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.

As data scientists, our role is to develop models that are accurate, efficient, and fair. However, when the AI systems are deployed into production environments, the importance of explainability increases, especially when a system’s decision-making can impact people’s lives. Helping our clients trust and understand the AI’s decision-making process is critical, particularly in highly regulated industries where ethical considerations are mission-critical. Equally, business leaders, product managers, and policymakers all need to be able to explain and justify the outcomes of AI models to customers, regulators, and the public, ensuring that AI systems are developed and used ethically and responsibly.

A lack of transparency can lead to reduced trust, legal complications, and unintended biases, negatively impacting both individuals and organisations.

In this piece, we explore how we use Explainable AI (XAI) techniques at Informed Solutions and how they can be leveraged to encourage a culture of transparency.

Why is explainable AI (XAI) important?

High Value, Trust Based Innovation

Users need to be able to trust the recommendations and decisions produced by AI systems to use them effectively, and if a user is unable to understand how an AI arrived at its conclusion, they are less likely to rely on it.

This lack of trust can have serious consequences. In healthcare, for instance, doctors may be hesitant to act on an AI’s diagnosis if they don’t understand the reasoning behind it, potentially leading to missed or incorrect treatments. Moreover, when users don’t trust AI systems, they are less likely to adopt them, which limits AI’s broader impact across industries. This can hinder innovation, prevent businesses from realising operational efficiencies, and slow down the transformation of industries that AI has the potential to revolutionise. Without XAI, therefore, the potential value of AI tools could be compromised.

Regulatory Compliance

Regulations such as GDPR mandate that AI-driven decisions be interpretable, especially in high-risk domains. Companies that fail to ensure AI explainability may face legal consequences, financial penalties and reputational damage, so it is essential that data scientists consistently uphold XAI as a standard when working on new systems.

Identifying Bias

The data used to train an AI model may contain biases, and if the decision-making process isn’t transparent, it becomes challenging to detect and correct discriminatory patterns. As such, Explainable AI helps organisations audit their models, identify and adjust biases, ensure fair outcomes and reduce the risk of harm from decision-making.

Improving Performance

AI models can sometimes generate unexpected outputs, and when they lack transparency, diagnosing and fixing these errors becomes difficult. Explainability, therefore, allows developers and data scientists to trace errors, refine model performance, and continuously improve system accuracy.

Five strategies to improve explainability

1. Choose Interpretable Models

Some AI models are inherently more interpretable than others due to their simple rules and relationships. Decision trees, for instance, are models that make decisions by splitting data into different branches based on certain rules. It’s like a flowchart where each node (or “question”) divides the data into smaller, more specific groups based on the values of the features (like age, income, etc). These decisions continue until the tree reaches a “leaf”, where a final decision or prediction is made.

At Informed, we follow a philosophy akin to Occam’s Razor—choosing the simplest, most interpretable model that still meets the required level of accuracy. For instance, when tackling a straightforward binary classification problem, we would favour a classical machine learning technique, such as logistic regression, over a more complex deep neural network, provided it delivers sufficient predictive performance.

2. Implement Explainability Techniques

For complex models, XAI techniques such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-agnostic Explanations) can provide insights into AI predictions by showing the ‘why’ behind the prediction or recommendation.

SHAP values, for instance, help explain how individual features influence a model’s prediction. In simple terms, SHAP assigns a “weight” to each feature, showing you exactly how much each feature (like “age” or “income”) influenced the final decision. This method is based on a concept from game theory, where each player’s contribution is calculated in a fair way.

LIME, on the other hand, works by creating a simple, interpretable model that mimics the behaviour of a more complex model for a specific instance or prediction. For example, if you’re trying to figure out why an AI recommended a specific product to you, LIME can create a simplified version of the AI for that one recommendation and show you which parts of your preferences (such as past purchases) were most influential in the decision.

At Informed, some of the problems we tackle involve complex relationships that necessitate the use of “black box” deep learning models. In these cases, interpretability techniques like SHAP and LIME enable us to look beneath the surface and better understand the factors driving specific predictions, helping us maintain transparency even with more advanced models.

3. Establish Transparent AI Governance

Organisations should establish AI governance frameworks that define clear guidelines for transparency. For instance, they should include guidance on documenting model development processes, as well as maintaining audit trails and ensuring explainability standards are met across AI implementations.

At Informed, our AI Charter commits us to putting ethics, safety, responsibility, and security at the core of everything we do. This means designing AI solutions that are safe, transparent, robust, and fair from the outset. Explainability plays a central role in upholding these values across our organisation. To support this, our “Well-Assured Framework” provides a comprehensive checklist that guides our team through key considerations, ensuring every solution we deliver is trustworthy and aligned with our principles.

4. Provide User-Friendly Explanations

AI explanations should be tailored to their audience. For example, a data scientist may need in-depth mathematical insights, whereas an end-user would require simpler, more intuitive explanations. Creating role-specific transparency, therefore, ensures that all stakeholders can meaningfully interact with AI systems.

Because we work closely with our clients, it’s essential that we communicate our models in a way that’s accessible and easy to understand. By prioritising explainability from the outset of a project, we’re able to offer clear, concise overviews of how models are developed, ensuring our clients remain informed and confident in the solutions we deliver.

