Manufacturing and production businesses that deploy integrated digital technologies will be best placed to navigate today’s complex supply chains, close the data gap to reduce greenhouse gas emissions, and attract and retain the workforce of the future, as Gino Hernandez, Head of Global Digital Business for ABB Energy Industries, explains.

Heavy, asset-intensive industries today face the challenge of balancing the urgent need to reduce energy consumption and CO2 emissions, in line with sustainability targets, while optimizing production and profitability

Energy accounts for more than three-quarters of total greenhouse gas (GHG) emissions globally, so reducing Scope 1, 2 and 3 emissions along the length of the supply chain is a priority for all energy producers and suppliers. Not only does it drive more sustainable operations, but it enables them to comply with evolving environmental legislation, protect their reputation and license to operate, and attract and retain the next generation of talent.

The digital revolution

Digitalization – the application of strategies and solutions across process automation, data analytics and remote technologies – is the key to unlocking business value. Armed with innovations like artificial intelligence (AI), the Internet of Things and Big Data, operators can seamlessly integrate renewables from the grid. This drives scale and brings the cost curve down on new, clean energy sources, and decarbonization technologies like carbon capture and storage (CCS) and hydrogen.

Companies that digitally connect and share knowledge with original equipment manufacturers, clients and suppliers will be in a stronger position to navigate today’s complex value chains and reduce GHGs. Having the right tools and expertise to deliver more effective, centralized data is key, allowing businesses to link multiple applications together to enable integrated operations, industrial intelligence, and monitoring and reporting.

Data: a challenge and opportunity

Consider this: the average plant uses only 20 percent of the data it generates, an astonishing statistic given that data is the lifeblood of modern industry. However, the idea that simply pooling data and then applying AI will automatically provide actionable insights is flawed. After all, not all information is useful information: what is required is a conceptual understanding of how the data got in that pool, and, most importantly, how it can best be applied to improve efficiency and sustainability.

Data is nothing without context. A gap exists between quantity and quality, whereby businesses are generating data but lack the knowledge or digital tools to cherry pick the most useful, analyze it and then apply it. Data can also be complicated due to its shelf life; if it isn’t used in a timely manner, its insights grow less valuable. In both these instances, automated workflows can help contextualize and interpret the blizzard of operational data captured from industrial processes.

Generative AI (GenAI), for example, has proven to reduce industrial and GHG emissions by up to 20 percent and deliver savings of up to 25 percent through energy optimization. 

By applying ABB’s energy management optimization system (EMOS), which monitors, forecasts and optimizes energy consumption and supply, ABB helped one customer save £1m and 13,000 tons of emissions a year, by making data-driven decisions.

The competition for talent

Attracting and retaining the next generation of digitally literate talent – young people who can work in harmony with innovations like AI, not in spite of them – is crucial. That said, the huge archive of knowledge acquired by veteran employees must not be allowed to exit with them when they retire. 

The digital transition must therefore be supported by the transformation of processes and people. In addition to training and upskilling, businesses need to establish succession plans to ensure that the existing expertise within the operation is successfully integrated with new skillsets and perspectives from Gen Z and Gen Alpha. 

Again, this is where digital can help. GenAI has the potential to add real business value by increasing workforce capacity and capability by factors of hundreds as part of a transition strategy and skills evolution.

Integrating new, sustainable energy sources

For the past 10 years, ABB and Imperial College London have been developing a dedicated carbon capture pilot plant – the only facility of its kind in the world – with the latest control technology and equipment to train the engineers of the future in carbon capture. ABB is working on digital twin track and trace technology, which uses surface and subsurface modelling and simulations to visualize and optimize carbon from the point of source to the point of injection, to ensure safe and sustainable operations.

In the emerging green hydrogen market, ABB is partnering with IBM and Worley on an integrated digital solution for facility owners to build assets more quickly, cheaply and safely, and operate them more efficiently. Meanwhile, ABB and Canadian company Hydrogen Optimized are advancing the deployment of large-scale green hydrogen production systems to decarbonize hard-to-abate industries such as metals, cement, utilities, ammonia, fertilizers and fuels.

These projects are all committed to unlocking the potential of digital technologies across the energy value chain, giving heavy industries vital tools to future-proof their businesses by reducing their carbon footprint while maximizing production and profits. 

Earlier this summer, the Government announced plans to transform the technological offering across the public sector and — in particular — to move from an analogue to a digital NHS. This is part of a broader plan to modernise the country’s existing technology and capitalise on opportunities created by emerging platforms. 

However, some key factors are preventing the transition, namely existing legacy systems that are deeply embedded into the public sector. But why is it so critical that the Government tackles its tech debt, and how can it benefit from major digital modernisation?

Tackling the tech debt

This isn’t necessarily a new focus for the public sector; indeed, tackling ageing tech has been on both the previous and the current Governments’ critical paths. However, Sir Keir Starmer has made several public statements highlighting the importance of delivering true digital transformation in the public sector and it seems as if there is more desire for change than in the past. 

More broadly the Government’s policy agenda, led by figures such as Peter Kyle, Secretary of State for Science, Innovation, and Technology, reflects a focus on digital reform. 

This includes proposals to “rewire Whitehall” to streamline services and enhance government performance through technology and highlight need and commitment to digital transformation as a driver for more efficient and effective public services.

Where did the tech debt come from? 

Before looking at why the modernisation of existing infrastructure is so important, we should examine how we’ve reached a position where the majority of public sector technology continues to be hugely outdated. 

I’d like to stress that I’m not attributing fault or placing blame but recognising a variety of challenges in public spending decision making – particularly where spending taxpayers’ money on technology isn’t ‘sexy’ and doesn’t win votes. 

Public perception rather than balanced decision-making has potentially shaped the outcome of several significant decisions in recent years. This is perhaps understandable. Few are willing to explain to the public why the Government elected to spend millions (or indeed billions) on improving public sector technology, rather than building a new hospital, for example. 

Moving the dial on IT spending in the public sector

More broadly, though, there are several barriers to overcome in order to move the dial on digital transformation in the public sector. The federated nature of UK governmental departments, for example, has played a part, and pressure on public finances since the start of the Global Financial Crisis in 2008 has also contributed to the lack of change.

This meant that the Government pushed transformation projects further down the line until we arrived at a stage where it was overwhelming to consider even tackling them. However, rather than looking to fix everything in one go, in reality, we need to put building blocks in place to ensure we’re creating robust, but flexible, technology foundations that are appropriate for the future.

