relies on foundational engineering practices. Observability, continuous testing, performance monitoring and controlled deployment are essential. These are not new concepts created for AI, but long-established techniques that have been adapted to a new class of technology.

              In early exploratory phases, it can be tempting to treat large models as something separate from traditional software systems. However, the moment AI begins to influence real decisions, the fundamentals return. Enterprises must be able to trace behaviour, explain recommendations and ensure consistent reliability, while regulators expect clarity and boards seek evidence-based decisions around technology choices, cost structures and risk.

              Organisations that approach AI as engineered infrastructure, rather than a mysterious capability, will be far better equipped to scale safely and confidently.

              Building Skills that Make Capability Real

              The UK is fortunate to have strong research institutions, a sophisticated regulatory mindset and a robust software talent base. To convert these strengths into durable national advantage, investment in skills must expand beyond narrow data expertise. Data scientists remain crucial, but sustainable AI delivery depends equally on software engineers, cloud specialists, machine learning specialists, testers, governance experts and operational teams who run systems at scale.

              Leading organisations recognise that AI delivery is a multidisciplinary effort. As architectures become more modular, value will flow from those who can integrate, monitor and guide AI systems responsibly. The UK must ensure that thousands of professionals have access to this training and experience. Real leadership emerges when capability is widely shared, not concentrated in a small group.

              Governance that Accelerates Innovation

              Strong governance does not slow innovation. It accelerates meaningful adoption by building confidence. When organisations can demonstrate transparency, control and reliability, AI can extend into more critical functions.

              For national strategy, this becomes a competitive advantage. Industries that manage financial and clinical outcomes are not resistant to technology. They simply require evidence that systems behave consistently and transparently. If the UK excels in building AI that is observable, testable and replaceable, trust will grow and adoption will move faster.

              Shaping a Resilient AI Future

              Every technology cycle begins with excitement and eventually settles into maturity. Those who succeed through this transition are the ones who invest in capability while enthusiasm is high. When the current market resets, leadership will belong to those with engineering depth, system agility, responsible governance and the skills to integrate specialised intelligence across complex environments.

              The UK has an opportunity to define this standard. Strength will come from transparency, interoperability and the ability to adapt to model and architecture changes without disruption. It is a quieter strategy than making declarations about imminent artificial general intelligence, yet it builds the resilience required to lead over the long term.

              The future will reward systems that can evolve, remain auditable and operate securely at scale. With the right foundation, the UK can shape this era of AI not through scale alone, but through excellence in engineering, governance and talent. That foundation is the true measure of AI power, and now is the moment to build it.

              Learn more at ten10.com

              • Data & AI
              • Digital Strategy

              Author

              Joe Logan

              Published

              12 January 2026

              Estimated Read time

              4Mins

              Joe Logan, CIO at iManage, on the need to avoid the hype, manage cybersecurity, focus on ROI and balance change management to get the best results with AI

              Across the enterprise, AI promises transformational power – however, it’s not as simple as just plugging it into the organisation and instantly reaping the benefits. What are some of the top things CIOs need to focus on to avoid any pitfalls, unlock its value, and best position themselves for success with AI? 

              1) Separate the Hype from Reality

              Here’s what hype looks like: using AI to “radically transform the way you do business” or to “accelerate comprehensive digital transformation” or – heaven forbid – to “completely change our industry.” These are big statements – and absolutely dripping with hype.

              Getting real with AI requires identifying specific use cases within the organisation where a particular type of AI can be deployed to achieve a specific goal. For example, maybe you want to reduce customer churn by 20% and have identified an opportunity to use chatbots powered by large language models to provide more effective customer service. That’s what reality looks like.

              In separating the hype from reality, organisations gain the added benefit of clearing up any misconceptions – at any level of the organisation – about what AI can and can’t do, thus performing an important “level set” around expectations.