5. Conduct Regular Audits

By setting up proactive alerts and tracking key indicators, such as shifts in data distributions, unexpected model behaviour, or signs of bias, we can respond promptly and appropriately. This not only helps maintain the integrity and performance of our models over time but also reinforces our commitment to delivering responsible and trustworthy AI solutions that adapt as real-world conditions change.

Conclusion

As AI usage becomes ubiquitous and continues to shape critical decision-making processes, ensuring explainability and transparency is a necessity. Organisations that prioritise XAI principles will not only comply with regulatory requirements but also build trust, mitigate risks, and enhance AI performance. Therefore, by adopting these practices, we can continue to ensure that AI systems are transparent, reliable, and more widely trusted and adopted.

Read More Bias &Ethics

The post The importance of implementing explainable AI appeared first on Digital Leaders.

I’ve walked the halls of Whitehall both as a civil servant and now as a supplier. And I can tell you this: the game’s changed. The speed’s cranked up. The shape of the blocks is different. And new power-ups have emerged that fundamentally alter the rules. 

The 2025 game-changers: New pieces, new physics

The core challenge remains: you have a board already crowded with legacy blocks and a stream of new digital demands falling from above that you must expertly slot in — and mixed in with these are rogue shadow IT pieces. 

But three revolutionary changes have altered the physics of the game. 1. The AI block: Your biggest opportunity and threat 

The single biggest change in recent years is the explosion of Generative AI. This isn’t just another falling block; it’s a polymorphic, unpredictable piece that can change shape mid-air. 

• As an opportunity, it’s the ultimate ‘line-clearing’ tool. AI can analyse and refactor legacy code, translate ancient programming languages, and automate testing, dramatically accelerating your modernisation efforts. 
• As a threat, it’s the most dangerous form of shadow IT you’ve ever faced. Staff using unsanctioned AI tools to write code, analyse sensitive data, or communicate with citizens can introduce profound security holes, data privacy nightmares, and algorithmic bias that your department will be held accountable for. 

2. The Platform power-up: Reshaping the board Itself 

The most forward-thinking departments in 2025 are no longer just playing blocks; they are strategically re-engineering the game itself. This is the rise of mature platform engineering. It’s about building a stable, automated foundation—a set of “Paved Roads”—that makes it incredibly easy for your teams to do the right thing. This platform provides the common components (identity, payments, cloud hosting, security monitoring) so service teams can build and deploy new, compliant digital services at a speed unimaginable just a few years ago. 

3. The economic “gravity”: No time for mistakes 

The post-COVID economic climate and relentless pressure on public finances have increased the “gravity” in our game. Blocks are falling faster. The political and public tolerance for multi-year, multi-billion-pound IT transformation projects that fail to deliver

immediate value is zero. You’re expected to clear lines—to show tangible results—far more frequently. This fiscal reality fundamentally challenges the wisdom of slow, sequential replacement strategies. 

How to play in 2025: A strategic approach beyond overhaul

A complete overhaul isn’t always feasible. But that doesn’t mean you’re stuck. The new rules of the game are about strategic, surgical interventions. 

From ‘tolerate’ to ‘actively contain’ 

In the past, you could “tolerate” a stable, non-critical legacy system. In 2025, that’s dangerously complacent. An unpatched but previously isolated system can become your biggest vulnerability when a staff member inadvertently connects it to an unsanctioned AI tool. 

The new rule: No system is an island. “Tolerable” systems must be actively contained. It means putting them in a secure enclosure, strictly controlling all data flowing in and out, and continuously monitoring them for anomalous activity. You must have a clear, costed, and regularly reviewed plan for their eventual decommissioning, even if it’s years away. 

• Example in practice: Following a near-miss where a legacy HR system was almost exposed via a shadow AI plug-in, a major department initiated an “Active Containment” programme. They used modern tools to create a security wrapper around the old system, logging every single access request and using AI to spot unusual patterns, effectively buying them time to plan a safe replacement while neutralising the immediate threat. 

From ‘optimise’ to ‘augment & automate’

“Optimise” used to mean a “lift and shift” to the cloud. It was a good first step but it’s no longer enough. The 2025 approach is to use new tools to fundamentally enhance your existing assets. 

The new rule: Use AI and automation to augment your systems and teams. This means using AI-powered tools to automatically refactor and modernise codebases, cutting down multi-year projects to months. It means wrapping legacy systems not just with basic APIs, but with intelligent APIs that can clean, validate, and enrich data on the fly. 

• Example in practice: The Department for Work and Pensions (DWP) has been a leader in tackling its vast legacy estate. Imagine them launching a pilot using AI-powered tools to translate millions of lines of critical COBOL code into a modern language like Java. This doesn’t just “optimise” the system; it transforms it from an untouchable black box into a modern, manageable asset, saving years of manual effort and significantly reducing risk. 

From ‘pace’ to ‘platform-led evolution’

The old idea of sequentially replacing systems at a certain “pace” is too slow for the demands of 2025. The modern game is played in parallel.