Public sector IT procurement

The procurement process in the public sector is another key factor. For a variety of reasons, the temptation has been to select the off-the-shelf or all-encompassing approach, and to opt for the largest provider, rather than the suppliers most suited to the project in question. 

Sometimes, biggest will be best, but in most cases, it benefits the Government to have a broad ecosystem of partners of all sizes in place, rather than just going for the decision that appears safest on paper. This is partly because of pressure placed on Crown Commercial Services and a lack of resources that have meant non-specialists are often making buying decisions, rather than industry experts. 

The skills shortage 

Skills are potentially the key issue underpinning the broader lack of focus on modernising public sector technology. There have been precious few ministers at the top level of either the current or previous Governments with technology backgrounds. 

When you consider the role that tech now plays in the running of the country and the importance that the Prime Minister is placing on transforming our digital offering, this seems like a missed opportunity. 

By sourcing more civil servants and senior politicians with an acute understanding of the potential that modernisation holds, the effective means of doing so and the risks of not moving forward, we would hopefully see more nuanced and strategic decision-making. 

But why is tackling the tech debt so important? 

Ageing technologies are by no means just an issue for the Government and its agencies. They’re also impacting several other markets. This notably includes financial services, where some of the most established financial institutions are struggling to keep pace with emerging challenger brands. 

However, within the public sector, these issues are harder to tackle and change takes longer because of the scale involved. 

When you add up inefficiencies across multiple areas, it’s hardly surprising that the UK trails behind almost every other major nation in productivity. Every year, UK workers waste millions of hours processing forms, manually inputting data, and fixing errors. The country could get this time back by upgrading some of the older, legacy systems currently in place. To misquote Henry Ford, a faster horse isn’t the answer.

Equally, this isn’t only a productivity issue, but a security one too. You won’t need me to tell you that most legacy systems are more vulnerable to threats than newer ones. While still robust, these older platforms contain well-known, well-documented vulnerabilities. 

The addition of newer environments like cloud and mobile has only expanded these weak spots and made them more open to attack. When you consider that – like a chain – your cyber security is only as strong as your weakest point, and it is public data and finances at risk, the scale of the challenge becomes clear. 

In addition, these older platforms also prevent the Government from fully embracing and leveraging emerging technologies, which could help to support further productivity improvements in the future. They also cost more to maintain. At a time when the discourse is more focused on cutting unnecessary expenditure, significant savings could be made in the long-term by modernising public sector tech.  

As usual, there’s no silver bullet 

Unfortunately, there’s no simple, universal solution to make this transformation a reality. While everyone is talking about AI, and suggesting it’s the fix for every problem, Whitehall is littered with the remnants of those who heralded other breakthroughs (like Blockchain, the metaverse, and countless more) as the silver bullet. 

GenAI is – and will only become more of – a valued tool. But here, there are a range of different needs that the Government needs to meet. The process requires nuance, understanding and informed decision-making.

With more services moving online and public costs coming under the microscope, now is the time to deliver long-term technological change that meets the needs of the UK of 2050, let alone 2024. Encouragingly, the new Government seems to recognise the importance of modernisation, however deep-rooted issues that are blocking real change need to be tackled before we can move forward.

Business budgets and priorities for 2025 are on the table. Now is the time for businesses to make the case for their digital transformation ambitions. 

Although the race to AI is now at full throttle, many businesses are still grappling with old legacy systems. It’s high time to address these issues, while paying close attention to rapidly evolving regulation and sector specific standards. 

Adoption of AI offers exciting opportunities, but it can feel overwhelming. For businesses looking to take their digital transformation to the next level in 2025, here are the three activities they need to piroritise.

1. Seriously look at AI and what it can do for your processes and your company. 

But, be careful who you partner with. With so many new AI companies out there, it feels a lot like the dot–com–boom at the moment.

AI really is the 4th industrial revolution. It almost feels the same as digital did 10-15 years ago when everyone was creating self-service products and services. 

One learning we can take from the early 00s is that businesses must adapt to the latest technologies to remain competitive. 

The challenge that businesses have is: who to turn to? Which AI platforms and service providers have sound foundations? With so many start-ups, it feels a lot like the like dotcom boom. It can be difficult to know which are legitimate and which have good, long-term business plans. 

Thankfully, regulatory bodies have started putting guide rails, controls and protections in place. New standards like ISO/IEC 42001 have been set out for establishing, implementing, maintaining and continually improving an AI management system. 

These standards are still coming out and evolving across sectors. This is why it’s important to do your research and to be aware and informed of the regulatory landscape in the sector where you operate. In the UK, the government has released the AI Regulation Policy Paper. In the US, the Federal Trade Commission (FTC) has advice on automated decision making. For Europe, the EU AI Act is destined to become a global standard like GDPR.

Another challenge is how AI affects cybersecurity. Are you protected against the ever-evolving threats of machine learning as a tool to attack, or deepfake videos impersonating your CFO? Working towards or requesting these standards will give you confidence in the AI partners you chose and the processes you embed into your own operations.

2. Review your legacy platforms, suppliers and skills. 

Outsourcing isn’t always the best option, think about the right sourcing to ensure that you have the support that you need.
Before the end of the year, it’s important to ask, when was the last time you reviewed your suppliers? 

Businesses are used to outsourcing to save money, but we often don’t review these arrangements. The changing global economy means that outsourcing isn’t always the most effective option – costs have gone up significantly in India over the last year, for example. 

Organisations can make big savings, while improving quality, speed and flexibility, by bringing some services back in house. At LRQA we’ve found the UK a particularly strong market for tech skills. We’ve hired about 100 roles since start of the year and remote working means that we can now draw on talent from across the country.

Added to this, we still see many companies with dilapidated systems and old platforms hampering their operations. There is now some urgency to move away from these. 

The risk for digital transformation is that many technical details and old processes are not documented, and often only existing in people’s heads. If you get the migration from these platforms wrong, it can cause problems for your business and your customers. 

The solution must be a planned and controlled migration, but before you need to reverse engineer these outdated processes, sometimes with the added challenge of the person who designed them having left the business.

3. Write your digital transformation to do list. 

Cost and roadmap for 2025 then speak to your investors and/or your board to get these costs approved.

Digital Transformation is a mixed bag. Some businesses have invested already, some are behind the curve as they’re working with legacy systems and platforms while others have cash constraints. There was a big investment during the pandemic – because it was necessary – but since then it has eased off. 

Now businesses are in another round of investment, being driven by AI. Smaller companies tend to have less transformation funds, but what people need is often the same – data, self-service and AI to help make decisions.