              2) Understand the Implications for Cybersecurity

              On one side, any AI tool you’re using has access to data, and that means that access needs to be controlled like any other system within your tech stack. The data needs to be secured and governed, and issues around privacy, sovereignty, and any other regulatory requirements need to be thoroughly addressed.

              As part of this effort, organisations also need to be aware of the security measures required to protect the AI model itself from bad actors trying to manipulate that model. For example: prompt injection – inputs that prompt the model to perform unintended actions – can affect the model and its responses if not carefully guarded against.

              Securing your AI system is one side of the coin; the other side is understanding how to apply AI to cybersecurity. There are a growing number of use cases here where AI can help identify risks or vulnerabilities by analysing large amounts of data, helping organisations to prioritise the areas they need to focus on for risk mitigation. 

              In summary? While any usage of AI will require you to “play defence” on the security front, it will also enable you to “play offence” more effectively. In that sense, AI has multiple implications for cybersecurity.

              3) Focus on the Right Kind of ROI

              When it comes to ROI for any AI investments, don’t narrowly focus on absolute numbers when it comes to metrics like time savings or cost savings. While well-suited to industrial workplaces that are churning out widgets every day, absolute numbers can be an awkward fit when applied to a knowledge work setting.

              The advice here for any knowledge-centric enterprise is: Don’t get hung up on the idea of actual dollars and cents or a specific number – instead, look for a relative improvement from a baseline. So, rather than saying “We’re going to reduce our customer acquisition costs by $100,000 this year”, it’d be more appropriate to focus on reducing existing customer acquisition costs by 10%. Likewise, don’t focus on each junior associate in the organisation completing five more due diligence projects per calendar year; look to complete due diligence projects in 30% less time.

              4) Give Change Management its due

              Change management has always mattered when it comes to introducing new technology into the enterprise. AI is no different: Successful adoption requires a focus on people, process, and technology – with a particular emphasis on those first two items.

              A major challenge is educating the workforce with an eye towards improving their AI literacy – essentially, enabling them to understand what’s possible and how they can apply AI to their daily workflows. 

              Know that a centralised model of control that dictates “this is how you can experiment with AI” is probably going to be ineffective. It will be too stifling for innovative individuals in the organisation. Far better to provide centres of excellence or educational resources to those people who are most inclined to take the initiative and move forward with AI experiments in their team or department. 

              One caveat here: It’s essential to have guardrails in place as teams and individuals experiment with AI, to prevent misuse of the technology. That’s the tightrope that CIOs need to walk when introducing AI into the organisation. Striking the right balance between “total control” and “freedom to explore, but with appropriate oversight and guardrails”. 

              The Future of AI Depends on what CIOs do next

              The promise of AI is massive, but only if CIOs adopting the technology focus on the right areas. And that means filtering out the hype, keeping security implications top of mind, redefining ROI, and guiding change with a steady hand. By paying attention to these areas, CIOs can safely navigate a path forward with AI. And ensure that it isn’t just a technology with promise and potential, but one that delivers actual enterprise-wide impact.

              Learn more at iManage

              • Cybersecurity
              • Data & AI
              • Digital Strategy

              Author

              Jon Abbott

              Published

              7 January 2026

              Estimated Read time

              5Mins

              Jon Abbott, Technologies Director of Global Strategic Clients at Vertiv, asks how we can build a generation of data centres for the AI age

              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.

              Discover more at vertiv.com

              • Data & AI
              • Digital Strategy
              • Infrastructure & Cloud

              Author

              Jan Van Hoecke

              Published

              5 January 2026

              Estimated Read time

              5Mins

              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

              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.

              Learn more at imanage.com

              • Artificial Intelligence in FinTech
              • Data & AI
              • Digital Strategy

              Author

              Santo Orlando

              Published

              8 December 2025

              Estimated Read time

              5Mins

              Santo Orlando, Practice Director – App, Data and AI Services at Insight, on how your organisation can level up with Agentic AI

              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.