The new rule: Invest in your platform to enable continuous, simultaneous evolution. By providing a secure, automated platform, you empower multiple service teams to modernise their own applications at the same time. This is the vision outlined in the government’s DDaT Playbook and is being realised by departments like His Majesty’s Revenue and Customs (HMRC). In their tech blogs, HMRC engineers describe their move towards an “Internal Developer Platform” which streamlines the process of building and deploying new digital tax services, allowing them to focus on user value instead of wrestling with infrastructure. This platform approach is a direct descendant of pioneering GDS services like the GOV.UK Platform as a Service (PaaS). 

From ‘systematically replace’ to ‘strategically decompose’

The monolithic “big bang” replacement is dead. The financial and operational risk is too great in the current climate. 

The new rule: Don’t replace the whole thing; strategically decompose it. You treat the legacy monolith like a patient in surgery. You identify the most critical functions locked inside (e.g., a specific calculation, a case management decision) and carefully extract them, rebuilding them as small, independent services on your modern platform. You slowly and safely “strangle” the old system piece by piece, showing concrete value and reducing risk at every single step. 

• Example in practice: The Home Office, faced with its sprawling legacy immigration casework systems, has shifted to this model. Instead of a single “Future Border” programme to replace everything, they are delivering value slice by slice. They launched a new, standalone digital service for student visa applications. It works beautifully for the user and plugs into the old monolith on the back end to access necessary data. Over time, more and more of these slices will be carved off until the old system has nothing left to do. 

The grand strategist of the great legacy escape

The game has changed. The speed is faster, the pieces are more complex, and the stakes are higher. But you also have more powerful tools at your disposal than ever before. 

Your role as a leader is not to play every move. It is to understand the new rules of the game. It is to champion the shift from a passive mindset of the past to an active, dynamic fit for 2025. It means harnessing AI, investing in your platform, and empowering your teams to dismantle your legacy piece by valuable piece. 

The challenge is immense, but the opportunity to build truly responsive, resilient, and effective public services has never been greater. 

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

Read More Regulation

The post The great legacy escape is a game of Tetris… but not as you know it. appeared first on Digital Leaders.

In 2025, that phrase feels woefully irresponsible – particularly as we see digital technologies advance at unprecedented speed, often causing real-world harm for citizens and the planet.

It is often claimed that Artificial Intelligence (AI) will be the biggest disruptor the world has seen since the Industrial Revolution. A multi-faceted technological colossus set to reshape every corner of society and business, yet still largely a mystery to most.

Experimental sketch of the ecosystem – showing complexity.

Investment into the AI industrial complex is staggering. There is a global race for AI superpower status, with countries such as Saudi Arabia, the UK and France squaring up to the US and China, to be a major force. And guess what, people are moving fast and breaking things; massive data centres are being built which consume enormous amounts of energy, algorithms are being trained on biased data, and the threat of an employment crisis is very real – with low-wage workers and women set to be disadvantaged the most.

Globally, governments are actively investing in AI to streamline public services, improve efficiency and to better serve citizens. However, AI readiness and the ability, or inability, of countries to adopt and regulate these technologies will also shape international competitiveness, equity between citizens and countries, ethical standards and future governance.

While its past, current and potential impact is alarming, the future of AI is not predetermined. It takes many human decisions to set the course and decide how things evolve and who sets to benefit; but ethical governance is needed to develop ethical policies and procedures.

In the UK, the Government published its AI Opportunities Action Plan in January, outlining plans to maximise AI’s potential to drive growth and benefit people across the UK – including advocating for its use in the public sector. The plan notes the potential for AI to radically change public services by automating routine tasks, making services quicker and more efficient, and making better use of Government data to target support at those that need it.

Both National and Local Government bodies are being encouraged to adopt AI within their services and processes. Photo: Bridgend District Council.

“But it also brings with it risks that must be managed to effectively support adoption and maintain public trust,” the plan acknowledges, citing fairness, safety and privacy. Indeed, it was a major point of discussion at the Digital Leaders Public Sector Insights AI Week in March, where I gave a presentation on digital innovation in uncertain, complex and emergency environments.

With so much at stake, it has arguably never been more important not to move fast and break things. But we can still move quickly – carefully. With that in mind, this article will reflect on the debate, while drawing on guidance, toolkits and principles to highlight the importance of care-ful innovation and good governance.

Arm yourself with knowledge

The need to arm ourselves with knowledge was a key takeaway from the Digital Leaders event, and one that underpinned my presentation.

The ever-changing narrative around AI and its sociotechnical impacts can lead to feelings of overwhelm; many of us feel daunted by the intensification of automation across all sectors, organisations and individual practices – whether you are working in it or observing from afar.

AI chatbots and automation tools are increasingly being pushed on citizens within digital workspaces and personal communication – Microsoft, Google Gemini, WhatsApp – often without explicit consent. It has become our individual responsibility to improve our knowledge and understanding of these tools and technologies; to think critically, challenge hype and, fundamentally, not accept a tech-determinist roadmap from tech companies at the helm of AI – many of whom, arguably, will be thinking about the financial bottom line and power that comes with it.