If you’re making the case for AI to investors, you need to set out your priorities for staying competitive and protecting your business, but there is also an argument for growth. Once embedded, AI driven processes provide efficiency and are easy to scale.

Get ready to get ahead

Digital transformation and the adoption of AI is crucial to gaining the competitive edge and the future success of your business. By setting up your plans for 2025 now, you can make sure you’re ahead of the competition and not left on the sidelines.

Last year, spending on digital transformation reached $2.15 trillion globally. Around the world, businesses in all sectors face continued pressure to streamline operations and provide better service to their customers. This total is expected to reach $3.9 trillion by 2027. For many organisations, though, the complexity surrounding the creation and ongoing maintenance of new technology-driven processes continues to stand in the way of turning digital investment into impact. According to McKinsey & Co’s research, around 70% of digital transformation efforts fail. At the same time, just one in eight digital transformation initiatives meeting their objectives.

Economic pressures continue to take their toll on budgets. As such ensuring digital transformations are successful has never been more critical. However, the journey isn’t always a simple one. Starting a digital transformation project can often be perceived as time-consuming, complex, and expensive. Processes are hard to find, out of date, and difficult to understand. Often, teams that inherit processes experience a loss of context and control over them. Meanwhile, employees impacted by the transformation are often averse to change, making the thought of overhauling existing processes far from inviting.

But it doesn’t have to be this way…

The secrets to success:

1. Knowing where to start…

… can often be complex and discouraging for those getting started with digital transformation. Before a process can be fixed or optimised, it must first be uncovered and analysed. Fortunately, there are tools available that can take the pain away from process discovery. They do this by creating a detailed map of all workflows scattered across the entire business.

Process mapping is the practice of looking at all the actions that your organisation does and visualising them in the form of a map. These processes can occur daily, monthly, or even annually, be it small or large. By creating this map, organisations can get a better understanding of how they are going to accomplish their goals. Mapping processes also allows the business to understand the direct and indirect impacts that changing one process might have on another, as well as the knock-on effect this could have on people, skills, systems, compliance and cost. 

2. Centralising processes

… is the next step on the journey to success. Digital transformation projects often require the development and improvement of multiple processes. Therefore, using Platform-as-a-Service technologies that can help centralise and connect these processes in an easy-to-use interface is essential. Challenges and causes for transformation are also generally not limited to a single department. Therefore, it’s important that multiple stakeholders across the business can have sight of these processes and their impact.

3. Getting employee buy-in… 

… and engaging key stakeholders, however, is half the battle when embarking on a digital transformation project. Collaboration is key when it comes to success, so those driving transformation projects must involve those whom it will impact, from the offset. Ultimately, your team needs to understand what the problem is, and why you’re changing it. The projects that see the most success are led by those who take the end-user on the journey with them, rather than presenting them with the end product to find it either isn’t user-friendly or doesn’t fully address the original need.

Utilising human-centric tools for digital transformation is crucial to overcoming this. Day-to-day employees can only be invested in the project if they can be involved in the development.

However, often due to complexity, transformation efforts are siloed to developers and those with technical skills. By embracing Platform-as-a-Service software that maps and centralises processes with a highly collaborative and intuitive user interface, organisations can engage business users, IT professionals, and process experts in mapping workshops, where employees can see their changes brought to life in real-time, and the impact created. Collaboration of this kind can also help to spark new ideas for further improvements throughout the transformation journey.

4. Having access to the necessary tools for change… 

…may seem obvious, but often process mapping software used by businesses does exactly what it says on the tin, leaving the transformation of these processes and finding the tools to do so, another task in itself. This is where adopting process mapping technology that can integrate with workflow automation tools such as RPA, AI and low-code development, is extremely beneficial. Being able to easily adopt these tools accelerates transformation efforts, meaning change happens faster, more efficiently, and with better results.

Ultimately, the secret to a successful digital transformation project is to empower those responsible for building processes to do so simply. Offering them the ability to document and continually improve the processes consistently and at scale, by removing duplication and eliminating errors, saves time.

By adopting robust and holistic tools that centralise the storage of process creation, whilst offering the integration of technology such as automation to uncover actionable insights and efficiencies, organisations can transform at speed. And this ensures a strong ROI on their digital transformation investment.

In recent years, digital transformation has promised to revolutionise organisations of all sizes, making them more agile to compete with nimble startups boasting innovative business models and products. However, almost two years on from ChatGPT’s entry into the mainstream, the hangover from this initial hype cycle is setting in. 

While most executives view digital transformation as essential for success, only 7% of CIOs say they are meeting or exceeding their digital transformation targets, according to CI&T’s recent findings. This stark discrepancy highlights a significant hurdle: the gap between aspiration and reality. 

The initial blueprint for digital transformation was clear: Agility, collaboration, customer focus, and experimentation. The mantra was “fail fast, learn fast,” emphasising rapid pivoting and adaptation. 

Enter the advent of powerful AI tools like GPT-4 and DALL-E 2, introducing a new layer of complexity to companies’ ongoing digital transformation journeys. Rather than a new technology, the evolution of digital transformation is intricately linked with the rise of AI technologies. As organisations look to achieve the agility and innovation promised by digital transformation, the integration of AI becomes a critical enabler.  

Moving into a more mature age of AI  

The initial phase of digital transformation laid the groundwork for agile methodologies and a culture of experimentation. Now, AI represents the next frontier in this journey, pushing the boundaries of what organisations can achieve through digital innovation. To fully leverage AI’s potential, organisations must overcome the fear of disruption and embrace the calculated risks necessary for AI deployment. At CI&T, we are helping organisation move beyond siloed experiments to scaling AI initiatives that deliver real value. 

However, fear of brand damage, business disruption, and reputational risk has gripped organisations and their boards, hindering widespread AI adoption. This reluctance is understandable, especially in light of the recent data breaches at OpenAI, where user data was inadvertently exposed due to a bug in the ChatGPT interface. Such incidents have heightened awareness of the risks associated with AI, prompting many companies to adopt a more cautious approach. 

The current state of experimentation reflects this fear. Most efforts remain siloed, focusing on internal proofs-of-concept that rarely translate into tangible customer-facing applications. A 2023 McKinsey report highlights that while many companies have successfully developed proofs of concept, few have fully scaled these projects. This risk aversion results in missed opportunities. 

How can companies take calculated risks and leverage Generative AI to deliver on its promises and potential for their customers? 