              Learn more at insight.com

              • Data & AI
              • Digital Strategy

              Author

              Kyle Hill

              Published

              28 November 2025

              Estimated Read time

              6Mins

              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

              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. 

                    Find out more at ans.co.uk

                    • Artificial Intelligence in FinTech
                    • Data & AI
                    • Digital Strategy

                    Author

                    Cathal McCarthy

                    Published

                    10 November 2025

                    Estimated Read time

                    4Mins

                    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

                    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.

                    • Data & AI
                    • Digital Strategy

                    Author

                    Dan Brightmore

                    Published

                    5 November 2025

                    Estimated Read time

                    5Mins

                    Interface hears from Emergn CTO Fredrik Hagstroem on approaches to AI best practice that can drive positive business transformations

                    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.”

                    Find out more from Emergn

                    • Data & AI
                    • People & Culture

                    Author

                    Dan Brightmore

                    Published

                    4 November 2025

                    Estimated Read time

                    3Mins

                    Join thousands of attendees in Dubai for the 2nd annual Artificial Intelligence & Data Science conference and find out what’s new in Data & AI

                    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!



                    • Data & AI
                    • Digital Strategy
                    • Event Newsroom
                    • Events
                    • People & Culture

                    Author

                    Dan Brightmore

                    Published

                    7 October 2025

                    Estimated Read time

                    3Mins

                    Samsung and OpenAI Announce Strategic Partnership to Accelerate Advancements in Global AI Infrastructure

                    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.

                    • Digital Strategy

                    Author

                    Nell Walker

                    Published

                    29 August 2025

                    Estimated Read time

                    2Mins

                    Collaborating with Amdocs has been a game-changer for Telkom. Here’s why.

                    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.”

                    Read the full Telkom and Amdocs story in the latest issue of Interface Magazine.

                    Author

                    Dan Brightmore

                    Published

                    24 July 2025

                    Estimated Read time

                    2Mins

                    Read in brochure format

                    Accenture is helping SSEN Transmission manage hundreds of infrastructure projects vital to achieving the UK’s Net Zero ambition. Effective delivery…

                    Accenture is helping SSEN Transmission manage hundreds of infrastructure projects vital to achieving the UK’s Net Zero ambition. Effective delivery required addressing fragmented data and disconnected tools that can slow the flow of information between systems. SSEN Transmission sought a partner to help reshape its approach for data-driven execution on capital projects.

                    Meeting the Digital Challenge with Accenture

                    SSEN Transmission partnered with Accenture to embrace automation and digitisation in response to increasing project demands, a challenge reflected across the wider Capital Projects sector. Through the adoption of BIM (Building Information Modelling) and the implementation of Integrated Project Management (IPM), which was developed with Oracle and Microsoft, this collaboration laid the groundwork for more connected ways of working and continues to promote transformation across the organisation.

                    Key Benefits Delivered

                    Accenture supported with IPM (Integrated Project Management) and Building Information Modelling (BIM) customised to meet specific needs and achieve key goals: 

                    • Digitise processes for a single unified environment
                    • Unify data for a standardised and trusted source of truth
                    • Create a scalable platform for delivering capital projects

                    “With a unified real-time view of project data, SSEN Transmission has improved efficiency and strengthened collaboration across internal teams and with external partners. This allows for more time focused on higher value insight-led work, supporting better outcomes, faster decisions and much more agile delivery”

                    Huda As’ad, Managing Director, Capital Projects & Infrastructure, UKI

                    Building for the Future

                    More than a solutions provider, Accenture helps with strategy and issupporting SSEN Transmission’s continued focus on refining best practice for smooth project delivery. The partnership is helping to evolve ways of working and strengthening the digital foundation for future readiness.