AI adoption in government

Thankfully, many responsible leaders are advocating for tools to better support the public procurement of AI. One such person is Professor Alan Brown of Exeter University, who is also AI director at Digital Leaders and held a fellowship at the Alan Turing Institute.

Discussing how to find the balance in digital power dynamics, Brown says we must first acknowledge the political dimensions of AI-based technologies and not pretend they are neutral tools: “When evaluating new AI technologies, my personal approach is to focus not just on defining ROI and efficiency goals, but also describing their governance implications: Who gains authority and who loses it? What values are encoded in this system? What dependencies are we creating? Whose interests are prioritized by default?”

These are all critical questions that need answering truthfully and thoughtfully.

Albert Sanchez-Graells, Professor of Economic Law and Co-Director of the Centre for Global Law and Innovation (University of Bristol Law School), meanwhile, recognises another problem: there is a gap in public sector digital skills preventing the public buyer from adequately understanding the technologies it seeks to buy. Therefore, “the public buyer risks procuring AI it does not understand, which is already a widespread phenomenon in the private sector.”

It doesn’t help that there is so much noise, marketing hype and complexity surrounding it all. On my own quest for clarity, I have come across some useful resources, which I have rounded up below to help others feel less overwhelmed and more informed.

Navigating AI with confidence

Representing a significant stepchange in responsible AI adoption, the UK Government published a playbook for AI in February. The playbook – designed specifically to offer public sector organisations to use AI safely, effectively and securely – includes 10 principles that should be upheld when using AI. These range from understanding the capabilities and limitations of AI, to working with commercial colleagues from the outset, to simply having the skills and expertise needed to implement AI solutions.

Again, Professor Alan Brown has published an in-depth analysis of the playbook, which I would highly recommend reading. According to Brown, while the principles reveal a thoughtful approach to balancing innovation with responsibility, there are additional considerations in the context of digital transformation at large, complex organisations – for instance, integration with legacy systems and cross-department coordination.

Signalling a national commitment to responsible AI use, similar guidance has been produced in Wales and Scotland. The Welsh version of the guidance includes a range of examples of successful uses of AI in the public sector – from helping to diagnose cancer to creating a ‘lost woodland’ dataset – while the Scottish AI Playbook includes a case study about how AI-automated image cropping helped estimate heat loss in homes.

These examples demonstrate the wide-ranging impact and potential of AI, and the importance of ensuring the public sector is equipped with the skills, knowledge and confidence to use it in a way that mitigates risk and maximises positive impact for people and planet.

Cyber Security and specialised toolkits

A major part of the AI education piece is around data and privacy, with cyber security risks intensifying as businesses embed AI into various operations.

According to the National Cyber Security Centre, some of the most dangerous flaws in AI systems are ‘AI hallucination’ (producing incorrect statements), being biased or gullible, creating toxic content and ‘data poisoning’ (susceptibility to being corrupted by manipulating data it is trained on).

These are serious issues that have serious consequences, so it is paramount that tools are developed ethically and security integrated into AI projects and workflows from the outset.

Another significant milestone this year came with the launch of the UK’s Code of Practice for Cyber Security of AI’, which addresses cyber security risks to AI. The Information Commissioner’s Office has also designed an AI toolkit for those seeking to better understand best practice in data protection-compliant AI – to “reduce risk to individuals’ rights and freedoms” caused by their own AI systems.

There are plenty of other specialised toolkits elsewhere, detailing how to harness opportunities in areas such as digital accessibility and civic AI. This toolkit, for example, gives advice on how civil society organisations and local authorities can empower communities to address the climate crisis. These are matters that have a critical real-world impact and which require innovation with care.

Final thoughts

There is no fixed position on AI; it is shifting sands so it is crucial to ask questions and get informed – particularly when employing it in the public sector, where the risk of causing harm is significant.

It is a challenge, but with the right collaboration, partnerships and care, it is possible to deliver digital innovation with demonstrable positive impact. In practice, it requires policymakers to implement robust data privacy and transparency regulations, ensure that labour laws protect fair treatment of workers, and embed diversity and inclusion into government funding frameworks.

It’s a challenge that Calvium is well-prepared to address, as proud members of the Digital Leaders AI Expert platform. As proponents of tech as a force for good, we combine data ethics with ISO and Cyber Essentials Plus accreditation, and clearance for government contracts. This means we help clients adopt AI responsibly, considering impacts at every stage and ensuring innovation is both safe and valuable.

Read More Bias &Ethics

The post Care-ful innovation, the public sector and AI appeared first on Digital Leaders.

Modern technologies can streamline regulatory processes, enhance decision-making, and scale oversight capabilities to address the growing complexity of globally connected economies. Regulators can harness Supervisory Technology (SupTech) to combine innovation with resilience, efficiency with integrity, and modernization with public trust. By utilizing cloud services, supervisors can rapidly deploy new supervisory tools, adapt to emerging financial risks, and efficiently process vast amounts of regulatory data while maintaining the highest standards of security and operational resilience. Cloud technology provides the agility and cost-effectiveness needed to keep pace with the rapidly evolving financial sector.

