For Unilink, a long-standing provider of secure digital solutions across justice and public safety, AI represents both an extraordinary opportunity and a complex responsibility. Over the last 30 years, we’ve built and deployed digital solutions for prisons, probation services, immigration detention, and secure hospitals. Now, the inclusion of AI across our product suite is opening new possibilities; not simply the automation of routine tasks, but the augmentation of decision-making, personalised offender support, risk modelling, and operational insight.

From digital transformation to digital justice

The Ministry of Justice’s (MoJ) digital strategy sets out an ambitious vision: a justice system that works better for everyone, through the smarter use of data, automation, and design. In 2025, this vision is taking shape. AI and digital technologies now support everything, from digital court processes and secure communication to case management and prisoner engagement. 

AI’s real potential, however, lies in turning complex, fragmented data into meaningful insight. Machine learning models can identify trends in case workloads, forecast resource needs, or highlight where individuals may be at risk of disengagement. Predictive analytics can improve safety and planning, while automation reduces the administrative burden and frees staff to focus on more humane services that are grounded in evidence and insight.

Ethical and transparent by design

However, as AI becomes more deeply embedded in justice operations, ethical and responsible deployment is essential. The sector deals with some of society’s most sensitive personal data, and the consequences of algorithmic decisions can be profound. We believe that for digital justice to succeed, ethical governance and human oversight must remain central.

That means explainable AI – systems that can show how and why a decision was reached. It means rigorous data governance that ensures quality, security, and fairness. And it means maintaining human oversight at every stage. Technology can support better judgment, but it should never replace it. Embedding these principles will help to ensure public trust, protect individual rights, and deliver genuinely fair outcomes. 

Human-centred services

Digital justice is about people as much as technology. When designed around user needs, it can transform the experience of both service users and staff, and make justice more accessible and empowering. Adaptive interfaces can support users with low digital literacy or those for whom English is a second language. Chatbots or virtual assistants can provide 24/7 access to essential information. Data visualisation tools can give frontline workers a clearer view of progress, helping them to intervene earlier and more effectively. 

Crucially, digital services can help individuals to build the digital confidence and skills that extend beyond the justice system, supporting rehabilitation, employability, and reintegration into society, and enabling long-term positive change. In this context, technology isn’t just modernising justice, it’s humanising it. 

Innovations that are shaping 2025 and beyond

Looking ahead, Unilink is focused on three key innovation strands for 2025 and beyond:

1. Adaptive digital services – in-cell devices or wing kiosks that don’t simply offer menus or book visits but learn from user interactions to improve accessibility and engagement
2. Predictive analytics for smarter operations – using AI to anticipate demand and improve resource allocation
3. Integrated data ecosystems – securely connecting justice, health, and rehabilitation data to provide a holistic view of an individual’s journey.

These innovations demonstrate just how AI can enhance transparency, improve outcomes, and make the justice system work better for everyone that it serves.

Towards a digital and ethical future

Across the UK, justice agencies and technology providers are collaborating to make this vision a reality. Companies such as Unilink, which develops secure digital systems for justice, are helping to turn AI’s promise into practical tools that support both efficiency and rehabilitation, from intelligent self-service and communications platforms to data-driven operational insight. The challenge now is to scale innovation responsibly, ensuring that, as technology becomes more capable, it remains accountable, explainable, and human-focused.

Digital innovation has always been about solving real-world problems. In the justice sector, this means using technology to promote fairness, reduce harm, and improve lives. AI is simply its latest manifestation. It’s not being introduced as a replacement for human interaction but as a way to make those interactions more meaningful, informed, and effective, and it must be guided by empathy, ethics, and human insight. 

In our view, the justice system of the future will be digital first, but human always, to enable a smarter, fairer justice system where data informs decisions, digital empowers people, and technology serves the public good. 

Read More AI for good

The post AI and digital innovation – Building a smarter, fairer justice system appeared first on Digital Leaders.