                    “Our collaboration is built on a strong digital foundation that can scale with SSEN Transmission’s growing needs. By unifying systems, data, and process, we are enabling the faster adoption of new capabilities and supporting the shift towards a fully data-driven capital project delivery”

                    Nithin Vijay, Managing Director, Industry X – Capital Projects & Infrastructure

                    Accenture: A Partner for the Journey

                    Transformation is a journey that begins with the right foundation across people, data and process. It also requires a digital partner that brings together the best of industry experience, process excellence and technology to:

                    • Develop a clear, actionable strategy for digital and data transformation
                    • Embed industry best practices to optimise processes and drive continuous improvement
                    • Enable smarter, more consistent delivery aligned to a long-term vision, from strategy through to execution

                    And that’s where Accenture makes its mark, helping clients navigate the journey with confidence.

                    Learn more about how Accenture is supporting SSEN Transmission on its digitisation journey with Huda As’ad, Managing Director, Capital Projects & Infrastructure, UKI and Nithin Vijay, Managing Director, Industry X – Capital Projects & Infrastructure

                    • Digital Strategy
                    • Infrastructure & Cloud
                    • Sustainability Technology

                    Author

                    Sean Galea-Pace

                    Published

                    8 May 2024

                    Estimated Read time

                    7Mins

                    Satya Mishra, Director, Product Management at Amazon Business, discusses how CPOs have become an important voice at the table to drive digital transformation and efficient collaboration.

                    Harnessing efficiency is at the heart of any digital transformation journey.

                    Digitalisation should revolve around driving efficiency and achieving cost savings. Otherwise, why do it?

                    Amazon is no stranger to simplifying shopping for its customers. It is why Amazon has become a global leader in e-commerce. But, business-to-business customers can have different needs than traditional consumers, which is what led to the birth of Amazon Business in 2015. Amazon Business simplifies procurement processes, and one of the key ways it does this is by integrating with third-party systems to drive efficiencies and quickly discover insights. 

                    Satya Mishra, Director, Product Management at Amazon Business, tells us all about how the organisation is helping procurement leaders to integrate their systems to lead to time and money savings.

                    Satya Mishra: “More than six million customers around the world tap Amazon Business to access business-only pricing and selection, purchasing system integrations, a curated site experience, Business Prime, single or multi-user business accounts, and dedicated customer support, among other benefits.

                    “I lead Amazon Business’ integrations tech team, which builds integrations with third-party e-procurement, expense management, e-sourcing and idP systems. We also build APIs for our customers that either they or the third-party system integrators can use to create solutions that meet customers’ procurement needs. Integrations can allow business buyers to create connected buying journeys, which we call smart business buying journeys. 

                    “If a customer does not have existing procurement systems they’d like to integrate, they can take advantage of other native tools, like a Business Analytics dashboard, in the Amazon Business store, so they can monitor their business spend. They can also discover and use some third-party integrated apps in the new Amazon Business App Center.”

                    Why would a customer choose to integrate their systems? Are CPOs leading the way?

                    Satya Mishra: “By integrating systems, customers can save time and money, drive compliance, spend visibility, and gain clearer insights. I talk to CPOs frequently to learn about their pain points. I often hear from these leaders that it can be tough for procurement teams to manage or create purchasing policies. This is especially if they have a high volume of purchases coming in from employees across their whole organisation, with a small group of employees, or even one employee, manually reviewing and reconciling. Integrations can automate these processes and help create a more intuitive buying experience across systems.

                    “Procurement is a strategic business function. It’s data-driven and measurable. CPOs manage the business buying, and the business buying can directly impact an organisation’s bottom line. If procurement tools don’t automatically connect to a source of supply, business buying decisions can become more complex. Properly integrated technology systems can help solve these issues for procurement leaders.”

                    Satya Mishra, Director, Product Management at Amazon Business

                    Beyond process complexity, what other challenges are procurement leaders facing?

                    Satya Mishra: “In the Amazon Business 2024 State of Procurement Report, other top challenges respondents reported were having access to a wide range of sellers and products that meet their needs, and ensuring compliance with spend policies. 