To achieve enhanced resilience and operational excellence in cloud-based supervisory systems, supervisory authorities can implement the shared responsibility model with their cloud service provider (CSP). This delineates security and operational accountabilities between both parties.

A white paper produced by Alliance for Innovative Regulation (AIR), and supported by Amazon Web Services (AWS), offers guidance that supervisory authorities can use to implement SupTech. Beyond Pilots and Sandboxes: Regulatory Innovation through SupTech Case Studies and Leading Practices, recommends five steps for success:

1. Start with a clear vision
2. Develop robust data governance frameworks and analytics capabilities supported by investments in modern cloud-based data platforms and talent
3. Collaborate internally and externally on innovation
4. Work iteratively and leverage agile methodologies
5. Balance innovation with security and resilience

The paper sets out ten core capabilities required for regulators to effectively supervise, monitor, and manage risks in the financial services sector and identifies those that are further ahead and those that could most benefit from knowledge-sharing and collaboration. The core capabilities range from licensing and case management to investigation and enforcement of regulatory violations.

For example, for licensing and case management, regulators can leverage optical character recognition (OCR), natural language processing (NLP), and generative AI capabilities such as large language models (LLMs) to transform how they review and analyze documents such as business plans, financial statements, and compliance records. The AIR report cites the example of The Australian Securities and Investments Commission (ASIC), Australian Prudential Regulation Authority (APRA), and the Reserve Bank of Australia (RBA) which recently worked alongside AWS to build a generative AI proof of concept (PoC) solution to compare, query, and summarize documents. Prioritizing responsible AI principles, the PoC achieved promising results, including as much as 93 percent confidence in some model outputs using publicly available documents. The experiment has been shared with dozens of other regulators and provides a glimpse into the future of regulatory practices and financial oversight.

This is an example of regulatory reporting and data collection, which are fundamental processes for financial regulators, enabling them to make informed decisions to safeguard financial stability. The growing complexity of financial instruments, coupled with the globalization of markets, has resulted in an exponential increase in data volume and diversity.

SupTech solutions can streamline data collection and reporting through automation, advanced analytics, and seamless integration, allowing regulators to process vast datasets efficiently, and maintain data quality and compliance.

For example, the Financial Industry Regulatory Authority (FINRA) operates one of the most sophisticated cloud-based systems for regulatory reporting and data management, overseeing the activities of US brokerage firms and exchange markets. Processing more than 100 billion daily market events, FINRA uses real-time surveillance and analytics to identify unusual trading patterns, detect fraudulent activities, and flag market anomalies. By combining real-time monitoring with historical data analysis, FINRA proactively mitigates risks and maintains compliance. The system’s cloud-based architecture provides scalability, allowing it to adapt seamlessly during periods of increased market activity or volatility, enabling uninterrupted data processing and robust surveillance. Encryption, strong protocols, and disaster recovery mechanisms safeguard data integrity. The adoption of open, standardized data formats enhances interoperability and facilitates efficient communication with external systems, streamlining reporting to other bodies.

The report concludes: “The road to a fully modernized regulatory framework is challenging but achievable. Through strategic investments, thoughtful integration of technology, and unwavering commitment to public trust, regulators can position themselves as stewards of a future-ready financial system, capable of navigating the complexities of the digital age.”

Read More Regulation

The post The future of financial regulation: How technology makes finance safer appeared first on Digital Leaders.

The House of Lords consist of a diverse group of experts with a lifetime of experience. Normally that means Peers are well placed to have detailed opinions on the legislation being discussed. With AI, it is different. I have noticed that when it comes to discussing AI in the upper chambe,r the number prepared to rise to their feet and express an opinion is very limited. A small group of regular speakers. 

This small core of experts between them have contributed a great deal to the debates, but it is not an area where the wider house on all sides has participated and debate of these important pieces of legislation has been far more limited and poorer as a result.

The obvious reason for this, we believe, is not a lack of interest, but rather a lack of knowledge with only a few AI Experts sitting in the Lords. Not surprising perhaps given the average age of Peers is 70 and particularly, given AI in the mainstream has only been with us since October 2022.

We clearly have a challenge then in the Upper Chamber. As AI becomes embedded in the fabric of daily life and institutional decision-making, we have a lack of knowledge in a key decision making part of our UK legislation process. It should be important that Peers and their advisor understand both the transformative potential of AI and the challenges it presents.

Therefore, if there is a definite gap in the upper chambers knowledge at a crucial time for AI legislation, how do we fix this?

It is not, I believe, a case of teaching Peers to use ChatGPT, but rather we need to help them understand and be able to think about the fundamentals of AI.  

This is why I applaud that the Digital Policy Alliance, with funding from Digital Leaders and in collaboration with leading industry experts, is starting a regular programme teaching Peers called, “AI Essentials for UK Policymakers”.

It’s a comprehensive programme designed specifically for Peers and their support staff. The series offers an accessible exploration of the AI landscape, providing them with the understanding needed to make informed decisions that will impact the UK’s digital future.

Led by Lord Ranger of Northholt and DPA Research Director Professor Alan Brown, The Digital Policy Alliance is running a carefully structured “in the room” teach-in and awareness programme that explores the critical dimensions of AI adoption: technology, economics, governance, and ethics.