This AI will need to be accessible, transparent and yet intuitive to a wide application-domain experts, who have much common knowledge and constraints, as well as individual blind spots and baggage. This field is rather separate from common data-driven decision-making within data-rich applications, which is grunt, high-frequency, decision-making that has been applied in many areas. It is time for AI to move up to strategic and radical novelty-seeking challenges. 

Many distinct sectors are deeply interested, as there is almost always no prior experience nor relevant case data on the table when they have to make high-stakes calls.  Yet, many existing AI players and users are either conflating both straightforward and hard issues; believing (wishing) that the present AI data-driven paradigm is the only game in town; or else being simply focussed on doing what everybody else is doing (but hopefully better) – transferring supervised and unsupervised decision-making, anomaly detection, object recognition, and all types of data-driven inferences into different fields of applications. 

They are all merely swimming while the next generation AI, working on no-data crises, will be “flying”. 

 And, as Nietzsche said, “He who would learn to fly one day must first learn to stand and walk and run and climb and dance; one cannot fly into flying.”

What is under the hood?

Such AI must be a hybrid, combining a generative layer with a logical-foraging-evolutionary layer. Ideally, this accomplishes two things in response to any given challenge.

1.         Iteratively finding hypotheses (response options), while prizing possibly “novelty”. It is termed “imaginative” or “creative” since such hypothesis generation is not merely regurgitating and combining incremental hypotheses that are presently available.
2.         Refuting or validating any hypothesis, depending on whether it does or does not contradict any sector knowledge or constraints and accepted logic. The rejected hypotheses are termed “fallacious”; while the accepted hypotheses are termed “irrefutable”.

This process can maintain a growing archive of all (so far) irrefutable hypotheses; which is useful in successively defining “novelty” measures that drive types of Novelty search. There are many possible alternative approaches to various elements within both steps. But the principal aim is clear at a high level.

Hybrid AIs such as these have attracted some recent attention during 2025: at Vancouver ICML 2025, where the Exploration in AI Today workshop, where AI moves from exploitation (emulating intelligent functions based on its ability to recombine, paraphrase, and simulate, yet rarely discovering novel things) towards the exploration of new ideas and knowledge discovery.  

The role of government and public funding

Whilst there are obvious radical and entrepreneurial elements in developing next generation AI, the process by which the UK Government supports such innovation is all hobbled by adherence to consensus-seeking peer review; or else by pre-defining fields of interest and applications (thinking inside the box). At a high level, governments desire R&D of radical, distinctive, AI concepts and applications, within “Sovereign AI”, yet the mechanics have little appetite for risk. They should instead champion controversial R&D: as a taxpayer, I want every AI programme to have a policy of investing, say 25% of the resource, into ideas that would not have an expert consensus (and avoid groupthink); and that are so controversial that they would start a fist fight in a pub full of experts. 

It is a problem of framing. The taxpayers want risk and growth from potential high-impact and fail-fast investments, yet the programme managers want to avoid failures and to remove project and business risks. Consequently, the Government confounds its own strategic mission by top-slicing the ambition and risks, and very often deploys expert consensus to justify investment (as “excellence”) to HMT. Furthermore, by pre-defining strategic and focus areas of interest, it maintains groupthink and eschews radical ideas. The government only address the known unknowns. Yet the only thing we know about the unknown unknowns is that they are out there. No wonder that most disruptive paradigm changes and next-generation AI, usually emerge from the venture space. 

Read More AI for good

The post Explorative and creative AI appeared first on Digital Leaders.

The logic of scale is seductive. Organisations like OpenAI, Google DeepMind, and Anthropic argue that bigger models bring predictable gains in reasoning and creativity. But evidence is emerging that this assumption is starting to falter and that generative AI may not sustain the exponential demand for GPUs that so much global investment is built upon.

If that proves true, history offers a useful analogy. In the 1950s and 60s, computing was dominated by vast, centralised mainframes that only the wealthiest institutions could afford. The assumption then, too, was that bigger central terminals would always be better. The real revolution, of course, came later when compute power migrated from research labs to the desks of workers around the world.

Hyperscale models are the new mainframes

Today’s hyperscale language models are our new mainframes: powerful, but centralised, fragile, and controlled by a handful of global firms.