                    “The report also found that 52% of procurement decision-makers are responsible for making purchases for multiple locations. Of that group, 57% make purchases for multiple countries.

                    “During my conversations with CPOs, I hear them say that having access to millions of products across many categories through Amazon Business has allowed them to streamline their supplier quantity and reduced time spent going to physical stores or trying to find products they’re looking for from a range of online websites. They’ve also shared that the ability to ship purchases from Amazon Business to multiple addresses has been very helpful in reducing complexity for both spot-buy and planned or recurring purchases. Organisations may need to buy specific products, like copy paper or snacks, in a recurring way. They may need to buy something else, like desks, only once, and in bulk, at that. Amazon Business’ ordering capabilities are agile and can lessen the purchasing complexity.”

                    How should procurement leaders choose which integrations will help them the most? 

                    Satya Mishra: “At Amazon Business, we work backwards from customer problems to find solutions. I recommend CPOs think about what existing systems their employees may already use, the organisation’s buying needs, and their buyers’ typical purchasing behaviors. The buying experience should be intuitive and delightful. 

                    “Amazon Business integrates with more than 300 systems, like Coupa, SAP Ariba, Okta, Fairmarkit, and Intuit Quickbooks, to name just a handful. With e-procurement integrations like Punchout and Integrated Search, customers start their buying journey in their e-procurement system. With Punch-in, they start on the Amazon Business website, then punch into their e-procurement system. With SSO, customers can use their existing employee credentials. Our collection of APIs can help customers customise their procure-to-pay and source-to-settle operations. This includes automating receipts in expense management systems and track progress toward spending goals. 

                    “My team recently launched an App Center where customers can discover third-party apps spanning Accounting Management, Rewards & Recognition, Expense Management, Integrated Shopping and Inventory Management categories. We’ll continue to add more apps over time to help simplify the integrated app discovery process for customers.

                    “Some customers choose to stack their integrations, while others stick with one integration that serves their needs. There are many possibilities, and you don’t just have to choose one integration. You can start with Punchout and e-invoicing, for example, and then also integrate with Integrated Search, so your buyers can search the Amazon Business catalog within the e-procurement system your organisation uses.”

                    Are integrations tech projects?

                    Satya Mishra: “No, integrations should not be viewed as tech projects to be decided by only an IT team. Integrations open doors to greater data connectivity and business efficiencies across organisations. Instead of having disjointed data streams, you can connect those systems and centralise data, increasing spend visibility. You may be able to spot patterns and identify cost savings that may have gotten lost otherwise. 

                    “It’s not uncommon for me to hear that CPOs, CFOs and CIOs are collaborating on business decisions that will save them all time and meet shared goals, and integrations are in their mix of recommendations. 

                    “One of my team’s key goals has been to simplify integrations and bring in more self-service solutions. In terms of set-up, some integrations like SSO can be self-serviced by the customer. Amazon Business can help customers with the set-up process for integrations as well.”

                    How has procurement transformed in recent years?

                    Satya Mishra: “Procurement is no longer viewed as a back-office function. CPOs more commonly have a seat at the table for strategic cross-functional decisions with CFOs and CIOs.

                    “95% of Amazon Business 2024 State of Procurement Report respondents say the purchases they make mostly fall into managed spend. Managed spending is often planned for months or years ahead of time. This can create a great opportunity to recruit other stakeholders across departments versus outsourcing purchasing responsibilities. Equipping domain experts to support routine purchasing activities allows procurement to uplevel its focus and take on higher priorities across the organisation, while still maintaining oversight of overarching buying patterns. It’s also worth noting that by connecting to e-procurement and expense management systems, integrations provide easy and secure access to products on Amazon Business and help facilitate managed spend.”

                    What does the future of procurement look like?

                    Satya Mishra: “Bright! By embracing digital transformation and artificial intelligence to form more agile and strategic operations, CPOs can influence the ways their organisations innovate and adapt to change.”