Including speakers from industry, civil society and academia and supported by the Digital Policy Alliance’s network of digital policy specialists, these sessions will hopefully equip those Peers who take part, with the knowledge and insights necessary to actively participate with confidence in the chamber and committees set the task of developing effective legislation that harnesses AI’s benefits while mitigating its risks. 

Each session builds upon previous events, creating a comprehensive understanding of how AI can benefit all UK citizens, residents, and businesses while maintaining the nation’s competitive edge. 

So what are the calls to action here?

If you are a Peer or advisor to a Peer please get in touch with me or the DPA and we can get you on this important journey which begins on 4th September 2025.

Equally, if you are an expert in AI or closely involved in a key area of this legislation, please reach out to me and let’s explore how you can be part of upskilling Peers. 

Do you agree with me? Would you like to get involved? Please drop me a DM on LinkedIn and I can introduce you to the right people at Digitalpolicyalliance.org to have a conversation.

Read More Bias &Ethics

The post Understanding AI: How the House of Lords can engage. appeared first on Digital Leaders.

Like most law-enforcement organizations, Bedfordshire Police, in the UK, faces multiplying crime threats and limited resources to fight them. We need to be effective and efficient in protecting citizens. This means we need to make best use of the public money that funds us, and improve the experiences of the people we serve.

Modern policing needs a modern approach. Today, we’re dealing with more weapons-based violence, gangs, drugs, and cybercrime, and this calls for new ways to protect the public and fight crime. Digital technology has enormous potential to help, as long as we can deploy it swiftly and efficiently.

To modernize Bedfordshire’s approach to policing, we had to change how we approached technology.

Heavy administrative tasks hampered officers’ work, so we needed a way to efficiently return their focus to fighting crime and delivering justice. Everyday frustrations included hours spent redacting personally identifiable information from evidence submitted to the Crown Prosecution Service (CPS) as well as collating and analyzing data to build a case. Then there’s the manpower tied up with guarding crime scenes. All of this is a drain on resources and staff morale.

While we recognized technology’s potential to enhance our work practices, our existing systems needed updating and our internal IT capacity faced some constraints. The traditional process of implementing new solutions—from defining requirements through to deployment and training—often meant that by the time a system was fully operational, business needs had evolved.

We needed a new approach to how technology was delivered.

A digital innovation partnership

We met with AWS to discuss how we could approach things differently. We knew we wanted to host any future solutions in a secure, scalable cloud. This is the accepted model in the public sector now—not just to add more servers, but to deploy innovative new capabilities at speed. Visits to other Forces confirmed this as the way forward.

We chose to work with AWS, because it has infrastructure that’s fully compliant with public-sector specifications (such as local data hosting) and is well-proven in a government and judicial context. It also offered access to an extensive pool of solution partners that would help us address modern policing challenges.

We realized we could learn a lot from a company built on agile and innovative solutions. AWS has a culture of innovation. Its processes aligned with our desire to be more responsive—to take a more “programmatic” or solution-oriented approach to policing, supported by the latest digital capabilities. Tools that are instinctively easy to use would also limit the time needed to train officers.

AWS quickly helped us to identify and deliver high-impact digital transformation solutions securely and at speed.

Working backwards and officer input

To address the need for fast access to new automation tools, AWS proposed an approach it uses internally—which is to look to the future and think big, but to start small. Its “working backwards” workshops helped cement the idea that if an issue isn’t fully understood, even the best solution will fail. We learned to drill down to identify specific pain points and find practical solutions, rather than devise a pilot and expect it to solve everything.

For example, we needed to rethink demand management in our control room, which faced unsustainable levels of demand—much of which didn’t need a human response. Although we’d introduced a webchat option, this actually created more work. By working with AWS, we devised an AI-based chatbot to help to manage non-emergency public contact, by filtering incoming traffic and signpost users to the best source.

Another approach we adopted was inspired by an apprenticeship program run at AWS. We now hold an annual Dragons’ Den/Shark Tank-style event. It encourages staff at all levels to propose ideas for solutions to transform our work and operational efficiency. The best ideas are shortlisted and the winners put into development. It has really captured people’s imagination. They’ve become part of the solution. Our culture has changed and officers now regularly approach us with new ideas.

Far-reaching impact

Working alongside AWS has changed the way Bedfordshire Police approaches innovation and process reinvention. For a long time, policing was about buying. Now, we think in terms of developing solutions. We also ensure all new solutions are intuitive, with no special training needed. That ensures high engagement and adoption, and the result is a much faster impact.

The control room’s AI-based chatbot saves more than 1,500 staff hours per year. Routine queries—concerning animals, fly-tipping, custody, property, civil disputes, to name a few—are automatically routed to the most appropriate source. As 39 percent of chatbot enquiry demand doesn’t need agent input, we can prioritize calls that do require immediate police attention.