According to RAND (2024), the economics of foundation models are already “precarious,” with soaring compute and energy costs delivering ever-smaller performance gains. Beyond the economic and environmental implications of this, they also raise deeper questions about sovereignty.

AI sovereignty: Beyond data location

True sovereignty means controlling how data is processed, protected, and governed. That’s impossible when relying on models hosted or operated abroad. Even “UK instances” of foreign models cannot change the fact that model training and governance policies are decided elsewhere.

As Harrison Kirby, Author of “Exploring GenAIOps” and co-founder at Great Wave AI, has argued:

“AI sovereignty isn’t just about data location—it’s about control, governance, and accountability. Just think back to COVID-19, when even the closest allies competed for scarce vaccine resources. What happens to our ‘sovereign’ instances of overseas tech when priorities diverge?”

Beyond sovereignty, there are structural security risks. Large models must process data in unencrypted form — meaning every sensitive input, from defence intelligence to patient records, is briefly visible inside a foreign-controlled system. How long can that remain acceptable?

Finally, sovereignty must mean accountability.  In recent weeks, both AWS and Azure have experienced global scale outages created from errors incurred in the US.  Other countries have had to accept the problems but have had no ability to identify or mitigate the issues. This issue of being a passenger in someone else’s technology “vehicle” will only get worse in an age of hyper-scale GenAI.

The rise of distributed, sovereign AI

Fortunately, a new alternative is emerging. Advances in model compression now allow smaller, domain-specific models to run securely on local devices. These systems can operate offline, respect privacy boundaries, and collaborate through encrypted orchestration layers – like the UK’s orchestration challenger; Great Wave AI – which are the digital equivalent of an AI operating system.

This is an important historical parallel: control of the operating system has been the foundation of US technology dominance for four decades. Owning the orchestration layer, rather than the models themselves, could be Britain’s strategic advantage.

Practical examples of sovereign AI in action

We are already seeing promising pilots in policing where compact language models could be deployed directly into patrol cars, giving officers secure, instant AI assistance even without an internet connection.

These local models can handle sensitive data within police networks, providing decision support without exposing information to external servers. That’s what sovereign AI looks like in practice: distributed, resilient, and locally governed.

Britain’s opportunity: Lead in standards and orchestration

Britain’s opportunity lies not in building the next trillion-parameter model, but in building the system that connects them – the orchestration layer that determines how intelligence flows securely across our digital infrastructure.

This approach does more than avoid the hyperscale arms race; it positions the UK at the forefront of the next computing revolution, where value comes from interoperability, governance, and control.

By leading in standards, transparency, and secure federation, the UK can set the global benchmark for trustworthy, distributed AI ensuring that our future systems are not only powerful but sovereign and secure by design.

Read More AI for good

The post Britain’s future lies in sovereign, distributed AI — Not in building bigger models appeared first on Digital Leaders.

We’re treating AI procurement as a binary choice between build it ourselves or buy it from a vendor. But there’s a third option hiding in plain sight, one that could determine whether Britain becomes an AI powerhouse or just another customer. 

The problem we’re not talking about 

Recent research shows that UK organisations plan to increase AI investment by 32% by 2026. Yet nearly 90% report they’re not delivering customer value from their AI efforts. Meanwhile, 62% cite an urgent AI skills gap, with agentic AI capabilities most in demand. 

Think about what that means. We’re investing heavily in technology we can’t fully control, with skills we don’t have, to solve problems we’re still defining. 

The symptom that worries me most is that 83% of UK organisations report “shadow AI”. That’s British employees bypassing official channels to use tools that actually work. When your official path is too slow or too locked down, people find another way. They always do. 

But here’s the question nobody’s asking: Why isn’t our official AI delivering what the shadow version offers? 

Why “Buy” isn’t working 

I understand the appeal of proprietary AI platforms. One vendor, one throat to choke, predictable support. It’s an IT tale as old as time. Simple. 

Until it isn’t. 

The pattern is familiar, where initial promises sound great, then walls appear. Features move to higher tiers. Integrations get deprecated. Prices change, sometimes by 1000% after an acquisition. Your data lives somewhere you can’t inspect it, processed by algorithms you can’t examine, behind terms that shift whenever convenient for someone else. 