                    Read the latest CPOstrategy here!

                    • Procurement Strategy

                    Author

                    Dan Brightmore

                    Published

                    12 October 2023

                    Estimated Read time

                    2Mins

                    Read in brochure format

                    Nigel Greatorex, Global Industry Manager at ABB, on how digital technologies can support decarbonisation and net zero goals

                    Nigel Greatorex is the Global Industry Manager for Carbon Capture and Storage (CCS) at ABB Energy Industries. He explains how digital technologies can play a critical role in the transition to a low carbon world by enabling global emissions reductions. Furthermore, he highlights the role of CCS and how challenges can be overcome through digitalisation.

                    Meeting our global decarbonisation goals is arguably the most pressing challenge facing humanity. Moreover, solving this requires concerted global action. However, there is no silver bullet to the global warming crisis. The solution requires a mix of investment, legislation and, importantly, innovative digital technologies.

                    Decarbonisation digital technologies

                    It’s widely recognised decarbonisation is essential to achieving net zero emissions by 2050. Decarbonisation technology is becoming an increasingly important, rapidly growing market. It is especially relevant for heavy industries – such as chemicals, cement and steel. These account for 70 percent of industrial CO2 emissions; equal to approximately six billion tons annually.

                    CCS digital technologies are increasingly seen as key to helping industries decarbonise their operations. Reaching our net zero targets requires industry uptake of CCS to grow 120-fold by 2050, according to analysis from McKinsey & Company. Indeed, if successful, it could be responsible for reducing CO2 emissions from the industrial sector by 45 percent.

                    A Digital Twin solution

                    ABB and Pace CCS joined forces to deliver a digital twin solution. It reduces the cost of integrating CCS into new and existing industrial operations. Simulating the design stage and test scenarios to deliver proof of concept gives customers peace of mind. Indeed, system designs need to be fit for purpose. Also, it demonstrates the smooth transition into CCS operations. Additionally, the digital twin models the full value chain of a CCS system.

                    Read the full story here

                    • Sustainability Technology

                    Author

                    Dale Benton

                    Published

                    16 October 2019

                    Estimated Read time

                    10Mins

                    Read in brochure format

                    In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau…

                    In early 2019, the Voluntary Health Insurance Scheme (VHIS) was introduced in Hong Kong by the Food and Health Bureau to regulate indemnity hospital insurance plans offered to individuals, with voluntary participation by insurance companies and consumers. The VHIS was designed as a means of encouraging and supporting customers to purchase private healthcare services and for Koh Yi Mien, Managing Director Health and Employee Benefits at AXA Hong Kong, this scheme represents a broader transformation of healthcare and insurance services. “Currently, the demand on healthcare in Hong Kong in the public sector is incredibly high with very long waiting times and waiting lists,” she explains. “As a result, people just aren’t getting timely access to treatment. The private sector in Hong Kong, which is world-class, has capacity. So, if we can rebalance and shift some of the elective work from public to private, it will free up more people to use the public service in a timely fashion.”

                    Yi Mien also points to a global drive for greater transparency, accountability, use of data and technology as well as promoting customer choice as key drivers of change in the insurance space. “It’s no longer a case of simply providing reimbursement to people when they need treatment,” she says. “It’s about being the patient’s partner throughout their whole life so that when they need healthcare, whenever and wherever they are, we are there to help and support them in their times of need.” 

                    The modern-day insurance customer is very different from the customer of the past. We live in times of greater access to information through the advent of social media and the increasing influence of the Internet and this has resulted in insurance customers being more knowledgeable about their conditions and asking more questions of their doctors than ever before. As a result, the balance between the customer and the healthcare provider is becoming more equitable. “Customers and patients, as a result, are becoming more demanding,” says Yi Mien. “Gone are the traditional ideas that doctor knows best. It’s not uncommon for patients to see their doctor with a list of demands, while expecting to be serviced.”