The AI-powered digital document-redaction solution is so fast and accurate in redacting personally identifiable information from documents submitted to the Crown Prosecution Service—it takes minutes to complete a job that previously took hours. The AI tool has a human-in-the-loop. Humans set the parameters for the redaction tool, specifying what information it is to detect and redact. Once the redaction is done, humans review the document to ensure compliance. This solution has saved more than 9,000 hours a year, equivalent to almost seven full-time officers. In September 2024, this innovation won the National Police Chiefs’ Council Commissioner’s award at the National AI Awards.

The change of pace in Bedfordshire’s innovation delivery has been huge. The redaction solution was developed in just three months, with enhancements actioned in hours. Seeing that rapid delivery is great for morale. Redaction used to be the biggest source of complaint, but now it is rarely mentioned.

Best of all, officers can do the policing they’re trained for. All this has transformed our ability to protect and serve the public, and supported the delivery of better justice.

Keeping citizens safe

Everything we do is geared to better outcomes for citizens. Another way we use AI tools is to collate intelligence faster, and protect vulnerable people more effectively.

In the UK, provisions under the new Domestic Violence Disclosure Scheme give citizens the right to know if their partner has an abusive past. The ability to gather relevant data from different sources means we can now identify potential victims faster and more proactively, and warn them sooner. A multi-agency safeguarding hub (MASH) built on AWS’s cloud infrastructure supports this. We’re working with local authorities to automate and digitalize information, with the aim to integrate up to 65 data sources.

Sharing what we’ve learned

Something else we’re proud of is that our innovative new solutions are replicable globally. So another mindset we’ve adopted from AWS is to share what we’ve done.

After all, Bedfordshire Police’s challenges are not unique to our county, or to the UK. All police forces face challenges with demand management, document redaction, scene-guarding, and more. Up to now, our redaction solution has been rolled out across 18 other police forces in the UK. The College of Policing estimates it would save around 7.5 million policing hours a year if adopted nationwide—equivalent to more than 4,000 full-time officers.

Our takeaways are that this isn’t just about harnessing the cloud; it’s about cultural change. That means leaders changing how they think and work.

The significant change Bedfordshire has made to the way it polices, and how we harness technology to support that, isn’t about innovation for its own sake. The real beneficiary is the public. We can now confidently keep pace with increasing crime threats, keeping citizens safe while delivering justice.

Our next goals include leveraging AI to perform tasks currently handled manually by officers and staff, particularly in the research and analysis of the ever-growing volumes of both digital and non-digital data. By utilizing AI for these functions, we believe that Bedfordshire Police will become more productive and strengthen decision-making processes. Currently, research and analytical tasks consume 80 percent of an individual’s time, leaving only 20 percent for decision making. We aim to reverse this ratio, allowing 80 percent of the time to be dedicated to making informed decisions.

Read More AI for good

The post Future-ready law enforcement: Bedfordshire Police embraces digital agility to deliver modern policing services appeared first on Digital Leaders.

Sopra Steria, in collaboration with FinTech Scotland and the University of Glasgow, has published a new whitepaper: “Consumers at the heart of innovation: Financial health evaluation in the UK.” This paper explores how technology, regulation and new engagement models are reshaping the financial health landscape – and what this means for industry leaders.

The rising tide of financial vulnerability

UK consumers are grappling with growing financial stress. Non-mortgage household debt now exceeds £17,000 on average and over 12.8 million adults feel overwhelmed or in difficulty, according to the FCA. One in four adults lacks financial resilience and one in ten has no savings at all. 

Traditional financial products and services often fail to meet the needs of these groups. As the whitepaper outlines, there is a pressing need for more inclusive, flexible and accessible solutions.

A shift from creditworthiness to financial health

Traditional credit scoring models rely on retrospective data and often miss early signs of distress. Sopra Steria’s whitepaper calls for a shift toward a more holistic, forward-looking model of financial health – one that incorporates income stability, spending behaviour, savings patterns, life-changing events and regional economic data.

“The future of financial services must move beyond traditional creditworthiness and embrace a more inclusive understanding of financial health,” says co-author Kal Bukovski, Consulting Senior Manager and Director of Academia and Research at Sopra Steria. 

The regulatory response: from compliance to purpose

The FCA’s Consumer Duty marks a major step toward purpose-led regulation, encouraging firms to focus on consumer outcomes rather than compliance alone. Initiatives like Breathing Space and the High-Cost Credit Review reinforce the growing regulatory emphasis on fairness, affordability and inclusion.

Sopra Steria urges firms to go further – embedding financial wellbeing into the core of strategy, product design and data ethics.

Technology as a catalyst

AI and advanced analytics are enabling earlier interventions, helping identify at-risk consumers through spending patterns or increased credit usage. Tools such as robo-advisors are broadening access to personalised financial guidance, particularly for those with limited resources or financial literacy.

The expansion from Open Banking to Open Finance also presents new opportunities to deliver tailored support across banking, insurance, pensions and investments – if trust and data ethics are prioritised. – if trust and data ethics are prioritised.

“Open Finance has the potential to shift power back to the consumer – but that depends on inclusion and responsible innovation,” says Bukovski.