You thought you bought a solution. You actually bought a dependency beyond your control.

For government, this isn’t just expensive, it’s strategically dangerous. When operational control and autonomy are top cloud sovereignty priorities (cited by 72% of UK respondents in the same research) it’s important to recognise that you can’t build digital sovereignty on rented foundations. 

Why “Build” doesn’t scale 

Building everything in-house sounds empowering, but it’s a trap of a different kind. 

Every department reinventing authentication. Every agency building its own document processing. Every council creating bespoke chatbots from scratch. 

This isn’t innovation. It’s sprawl. It’s waste. And most of all, it’s bad for taxpayers. 

British tech talent, which we are told we don’t have enough of, gets consumed rebuilding commodity capabilities that already exist. Meanwhile, the truly differentiated work that could set UK government apart never happens because our best people are stuck reinventing wheels. 

The third way: Commoditise the common, differentiate where it matters 

Government should be asking “What are the common, underlying foundations that everyone needs, and how do we make those a shared utility?” 

Authentication. Document processing. Natural language understanding. Computer vision. These are becoming commodity capabilities. They need to work reliably, but they’re not where government creates unique value. 

The differentiated value, the work that truly serves citizens, comes from applying these foundations to uniquely governmental challenges. Better service delivery. Faster benefit processing. More responsive emergency services. 

Enterprise open source provides the bridge. 

This isn’t the “open source” that people interpret as “download it and figure it out for yourself.” Enterprise open source means professionally supported, fully integrated platforms built on open foundations. You get the stability and support of a commercial relationship, and you retain flexibility and choice. 

The code is inspectable. The community improves it globally. Security vulnerabilities get caught because thousands of eyes are watching, not just one vendor’s security team. And if you ever need to, you can take it in-house, fork it, or switch providers.

That optionality rebalances the equation. Vendors must earn your business annually, not just lock you in and extract value. 

What this looks like in practice 

When researchers optimised the Linux kernel and cut data centre energy use by 30%, every Linux user benefited. Not just premium customers. Everyone. That’s how innovation compounds. 

Enterprise open source foundations already underpin critical national infrastructure around the world, including the UK’s Ministry of Defence and National Energy System Operator. Linux. Kubernetes. The tools powering the internet. They’re proven, working, and reliable, but they are not getting the strategic attention they deserve in AI discussions. 

France and Germany built La Suite, an open source alternative to Microsoft 365, not due to vendor hostility, but from understanding that digital sovereignty requires controllable foundations. 

China is investing heavily in open source AI, and many would say that it’s not purely due to community spirit, but because whoever controls the foundations controls the future. 

What Government should do now 

Make enterprise open source foundations the default for government AI. Not because it’s cheaper (though it often is), but because it’s smarter. When something breaks, you can inspect and fix it. When you need to pivot, you can. When you need someone to call for help at 3am, experts are waiting at the end of the line. 

Invest in talent differently. Train civil servants on technologies they’ll own, not just operate. Skills built on open foundations transfer everywhere. Skills built on proprietary platforms trap people and waste the talent pool we have. 

Contribute to global communities. Join and shape the open source projects that matter. Help set tomorrow’s standards before they’re set for us. 

Think in layers. Commoditise the common infrastructure. Let hundreds of departments collaborate in a consistent way and stop rebuilding authentication or cloud platforms. Liberate them to focus on the differentiated services that only government can provide. 

The real question

Of the UK respondents surveyed, 83% believe the UK is or could become a global AI powerhouse within three years. That’s a smaller proportion than in Spain (99%), Sweden, Germany, and the Netherlands (all 98%). 

Our lower confidence isn’t just British cynicism. It reflects a real constraint. We’re building on someone else’s foundations, with someone else’s rules, at someone else’s prices. 

The AI revolution isn’t just about models and compute. It’s about who gets to use it, how it works, and who decides what happens next. 

The question isn’t whether to build or buy. It’s whether to depend on proprietary foundations that can shift beneath us, or invest in open ones we can control, improve, and build upon. Especially when this means enterprise support is readily available. 