                    Running parallel to becoming more knowledgeable and demanding is the use of smartphones and how it has created a culture of service in an instant. When customers purchase etiquettes or use banking services, they expect the ability to be able to access and complete these transactions and services via their smartphone devices. Fewer and fewer people are accessing physical bank branches and the healthcare insurance sector, despite being still very traditional, is feeling the effects of this instant demand. “Healthcare is a very traditional sector sure, but asking patients or customers to book weeks in advance and telling them they don’t really have any choice is becoming increasingly unacceptable and so healthcare becomes a commodity,” says Mie Koh. “They, like any other customer, vote with their feet and want 24/7 access to quality healthcare without waiting directly from us as the insurer.”

                    The informed customer and patient have also transformed the relationship between customer and doctor. It is no longer a bilateral relationship and the entire healthcare ecosystem works to provide services from prevention right through to treatment. The result? Insurers like AXA work with customers before they are sick and encourage them to maintain their health, but they also work with clients during their illness and even afterwards AXA will continue to treat them in their rehabilitation. “During their healthcare journey, customers want some handholding in order to navigate the very complex healthcare system, to make sure they get the right healthcare provider, doctor and hospitals that are best for them in their time of need,” says Yi Mien. “This can only happen if we are using digital so that it becomes more real time.”

                    AXA has been embracing technology for a number of years to be able to serve and effectively work with its customers. It achieves this by starting with the definition of a product, because the product sets the rules. Yi Mien highlights that the rules would often be how AXA would spell out the terms and conditions, the provisions, but these rules also set the customer expectations. Throughout late 2018 and 2019, AXA has invested in digital to enable its customers to buy online, service online, claim online and check-up online. The company also launched a servicing app called Emma, a ‘digital companion’ that enables even faster service. Yi Mien describes this app as a true “health companion”. She is also keen to highlight that the technology is only part of the story. AXA has built a vast medical network with some of the leading hospitals and doctors and customers simply having to log into their companion app to be able to access this network at the touch of a button. “All they need to show is their digital card, their e-card, and with the QR code, the provider just scans it. All of the data is downloaded and all they need to do is sign, get their treatment, and then when they discharge, just sign that they have received the treatment and off they go,” she says. “The hospital will bill AXA directly so there’s no out of pocket. The data is also transmitted to AXA which means that we have more comprehensive and more reliable data.”

                    Comprehensive and reliable data is crucial to the technology journey of AXA, but it is also integral to the customer journey. With a customer’s entire electronic medical records stored effectively and securely, as Yi Mien notes, why would they go anywhere else? The data that an insurer handles is often complex in nature, but this data is processed through artificial intelligence, with AI being used to process claims more effectively and interpret the information to allow AXA to create rules and algorithms to better serve its customers. AXA also utilises AI through its companion app Emma. “Emma is our chatbot,” explains Yi Mien. “Emma has been built up based on a multitude of Q&As that our customer services team have recorded and collected over many months and years. As we continue to build, and more people use Emma, then the quality of the responses she has in her arsenal will improve.” In the first two months of operations, Emma recorded an accuracy level of 50%. Yi Mien firmly believes that as more people engage with Emma and as a result, the chatbot will evolve and become more of a real-time navigator that can direct customers across the whole ecosystem.

                    In the global discussion around AI, the topic of transparency is often a key point of debate. With governments around the world shining a spotlight on exactly what data is collected and how it is used, AXA ensures that it maintains an open and transparent dialogue with its customers. As customers engage with Emma and the companion app, they can at any time request their transcripts. Should they choose to speak with a human adviser, all calls are recorded and again they can access those recordings should they wish. Not only is this an example of AXA complying with global governing laws, it also highlights that the customer is at the very heart of every decision it makes and it maintains this as it continues to implement new technologies. “If you look at banking as an example, we all are so used to accessing our bank accounts at any time, be it through our phones or online,” says Yi Mien. “If we want to speak to someone, we can. If we want to go into a branch, we can. I believe this is the way to go with insurance as well. We make it easy for our customers to contact us. We are doing everything we can to allow that.”