A successful Generative AI deployment strategy, like any effective digital transformation, requires calculated risks. While it’s important to explore and learn from emerging technologies such as Generative AI, it’s crucial to avoid developing solutions that are impressive but don’t actually generate value for the company. 

A smart risk-taking strategy must include building robust contingency plans, incorporating loss provisions, and crisis communications plans and employing best-in-class software engineering practices. For example, Google’s Bard AI project has demonstrated the importance of continuous testing and iteration. After the initial launch, which was met with mixed reviews, Google swiftly implemented feedback loops and A/B testing to refine the AI’s performance, demonstrating a commitment to both innovation and risk management. 

Generative AI models can be unpredictable because of their nature and frequent updates. Therefore, practices like A/B testing, canary deployments, DevOps, robust observability, and triaging systems are essential to ensure brand safety and minimise the risk of reputational damage. Additionally, an MLOps function to manage AI infrastructure changes automatically is vital. 

It’s also essential to target AI initiatives where the potential for harm is minimised.  Companies must assess and research the types of risks to take based on their industry and potential consequences. For instance, while a retail brand may risk its brand loyalty among a set of customers, a tech error for a pharmaceutical company may result in severe consequences for patients. By focusing on specific business areas and customer segments, we see regularly how organisations can maximise benefits while thoroughly managing risks. 

Building Trust and Transparency in AI 

Open and transparent communication builds trust with customers, which is vital for gaining acceptance of new AI-powered solutions. Salesforce data reveals a significant trust gap in AI, with only 45% of consumers confident in its ethical use. To bridge this divide, it is imperative to build strong customer relationships centred on understanding and meeting their needs. 

The reality is that competitors are actively exploring and deploying these technologies, potentially disrupting market share. For example, we worked with YDUQS, a Brazilian-based company in the education sector, to incorporate GenAI into its solutions and enhance the student journey. As a result, the company was able to achieve efficiency gains, reduce lead time in operational activities, and position itself as an innovator in the industry. With big tech companies like Amazon integrating GenAI into retail operations, they are setting a new standard, leaving competitors little choice but to innovate or risk obsolescence. 

Don’t be afraid to experiment, but do so responsibly. Learn from failures, iterate quickly, and use this knowledge to propel your organisation to the forefront of the next technological revolution. 

Balancing Risk and Reward 

The challenge lies in balancing risk and reward. It’s about taking calculated risks, understanding where to experiment, and building customer trust. Customer engagement is pivotal. Without a deep understanding of customer needs and preferences, it’s difficult to deploy AI solutions effectively and responsibly. 

The rewards of successful AI integration are significant, but so are the risks. As the digital transformation hangover sets in, the question is not just about readiness but about the strategic foresight to navigate the complex landscape of AI responsibly. 

A new report by e-signature and software developer Docusign highlights a counterintuitive trend in European organisations. Despite increasing investment, developing technologies, and widespread consensus on its importance, progress towards digital transformation has “stalled” across Europe. 

The report, Accelerating Digital Maturity in 2024: How Businesses Can Break the Productivity Paradox, highlights a range of factors as driving this “digital transformation paradox”, including resistance to change, growing digital skill gaps, and resource-based barriers like lack of time, budget, and staff. 

Frustration is intensifying with the digital transformation progress paradox. Over a third of business decision makers said they would consider leaving their company in the next 12

months, a significant rise compared with 31% in 2023. 

Digital maturity describes how strongly a company’s digital infrastructure is built to achieve the business’ overall goals. A higher level of digital maturity is directly linked to business success. According to Docusign’s research, organisations that are considered digital leaders in their sectors generate 50% more revenue than their less digitally mature peers.

Digital first does not mean digitally mature 

Digital maturity is an obvious value creator for businesses. However, Docusign’s research found that progress towards it has stalled. Today, fewer than half (46%) of all organisations considering themselves to be highly or very highly digitally mature. 

Despite this fall in digital maturity, investment in digital transformation is rising. Docusign found that 74% of businesses reported increasing their investment in, and adoption of, digital technologies over the past year. This was up from 70% in 2023. Clearly, the takeaway is that digital transformation is about more than investment. Businesses that aim to overtake their peers and digitally transform clearly need to pair digital investment with “deeper structural and cultural change”, according to the report. “It’s a sure sign that while digital technologies and digital transformation efforts are evolving in tandem, businesses are struggling to keep pace,” adds the report. 

Despite half (51%) of businesses surveyed reporting the digital maturity level of their competitors to be high. Around the same number said they feel slightly behind in terms of their own organisation’s digital maturity (46%). However, the majority (56%) of businesses still considered themselves to be a “digital first organisation.” An additional 31% said they were working towards becoming one. Digital maturity is obviously a near-ubiquitous goal, despite many companies struggling to attain it.

“A willingness to self-define as ‘digital first’ may be linked to the fact that many businesses have increased investment in digital technologies in the last 12 months,” notes the report. However, given the digital maturity paradox, Docusign’s research suggests “either these efforts aren’t untapping the desired results, or companies are yet to see the return.”

In today’s digital landscape, cybersecurity threats pose an ever-present danger to enterprises. These threats occur with alarming frequency—every 39 seconds on average. These attacks not only jeopardise sensitive data but also undermine customer trust and inflict significant financial losses.

The Inadequacy of Traditional Authentication Methods

Traditional cybersecurity defences, once considered robust, now face unprecedented challenges in an era of sophisticated cyber threats. Take the ransomware attack on JBS Foods in 2021, which disrupted operations across multiple continents. This incident underscored the vulnerabilities of static authentication measures. It also highlighted the urgent need for more resilient security strategies like continuous authentication.

In January 2023, a cyberattack forced the Royal Mail to suspend deliveries to several countries (source). This disruption not only impacted operations but also raised concerns about the security of critical infrastructure. This attack could have been mitigated by continuous authentication verifying access attempts and promptly detecting unauthorised activities before they could inflict widespread damage.

Continuous Authentication: A Proactive Defense Strategy

Continuous authentication stands out as a proactive approach to cybersecurity. The approach relies on continuously verifying user identities through dynamic factors such as facial recognition, behavioural biometrics, and device attributes. This real-time monitoring enables organisations to detect anomalous behaviours and potential threats promptly, mitigating risks and preserving business continuity.

Looking Ahead: Embracing Innovation in Cybersecurity

As enterprises navigate an increasingly complex threat landscape, embracing innovative authentication solutions like YEO‘s is crucial. By integrating advanced technologies and robust security measures into their existing platforms, organisations can enhance their cybersecurity posture, comply with stringent regulatory requirements, and safeguard customer trust in an era defined by digital transformation.