The UK doesn’t need to reinvent AI. We need to commoditise the common parts and unleash our talent on the problems that matter. 

That’s not a technology decision. It’s a sovereignty decision. 

And, the window to make it is still open – for now.

Read More AI for good

The post Beyond build vs buy: Why UK Government needs open foundations for AI appeared first on Digital Leaders.

This isn’t simply about replacing physical cards with digital ones; it’s about creating the foundational layer upon which a more intelligent, responsive system of governance and commerce can be built. This infrastructure is crucial for realizing the immense potential of AI to transform healthcare, education, and business operations. Crucially, the success of this strategic pivot depends entirely on getting the infrastructure right from the beginning.

The potential for AI to transform how we deliver healthcare, education, social services, and business operations is immense.

However, the success of such initiatives depends heavily on getting the infrastructure right from the beginning. To ensure our national digital ambition succeeds, we must leverage international evidence, examining both successes and critical shortcomings.

What can we learn from these efforts, and how should they impact the UK’s AI strategy?

Download the Digital Leaders White Paper

Read More AI for good

The post Digital Identity – Lessons for the UK’s AI strategy appeared first on Digital Leaders.

We’re living through an “AI revolution”. And there’s no doubt that it is having an impact across every aspect of our lives. But as we get to grips with the implications of this disruption, I wonder if we’re looking at it through the right lens.

The post Why you might be focused on the wrong AI appeared first on Digital Leaders.

Data-informed decision making

With CPOs working towards profitability, and consolidation likely on the horizon, today’s decisions will shape long-term competitiveness. Against this backdrop, utilisation data provides a dynamic picture of charge point usage across locations and customer segments. For CPOs, this data reveals more than just frequency or duration; it illuminates customer behaviour patterns. 

In a recent conversation with Fastned, the European ultra-rapid charging network, we heard about two sites with identical energy throughput but very different stories. A London site with an improbable sounding 60% utilisation reflected steady, round-the-clock demand from urban taxi fleets. In contrast, a Dutch site reporting a more moderate 25% utilisation operated with twice as many chargers and served users at a consistent but lower intensity through daytime hours. A site with eight chargers reporting 25% utilisation might signal  that two of those chargers are in almost constant use, or that all are full during peak hours, leading to queues and customer drop-off, requiring radically different operational responses. Without such insights, operators risk misallocating resources, either by overbuilding in low-demand areas or missing critical opportunities in high-demand zones.

Building strategic resilience through robust data 

With millions of charges taking place each month, the industry must ensure its data is accurate, complete, and consistent. Robust data collection hinges on comprehensive aggregation across multiple CPOs and data providers, ensuring a holistic and unbiased view. Transparency and collaboration within the industry help everyone progress; sharing metrics while respecting commercial sensitivities can seem threatening in fledgling markets but is the norm elsewhere. Data granularity is equally essential; going beyond simple percentages to analyse session-level details such as simultaneous usage, state of charge on arrival and departure, and charging speed distribution. 

At Zapmap Insights we’re advancing live data aggregation by collating, cleansing, standardising and segmenting these data feeds from diverse operators on our platform. Constant feedback loops and input from end-users ensure continuous quality improvement cycles, resulting in current and historical performance insights, delivered in standardised formats with flexible delivery, creating the foundation for more robust, real-time intelligence.

The right chargers in the right locations

Location types such as urban hubs, motorway service areas, retail locations, and residential streets serve distinct use cases with varied utilisation profiles. Slow, on-street charge points may be in use for longer but see a low number of sessions, while motorway sites see more transient, time-limited use. Understanding these patterns ensures network growth is not only technically sound but commercially viable and user-led.

Combining utilisation data with additional sources such as demographics, housing stock, traffic flows, and grid capacity maps yields even richer planning insights. These datasets help identify true demand centres, avoiding overbuild in underused areas while recognising the latent potential of emerging corridors. This approach also supports collaboration with Local Authorities, crucial to effective infrastructure planning and equitable access by balancing ultra-rapid hubs with on-street and destination chargers. 