                    “Healthcare is quite personal, so we are doing what we can to allow customers to speak to people, should they not wish to use our chatbot. These are very personal journeys and digital is still in its early days, so we really have to provide different avenues and channels for our customers to contact us.”

                    As Yi Mien notes, AXA designs its customer journey by starting at the product and going through all the way to treatment. The company makes every decision with the customer’s perspective in mind. As a doctor by trade, Yi Mien sees that all new products are designed by doctors because they understand how the patients move throughout the whole healthcare ecosystem. When AXA designs new products, it does not operate within a vacuum. It has a customer insight group, where around 1,000 customers operate as a real-time focus group in which AXA can test its products with. “When I think about future products, we will test with this group of people and get feedback to see whether we are aligned with the current customer need. So, it’s not just technology per se, but actually meets a customer’s needs,” she says. “One other area to make sure that we are doing the right thing, because technology also costs money, is to make sure that we are very robust in what we do. AXA is unique in that we sell life insurance, health insurance, employee benefits, and we also have P&C. So, being a multi-line insurer, we have the opportunity of having one approach and cross-selling across the business lines, which is a fantastic opportunity. We can only do that through technology.”

                    Over the course of her career, Yi Mien has been a champion of the transformative effect of technology in becoming a greater enabler for healthcare and healthcare insurance providers around the world. One area in particular that is close to her heart is the mental health space. In Hong Kong, the waiting time to see a psychologist is close to two years and if patients were to seek private care, it is an expensive solution. “Look at a country like Hong Kong, or Australia, they are so vast that there just aren’t enough practitioners to cover the breadth of the geography. Digital is the solution,” she says. “Digital enables people to seek, support and care at the time that is most convenient for them.”

                    “In the past two to three years, there has been a proliferation of digital tools. Recent studies have shown that digital tools are as good as, if not better, than in-person therapy because customers prefer to talk to a robot rather than face-to-face because they feel that the robot is not judging them.”

                    Another example that Yi Mien highlights is in the UK, where a VR program has been developed by programmers that is therapy through gameification. The treatment is consistent every time and because of its mobile platform, it is accessible. “We can provide it where you work,” she says. “That’s just one example as to how we can destigmatise mental health through technology.”

                    AXA operates within a broad healthcare ecosystem, an ecosystem made up of partners, providers and doctors and Yi Mien stresses that in the future of insurance, it will be impossible for insurers to control the ecosystem. “I don’t foresee a future where that happens,” she says. “Partnerships are incredibly important. Things are moving so fast there’s no way we can catch up alone. We need to have partners, collaborators, who are working together to ensure we are at the top of our game and at the forefront of innovation.”

                    “Over the course of our lives, so many different things can happen and so people will need better care and support. By having a collection of data that represents our customer’s needs we are able to push or suggest services that better meet those needs. In order for us to do that, we need to have players collaborate in the ecosystem. It’s imperative.”

                    As AXA continues this digital growth journey, the next few years will be defined by improving the agility of the digital companion in order to improve the interaction with customers. AXA will also be looking at developing a digital marketplace in which customers can go shopping within an AXA owned digital platform. For Yi Mien, though, the future is clear for AXA and in order to be successful, she feels it’s down to one thing. “AXA has a clear digital strategy for sure, where it will transform its digital system and build new IT infrastructure to transform the customer experience,” she says. “But the technology is only one part of the story.”

                    “Unless we can transform the customer experience to deliver a service they truly value, then technology doesn’t do anything. It’s important to recognise that technology is enabling us to transform healthcare, to make it easier, faster, and cheaper for people to receive care. That means in the long-term, sustainable healthcare and health services, which fits into sustainable insurance.”