Continuous authentication emerges as a pivotal element in the future of cybersecurity, empowering enterprises to combat evolving threats proactively. By learning from past incidents and adopting cutting-edge security measures, businesses can fortify their defences, protect critical assets, and uphold their commitment to data privacy and integrity in an interconnected world.

Even within a technology industry known for hyperbole, the growth of the artificial intelligence (AI) market is incredible. Last summer, a Bloomberg Intelligence report proposed that the AI market would reach $1.3 trillion over the next 10 years. A significant increasse from a market size of ‘just’ $40 billion in 2022. Most recently, June 2024 saw AI giant NVIDIA hit a value of $3tn, eclipsing Apple. The iPhone maker immediately responded with the launch of Apple Intelligence AI. And the race between major tech firms shows no sign of slowing.

A critical part of this growth is that AI has rapidly evolved beyond being the exclusive province of large corporations. The ease of use enabled by AI interfaces has led to businesses of all sizes embracing the technology.

In particular, high-growth, medium-sized businesses (MSBs) recognise the potential of AI. In the UK, there are approximately 35,900 such MSBs. For these organisations, the possibility to automate tasks and accelerate decision-making is a huge source of competitive advantage. However, successfully embracing AI requires a strong foundation of digital infrastructure that such organisations often overlook.

The right framework

An AI-ready digital infrastructure can be broken into four key areas:

High-performance networking: Inference AI applications need networks that reduce latency to the edge for in-flight analytics and real time data processing. Training AIs need high, 100 Gbps bandwidth to the data centres or cloud where large training datasets are stored. This connectivity must be highly reliable, with multiple connections and bandwidth across resilient, fast networks, and secure data transfer protocols.

Secure data storage: Artificial intelligence lives and dies by the data it is ‘trained’ on. If an MSB is set to embrace AI, it must have a secure, scalable data storage solution to house both the structured and unstructured data that is used for training and running AI models. 

Data management and governance: Extracting value from the data used by AI, requires effective data management practices. And it is not just a commercial imperative. MSBs need robust data governance frameworks in place to ensure compliance regulations. Establishing secure pipelines to automate the collection, organisation, and preparation of data for AI is crucial.

Regional edge: While cloud computing offers immense power for some AI use cases, it can introduce latency issues for applications that require real-time decision-making. Regional edge computing puts processing power closer to the source of data. This reduces latency and enables faster processing of time-sensitive data. This has already shown its value in applications such as predictive maintenance or real-time video analytics.

Diving deeper – AI and the regional edge

MSBs typically operate in geographically dispersed locations, dealing with real-time or near-real-time data streams in order to serve markets faster. In these contexts, regional edge computing offers significant advantages. 

• Reduced latency: By processing data closer to its source, regional edge computing minimizes the time it takes for data to travel between collation and processing. This is crucial for applications requiring immediate insights and decisions, such as real-time anomaly detection for fraud, or optimising dynamic supply chain logistics.

• Improved bandwidth efficiency: Edge computing reduces the amount of data that needs to be sent back to centralised facilities, freeing up valuable bandwidth and lowering network costs.

• Enhanced security: Sensitive data can be processed at the edge before being sent to the cloud or elsewhere. This reduces the security risks associated with data transmission over long distances.

With these sorts of benefits, it is little surprise that a recent report found that 77% of executives say their technology architecture is either very critical or critical to the overall success of their organisation.

When it comes to AI, regional edge opens a pathway to cost-effective deployment. In the recent 2024 Trends in Datacenter Services & Infrastructure report from S&P Global Market Intelligence, the analysts note: “…the rise of AI inference workloads that may also have latency and data location requirements could further drive edge deployments.”

The same report also notes that “…use cases may vary enormously, so it may be hard for vendors to gain scale in an atomized market. The ecosystem of vendors, operators, financing and network providers at the edge is evolving rapidly.”

Partnering to drive AI 

The idiosyncratic demands of MSBs looking to embrace AI will be best served by regional digital infrastructure providers. These providers can partner with MSBs to address new use cases in the face of profound industry challenges.

Given the fragmented market, and technological demands, building and managing a complex foundation for AI is daunting for MSBs. This is especially true in the face of skills shortages throughout AI and infrastructure alike. 

As far back as 2021, the UK Government identified that nearly half (49%) of UK firms had been affected by a lack of technical AI skills, and almost a third (32%) had been similarly impacted by a lack of non-technical capabilities.

Similarly, 2023 figures from the Uptime Institute showed that more than half (53%) of UK data centre operators report having difficulties in finding new talent, up from 38% in 2018.

In the face of this, the expertise and guidance of experienced technology partners carries major benefits. It means a faster implementation, optimised costs and compliance with demanding data privacy regulations.

By leveraging a digital infrastructure partner that can combine high-performance networking, secure data storage, cloud options, and the emerging power of regional edge computing, MSBs can approach AI methodically and with minimal disruption to ongoing business, whilst navigating the opportunities that AI will undoubtedly bring.

Extreme weather, climate-related events and environmental disasters are growing in frequency and becoming more costly for business, governments, and consumers. When the lights go out due to severe storms, flooding, wildfires or worse, it’s energy networks that are often most at risk. 

Extreme weather conditions have doubled power outages in the US over the past 20 years, according to the U.S. Department of Energy. It estimates that extreme weather events cost businesses around $150 billion per year, with power outages a significant part of these costs, shutting down operations and even large parts of the country for days at a time. More extreme temperatures are also pushing the power grid to its maximum.

Last year’s 4 July weekend saw some of the hottest days on record in the US, while parts of southern Europe and north Africa were hit by record-breaking April temperatures, made worse by droughts and wildfires.

In the UK, we are seeing similar patterns. Utility Week’s 2023 UK utilities risk report published in association with Marsh highlights the growing concerns of water and energy companies, with the risk of extreme and unpredictable weather surpassing cybersecurity threats for the first time. This follows a period of record-breaking storms, flooding and heatwaves pushing infrastructure and network resilience to the brink.

No longer fit for purpose

It’s something we can longer ignore. We now live in a world of changing climate and weather extremes that are having a major impact on our systems, while our grid infrastructure is no longer fit for purpose thanks to outdated technology, in many cases, and under-investment in communications networks.

Understanding and being able to source the location of power outages is vital for emergency maintenance teams when problems occur. It means utilities can quickly identify problems and act, whether that’s making sure power can be restored or redirected, if necessary, to help minimise disruption in service delivery.