Join the webinar

Explore these insights and more at this upcoming webinar:
“Reimagining financial health – Innovation and regulation in the UK’s financial services”

Thursday, July 31, 2025 

2:00 PM – 3:00 PM
Register today

Panelists:

• Kal Bukovski, Director of Academia and Research, Sopra Steria
• John Finch, Professor of Marketing, the University of Glasgow’s Adam Smith Business School
• Kirsty McKenna, Innovation Programme Manager, FinTech Scotland
• Danail Vasilev, PhD Fellow, Economics Institute, University of Oslo

Read More Data & Decision Making

The post Reimagining financial health: Innovation and regulation in the UK’s financial services appeared first on Digital Leaders.

Key AI Themes: Sovereignty, Scale, and Service Transformation

Looking at the spending commitments, three dominant themes emerge. First is digital sovereignty, with the creation of a £500 million UK Sovereign AI Unit designed to reduce dependence on foreign AI capabilities. This reinforces a fundamental shift from previous approaches that relied heavily on international partnerships and private sector leadership. The £750 million Edinburgh supercomputer investment is an example of this sovereignty agenda, positioning the UK to compete with similar national AI infrastructure projects globally.

Second is the emphasis on scale, particularly evident in the commitment to expand the UK’s AI Research Resource by at least 20-fold. This ambitious target suggests the government recognizes that incremental improvements will not suffice in the current AI landscape. The scale of investment across research and development, reaching £22.6 billion annually by 2029/30, positions the UK among the world’s most aggressive public investors in emerging technologies.

Third is the integration of digital transformation across public services, with £10 billion earmarked for NHS technology modernization and £3.25 billion for the Transformation Fund to drive digital-first approaches across government. This represents a move beyond isolated pilot projects toward systematic digitization of public services and operations.

Gaps and potential risks

With AI and digital transformation a clear focus, attention must now turn to delivery. Despite the substantial financial commitments, my concerns centre on where the spending review stops and the challenges of delivering AI at scale begin. Most troubling is the absence of dedicated funding for AI ethics oversight or algorithmic accountability frameworks. While the AI Security Institute addresses technical safety, is there sufficient attention to the broader societal implications of rapid AI scaling across public services? 

From this perspective, the review’s emphasis on speed and scale may conflict with responsible deployment principles. The commitment to “at least 20-fold expansion” of AI research resources could be seen as a quantity-over-quality approach that could overwhelm existing governance structures. Without corresponding investment in regulatory capacity and ethical oversight, I worry this rapid scaling risks deploying AI systems before adequate safeguards are established. 

So what to do? Four key actions for Digital Leaders

Based on my reading of this review and recent discussions with many organisations, our digital leaders can view this spending review as both an opportunity and a warning. The substantial funding creates unprecedented possibilities for AI-enabled transformation and will undoubtedly drive the pace of AI adoption, but the execution risks cannot be ignored and are equally significant. This suggests 4 key actions.

1. This review reinforces the need for organizations to develop robust AI governance frameworks immediately, rather than waiting for government guidance. The review’s emphasis on rapid deployment suggests to me that regulatory frameworks will struggle to keep pace with implementation. So our digital leaders and their organisations need to act now;
2. Digital leaders members, should prioritise interdisciplinary AI teams that combine technical expertise with business, ethical, legal, and social science perspectives. The review’s skills focus on technical capabilities creates what I see as a market opportunity for broader AI literacy. It’s time to get everyone in your organization moving up the AI maturity ladder;
3. I encourage our digital leaders and their organisations to engage proactively with a wide set of stakeholders, local communities, users, and employees affected by AI deployments. This investment will increase pressure on leaders to act. Yet, the review’s priorities create both reputational risks and opportunities for organizations without encouraging transparent, participatory approaches; and
4. I would suggest that digital leaders prepare for a difficult period of regulatory confusion in the AI landscape where UK-developed capabilities may diverge from international standards. This requires careful consideration to ensure interoperability and data portability in AI system design.

Conclusions

There is no doubt that the Spending Review 2025 represents the UK’s most ambitious commitment to AI leadership, but in my view, its ultimate success will depend on defining an execution strategy that balances innovation with responsible execution. Those digital leaders who navigate this challenge effectively, will help determine whether the UK emerges as a model for responsible AI scaling or a cautionary tale of technological overreach.

Read More AI for good

The post Four AI actions for Digital Leaders from the UK Spending Review appeared first on Digital Leaders.

The two best known AI agents that have been released by major developers are Operator by OpenAI and Manus by a Chinese startup called Butterfly Effect. Those with an OpenAI Pro account (cost: $200/month) can trial Operator, while Manus has recently become available to the general public. Platforms such as ServiceNow and Salesforce are also offering agentic AI solutions. These prototypes are not yet at the ’buy a flight’ stage. Instead, they are targeting use cases such as customer support, scheduling and procurement. 

• It’s crucial for organisations to prepare for Autonomous AI adoption to avoid unmanaged and ungoverned use.
• Organisations need to focus on strategy, compliance, governance, technology, data, and expertise to effectively implement agentic AI.
• Avoiding hype, ensuring robust governance, and focusing on user experience are key. Leaders should start small, learn fast, and plan for maturity with cross-functional oversight and governance models.

Originally posted here

Read More AI for good

The post Agentic AI is coming, but is your business ready? appeared first on Digital Leaders.