From performance metric to strategic asset

When combined with predictive analytics, utilisation data becomes a powerful, forward-looking tool. Grid capacity and constraints data enable CPOs to forecast site expansion feasibility, while traffic flow data helps predict demand. Customer behaviour analytics inform design improvements and targeted service offerings. Utilisation combined with pricing data enables intelligent demand shaping and dynamic pricing strategies. Integrating real-time data from sources like charger telemetry and customer feedback improves the accuracy of demand forecasts. This data-driven approach supports dynamic strategies such as load balancing and pricing incentives, which reduce grid stress, guide investment, and protect profitability.

Conclusion: Investing in Intelligence

The EV charging industry already generates vast amounts of data. Those who leverage it fully to optimise their networks in a smart and capital-efficient way will shape the future of EV infrastructure. In a market where capital is finite and expectations are rising, investing in data is a critical investment in resilience.

Read More Data & Decision Making

The post Why winning Charge Point Operators are putting utilisation data at the core of their strategy appeared first on Digital Leaders.

In a time of rising inflation, growing consumer debt, and increasing financial vulnerability, the UK financial sector is undergoing a quiet but profound transformation.

We worked alongside FinTech Scotland and the University of Glasgow to create a whitepaper called ‘Consumers at the heart of innovation: Financial health evaluation in the UK’ which explores how emerging technologies, regulatory frameworks, and new models of consumer engagement are redefining financial health evaluation.

The post Transforming financial health by bringing consumers to the heart of innovation appeared first on Digital Leaders.

The AI Opportunities Action Plan mentioned the need to “push hard on cross-economy AI adoption” and urged the public sector to “rapidly pilot and scale AI products and services.” It’s an understandable demand — AI is seen as a key driver for economic growth and improved public services. 

But there’s a problem. 

Do organisations have the strong digital foundations and high-quality data needed for AI to learn from? And how do legacy technology and manual processes undermine these necessary foundations. 

A report earlier in the year by the Public Accounts Committee warned that out–of–date legacy technology and the poor quality of data and data sharing in the public sector puts AI adoption in the public sector at risk. 

“AI relies on high quality data to learn, but too often government data is of poor quality and locked away in out–of–date legacy IT systems,” it said. AI has the potential to radically change public services but these barriers make it an uphill struggle, it added. 

The report warned there are no quick fixes and calls for remediation funding. 

AI can’t thrive on outdated and disconnected systems — it needs high-quality data on which to learn. 

Legacy systems and under-digitisation: the government’s AI readiness gap 

We know how legacy IT systems and manual processes affect organisations. As we mentioned in a recent blog, they’re costly, inefficient, unreliable, difficult to change, and pose substantial security risks. 

When it comes to data, government departments deal with: 

• Manual data entry and paper forms that lead to duplication and inconsistent records
• Siloed systems and fragmented processes that keep data out of reach and unsharable 
• Poor quality and unstructured data that undermines the ability to analyse and draw meaningful insights for decision-making. 

These aren’t just technical frustrations that hurt service delivery. These data-related issues actively limit what you can do with AI. 

AI needs: 

• Digital processes that can be automated 
• Connected systems that can share information 
• Clean, structured, accessible data that AI can learn from 

For example, for Border Force, we standardised their workflow and eliminated paper-based processes, which enabled data sharing and paved the way for AI integration. Once the foundations were in place, we helped the team explore how AI could further improve operations. 

Until government organisations digitise their services and find an appropriate way to deal with their legacy systems, there’s a danger AI projects will under-deliver, or fail altogether. 

AI is a powerful tool — but only when the foundations are fixed. 

Don’t miss this Digital Leaders AI week webinar: Join Chad and senior experts from the Home Office, the Cabinet Office, and the Department for Business and Trade, as they share insights of what it takes to implement AI effectively and responsibly in government. Register now. 

Originally posted here.

The post AI can’t fix a broken foundation – here’s how tackling government legacy unlocks it appeared first on Digital Leaders.

The post NHS continuing Healthcare patient level data set appeared first on Digital Leaders.