The loss of vital communications and information is a real possibility if a storm or flood damages the network infrastructure in the case of cellular networks.

As a viable use case for wireless mesh networking technology, outage management enables utilities to work out where problems are with a much greater degree of accuracy and level of granularity. This then enables them to reroute power if a tree has fallen on a cable, for example, by disconnecting that part of the network, and then reconnecting the power through a different circuit.

Improving outage recovery times

With the number of extreme weather events increasing, it’s no surpsise that utilities are starting to invest in smart technologies. These include advanced weather prediction tools in response to power outages caused by extreme weather and disasters.

Published earlier this year, our research among senior professionals from US utility companies shows they are looking to boost network resilience with the use of IoT and smart technologies and tools.

The results are not unique to US companies. We would expect to see the same attitudes elsewhere with respondents adopting new approaches to new problems – to help mitigate outages and improve recovery times, while also looking at ways to control rising costs.

What’s clear is the need to build extreme weather events and other disasters related to climate change into the risk profile of any utility company regardless of region.

While advanced weather prediction tools topped the list of initiatives to bolster network resilience, our research showed there is also a growing focus on renewable energy integration and grid modernisation. IoT devices can facilitate the integration of renewable energy sources like wind and solar into the grid, while monitoring the energy generated, adjusting the flow in accordance with current conditions, and integrating fluctuating renewable energy assets.

Utilities are also looking to predictive maintenance analytics and enhanced communications to help improve outage recovery times, together with the use of drones and robotics to inspect assets. It’s perhaps no surprise that AI is also finding its way into a range of utilities applications. Our respondents recognise the opportunities to integrate AI as part of their network infrastructure, with use cases ranging from energy consumption forecasting to automated fault detection.

Final thoughts

The research confirmed an increasing reliance of utilities in their access to data from their network. Any new technologies and applications are only as good as the communications network infrastructure supporting them. It’s impossible to overstate the importance of reliable, robust and secure networking. By combining IoT with other smart technologies like grid sensing technology, utility companies can better respond and manage these extreme events, measure and cope with the outcomes.

For more on the Wi-SUN Alliance utilities research findings, see here.

Through complex algorithms, Generative AI (GenAI) creates content including imagery, music, text, and video – all on demand. It can also be used to perform tasks and process data. This makes tedious tasks more manageable and, therefore, allows the technology to save considerable time, effort, and money. This is transformational for many industries, especially for teams looking to boost operational efficiency and drive innovation.

Compliance as a sector has traditionally shown hesitancy when it comes to implementing new technologies. In general, compliance takes longer to acquire and roll out new tools due to caution over perceived risks. Many compliance teams will not be using any AI, never mind GenAI. However, this hesitancy also means that these teams are missing out on significant benefits. At the same time, other less risk-averse industries are experiencing the upside of having the technology implemented into their systems. 

Therefore, it’s time that compliance teams look for ways to leverage all forms of AI, specifically GenAI. Nevertheless, this needs to move forward in safe and tested ways, without introducing unnecessary risk. 

Dispelling fears

GenAI is a new and rapidly developing technology. It’s only natural therefore that many compliance teams have some reservations surrounding how they can be applied safely. Particularly, teams tend to worry about sharing data. This information might then be used as part of training and become embedded into future models. It is also unlikely that most organisations would share data across the internet without following strict privacy and security measures. 

When thinking about the options for running models securely or locally, teams are likely also worried about costs. Much of the public discussion surrounding generative AI has focussed on the immense costs of preparing the foundation models. 

Additionally, model governance teams within organisations will worry about the black box nature of models. This casts a spotlight on the potential for models to embed biases towards specific groups. Once embedded at the foundational level, this bias can be difficult to spot. 

However, the good news is that there are ways to use GenAI to overcome these concerns. This can be done by selecting the right models which provide the required security and privacy. Then, compliance teams need to fine-tune those models within a strong statistical framework to mitigate biases. 

In doing so, organisations will need to find the right resources. That could mean data scientists or qualified vendors. Once they do, these resources can be leveraged to support them. However, this may also prove challenging. 

Challenges compliance teams may face

Despite initial hesitancy, analysts and other compliance professionals stand to gain massively by implementing GenAI. For example, teams in regulated industries such as banks, fintechs and large corporations are often faced with huge workloads and resource constraints. Depending on the industry, teams may be responsible for identifying a range of risks – including sanctioned individuals and entities, adjusting to new regulatory requirements and managing huge quantities of data – or a combination of all three.

For compliance professionals, the task of reviewing huge quantities of potential matches can be incredibly monotonous and prone to error. If teams make mistakes and miss risks, the potential impact for firms can be significant – both in terms of financial and reputational consequences. It is not surprising that organisations can struggle to hire and retain staff, leading to a serious skills shortage among compliance professionals as a result. 

So what can organisations in regulated and other industries do to tackle issues of false positives and false negatives associated with modern customer and counter-party screening? It seems GenAI may hold some of the answers.

False positives are where systems or teams incorrectly flag risks, while false negatives are where we miss risks that should be flagged. These errors may come from human error and inaccurate systems, but they are hugely exacerbated by challenges such as name matching, risk identification and quantification. All of which can be mitigated with the right implementation of AI tools including GenAI without sacrificing accuracy.

Using Generative AI in compliance

GenAI can be implemented in various useful ways to improve compliance processes. The most obvious is in Suspicious Activity Report (SAR) narrative commentary. Compliance analysts must write a summary of why a specific transaction or set of transactions is deemed suitable in a SAR. Well before the arrival of ChatGPT, forward thinking compliance teams have been using technology based on its ancestor technology to semi-automate the writing of narratives. It is a task that newer models excel at, particularly with human oversight.

The ability to produce summarised data can also be useful when it comes to tasks such as Politically Exposed Persons (PEP) or Adverse Media screenings. These processes involve conducting reviews or research on a client to check for potential negative news and data sources. Importantly, these screenings allow companies to identify potential risks, preventing the company from becoming implicated or face reputational damage as a result.

When deployed correctly, summary technology can enable analysts to review match information far more effectively and efficiently. With any AI deployment, it is essential to consider which tool is right for which activity and the same is true here. Merging GenAI with other machine learning and AI techniques can provide a real step change. This involves blending both generalised and deductive capabilities from GenAI with highly measurable and comprehensive results available in well-known machine learning models.

For instance, traditional AI can then be used to create profiles. These profilesdifferentiate between large quantities of organisations and individuals, separating out distinct identities. The techniques move past the historical hit and miss processes that saw analysts carry out manual searches. The results of these searches were limited by arbitrary numeric limits. Once these profiles are available, GenAI supercharges analysts even further. 

Final thoughts 

Results from the latest innovations are showing that GenAI powered virtual analysts can achieve, or even surpass, human accuracy across a range of measures. Concerns about accuracy will still likely slow its adoption.

However, it is clear that future compliance teams will benefit heavily from these breakthroughs which will enable significant improvements in speed, effectiveness and the ability to react to new risks and constraints.

To keep up with rising customer expectations and stay ahead of the curve, businesses are continuing to invest in digital transformation to innovate user experiences and enhance operational efficiency. Amid escalating costs and tightened budgets, this endeavour has grown more challenging. As this persists into the second half of the year, organisations need to operate more efficiently, maximising their resources like never before.

Developers under pressure

Software development teams are facing significant demand from the business, as they strive to speed up digital transformation without additional budget or extra staffing resources. To enable success, digital leaders must urgently reduce toil in the development and delivery processes. This has triggered a significant focus on platform engineering, which gives developers a set of reusable tools and components they can use to create software with less manual effort. According to Gartner, 80% of large software engineering organisations will have established platform engineering teams by 2026.

To relieve the pressure, software engineers are taking the lead on building an Internal Developer Portal (IDP) for their organisation, as seen by some of the world’s most innovative companies like Spotify, and founders of the  CNCF project Backstage. Many organisations’ IDPs are built around that same Backstage foundation. This allows them to self-serve provisioning pipelines, testing and infrastructure, without having to build these out for each service or product. This has become even more important as organisations have increased their use of microservices, Kubernetes, and multi-cloud architectures.

Without an IDP, such ecosystems introduce more moving pieces to the tech stack. These ecosystems add to the number of tools and platforms developers rely on to get code into production, and require them to master the configuration of multiple infrastructure types. As a result, developer experience has worsened, and it has become more time-consuming and complex to onboard new team members.

Internal developer portals are taking off

With an IDP, organisations can overcome these problems and lighten the burden for their developers, helping them access the tools and capabilities they need to deploy code, and manage all the services and components they are responsible for – from a single interface. In the same way that a bank’s customers don’t need to think about everything going on in the technology stack when they check their balance in a mobile app, an IDP puts a wrapper around development infrastructure. This means developers can focus on their ideas rather than building staging environments and dealing with deployment processes. What’s more, they can spend more time creating new features, and less time jumping through all the hoops to get their code into production.

As a further benefit, an IDP approach also helps developers to improve the quality and security of their services, without spending significant extra time on testing. With an IDP embedded within their modern software delivery platform, engineering teams can integrate automated testing processes and best practices into the delivery pipeline to ensure all new releases meet strict key performance indicators (KPIs) for performance and reliability. That makes it infinitely easier to ensure code releases are free from vulnerabilities before they enter production.

As a result, developer happiness and morale gets a boost, as teams can get code into production faster and with greater confidence.

Having fewer tools and processes to master also makes it easier to onboard new team members, as developers can commit, build, test, and promote code with knowledge and experience of the organisation’s unique systems. As these benefits become more widely recognised, Gartner estimates that by 2025, 75% of organisations with platform teams will provide self-service developer portals to improve developer experience and accelerate product innovation.

Meeting developer expectations

IDPs have gained traction over the past 18 months, and developers’ expectations of them have increased exponentially too. 

They want a dynamic and fully self-service experience, so they can quickly and easily find the tools and capabilities they need to deploy their code and move onto the next project. Platform engineering teams therefore need to ensure their IDP includes a catalogue of services and documentation that is available for their developers to use.  As developers reach outside their immediate team to use other services, this catalogue-based approach makes it easier for them to consume existing capabilities without an extensive search for help. This further enhances their productivity by removing potential roadblocks while developers wait for support.

Developers should also be empowered to automate simple workflows through their IDP, such as creating a new staging environment. It’s possible to provide frameworks that remove the need for developers to manually trigger repeatable processes, like running tests. IT leaders can enhance these capabilities using scorecards. These allow developers to measure the quality of their services against established KPIs, enabling them to quickly identify any performance issues or vulnerabilities.

Those building their organisation’s IDP also need to account for the fact that developers often have entrenched preferences for the tools and processes that they are used to. As such, it’s important for an IDP to seamlessly integrate with the most popular third-party solutions in the development toolchain. Platform engineers must also maintain security by ensuring that developers only have access to the functionality and data that they need to complete their work. To enable this, IDPs should be ingrained with role-based access control and centralised governance capabilities to ensure the organisation can maintain oversight.

Empowering developers for a sustainable, successful future

As investment in IDPs remains a priority for organisations, they will need a clear strategy for delivering a platform that caters to the needs of their developers now and in the future. Many have started by building their own IDP from the ground up, using DIY know-how. Whilst these approaches may have worked to begin with, they aren’t scalable as a long-term solution. Furthermore, platform engineering teams could undermine the efficiency gains they stood to gain due to the effort and costs involved in operating, managing, and hosting their own custom-built IDP.

Rather than relying on improvised solutions, organisations should explore purpose-built, enterprise grade offerings that streamline the process for creating and maintaining an IDP. Developers can then focus on building and operating their software, alleviating the need for them to construct delivery pipelines – consequently increasing their morale and boosting productivity. 

This will help organisations to run leaner whilst maintaining momentum in their digital transformation efforts, thereby establishing a robust foundation for a sustainable competitive advantage.

As the effects of the COVID-19 pandemic lessen, the retail sector faces a unique challenge. While some consumers are spending more after two years of lockdowns and reduced activity, others are not. Inflation, rising prices, and mass layoffs are creating high levels of economic uncertainty. That uncertainty is translating into unpredictable consumer behaviour at a time when retailers are desperate for certainty. 

Even before the pandemic, the retail sector was struggling to balance rising costs with a changing customer base.

“Margins are stressed from all sides,” noted a report by McKinsey back in 2019. “Higher costs to manage e-commerce supply chains, growing demands from suppliers to pass on raw-material cost inflation, higher investments to match new competition, and steadily rising labour costs,” are all pressuring retailers. 

Today, that uncertainty is mixing with growing economic pressures. The results are threatening to create genuine pain points for retailers.

A recent survey by Prosper Insights & Analytics found that 36% of American consumers were reducing their shopping trips. At the same time, however, 42.4% were found to be shopping more during sales events. These conflicting trends make it difficult to predict demand.

Now, retailers are turning to technology to find their feet. Technology ike intelligent automation and artificial intelligence (AI) will, they hope, help meet the need for predictability, cost-savings, and efficiency. 

A platform approach to retail 

Sanish Mondkar, founder of Legion and ex-SAP exec, came “face to face with the labour crisis during a long road trip across America.”

In an interview with Forbes, Mondkar explains how “It was striking to see labour-intensive businesses like retailers and restaurants having perpetual ‘for hire’ signs outside their locations, but at the same time, employees were changing jobs at a rapid rate and still couldn’t make a living wage.” 

Mondkar’s company operates a workforce management platform he says helps retailers “strike a balance by offering workers the autonomy typically associated with gig jobs while ensuring the stability of hourly positions — without sacrificing labour efficiency.” 

A great deal of the increased efficiency that platforms like Legion offer stems from intelligent automation. Workforce management platforms are leveraging digital tools that can automatically deploy surveys to team members, deliver feedback, confer well-earned awards, and provide instant access to earned wages. 

Most importantly, they can automate scheduling by predicting demand based on past data. “By leveraging data from historical and ongoing operations, local events, weather, and holidays, we assist retailers in obtaining a truly accurate understanding of their expected demand across all customer touch points and locations every 15 minutes,” Mondkar explains. He adds that this enables retailers to ensure the right number of workers are scheduled on particular shifts. 

Digital transformations, depending on who you ask, fail anywhere between 70% and 90% of the time. That’s a high failure rate. According to Gartner, 91% of businesses are currently engaging in some form of digital initiative. An overwhelming percentage (87%) of senior business leaders say that digitalization is a priority.

The IDC’s 2023 FutureScape report notes that “IT isn’t an organisation — it’s the very fabric of the enterprise”, and “we have now entered the era of the digital business, where transformation must be part of enterprise DNA.’’

So, if digital transformation is in our DNA—a fact of life—why aren’t we better at it? 

Many people’s first instinct might be to blame the “digital” part of digital transformation for the universally low success rate. Broken technology, buggy software, and poorly integrated systems are what a lot of people think of when they imagine digital transformations gone awry. However, according to industry experts, this is rarely the case.

“Your tech is fine; it’s the people who are getting in the way,” says Dr Corrie Brock, an Organisational Behaviour Expert and Executive Coach based in Dubai. “You will fail 84% of the time, not because of inadequate technology, lack of organisational capacity or lack of funds… Humans are the problem. And the solution.” 

Through this lens, which favours change management and culture over shiny toys, here’s why your digital transformation is probably in danger of failing. 

Poor communication creates adversity 

A digital transformation is a significant change for an organisation. It can mean relearning processes, changing responsibilities, and pockets of obsolescence. If communication is poor, employees will expect to be laid off as soon as they finish installing the new AI-powered automation platform, and no one is going to willingly take part in their own perceived extinction. Poor communication means they go from being the executors of a digital transformation to its enemies. 

It’s human nature to fear the unknown. Consequently, most employees will resist adopting new software, tools, apps, and processes. This is true even if these innovations promise to enhance their lives. Only educating workers about the use and purpose of new tools will build the necessary trust. Leaders who build trust across their teams, clearly communicate their vision, and support their employees will have a better chance of success than those who don’t. 

Losing sight of the fact that every digital transformation exists to drive business outcomes is a huge red flag. Implementing cool technology because it’s cool is a sure fire way to waste money, time, and the goodwill of the C-Suite. 

If your digital transformation srategy is festooned with cutting edge technology, but that that techology doesn’t actually support your business’ key objectives, it’s time to rethink your strategy. 

Whether it’s moving to the cloud, implementing AI, or integrating an ERP system, organisations are becoming more technologically saturated than ever before. Trillions of dollars of working capital ride on the ability for organisations to transform. Those who fail to successfully embrace new technologies and ways of working will find themselves left behind. 

However, just because digital transformation is the process de jour across every industry, market, and size of organisation, it doesn’t mean success is guaranteed. It certainly doesn’t mean it’s easy. It doesn’t even really mean people have figured out how to do it consistently.  

According to Boston Consulting Group, it’s not just a minority of “troubled companies” that struggle with successfully implementing digital transformations. “Top performers, market leaders, and investor favourites,” are not immune from the threat of a failed transformation. Their research points to the fact that, while 80% of companies plan to accelerate their companies’ digital transformations, a mere 30% of transformations actually succeed in delivering on the objectives set out for them. 

Why do digital transformations fail? 

Every digital transformation is different. A multitude of factors, from the size of the company, its industry, economic climate, company culture, and the technologies being adopted have an effect on both the nature of the transformation and the likelihood of its success. 

However, there are common threads we can point to among the 70% of organisations whose digital transformations fall short of the mark. 

“One of the biggest mistakes I see companies make is trying to boil the ocean — attempting to do everything, all at once,” Ryan Lee, CEO of B2B commerce platform Nautical Commerce (wielding an appropriately maritime metaphor) writes. “In my experience, companies that find success do so by boiling one small pot of water at a time.” 

One of the main reasons digital transformations run aground (if we’re continuing with the nautical imagery, Ryan) is the combination of scope and complexity. The answer? How do you eat a whale, manatee, or other large, elephantine-yet-aquatic creature? 

One bite at a time—a phased digital transformation 

Digital transformation is not a magic wand and transformation is not instantaneous. Digital transformation teams can manage the complexity of digital transformation projects much more effectively by breaking down the project into discrete phases.

A phased approach allows organisations to break down their end goal into smaller steps, understand the requirements at each stage of the journey, and assess the impact of the transformation on the business model by gathering feedback throughout the process.

First, an accurate assessment of the business’ existing digital capabilities (and pain points) is necessary. Next, leadership conducts an internal assessment to evaluate company’s processes, data, and operational performance. Then, the company identifies the technologies required for its digital shift, determines target customers, and analyses their needs (both during and after the transformation). With this information in hand, the company can outline a digital strategy and set objectives for its digital transition. 

These objectives then guide the creation of a comprehensive roadmap alongside the implementation of necessary changes to initiate the digital transformation. Following this, the company focuses on managing these changes within its team, as cultural change is every bit as important as technology adoption. Finally, the business launches its new model. This is accompanied by marketing adjustments aimed at optimising the integration of digital tools within the company.

As argued by Jon Roskill, former head of cloud ERP software maker Acumatica, many decision makers have been “led to believe that digital transformation is a magic trick where they simply wave a digital wand to change a process or two. Poof! Their business is revolutionised.” The reality is that, “In order to ensure a transformation is successful, it’s important to think about it as a continuous process.”