Discovery and Alpha have always existed for a reason. They give multidisciplinary teams the space to understand users, test assumptions, explore technical options and build enough confidence to decide what comes next. Good agile delivery has never been about moving quickly for the sake of it. It’s about reducing uncertainty before committing significant time and investment.

What agentic AI is beginning to change is not the purpose of those phases, but the pace at which parts of them can happen.

At Made Tech, we’re seeing opportunities to compress Discovery and Alpha from the 12 to 20 weeks many organisations have become accustomed to, towards eight to 12 weeks. That isn’t because teams are skipping research or cutting corners. It’s because AI can help complete some of the most time-consuming activities much more efficiently, allowing teams to move more quickly from gathering information to testing ideas.

That’s an important distinction because it challenges one of the biggest misconceptions surrounding AI.

Much of the conversation focuses on automation or replacing people. Our experience has been different. The greatest value comes from removing the waiting. Activities such as synthesising research, analysing technical options, generating documentation and producing early prototypes can now happen far more quickly, giving teams more time to engage users, challenge assumptions and iterate solutions.

In other words, AI isn’t replacing agile delivery. It’s compressing it.

Faster delivery doesn’t mean less discovery

There’s a temptation to assume that if Discovery and Alpha become shorter, teams must be doing less work. In reality, the opposite should be true.

The activities that matter most in Discovery have never been writing documents or creating slide decks. They are understanding users, testing assumptions and building confidence that a team is solving the right problem before moving into delivery. Agentic AI can help remove much of the administrative effort that surrounds those activities, allowing teams to spend more time where human insight adds the greatest value.

Rather than investing weeks pulling information together, delivery teams can move more quickly into conversations with users, explore a wider range of technical options and iterate solutions sooner. Shorter delivery phases should create more opportunities for learning, not fewer.

That’s why I see AI as an evolution of agile rather than a replacement for it. The principles remain exactly the same. We are simply reducing the time between learning, deciding and delivering.

The bottleneck is no longer the technology

As delivery accelerates, another challenge begins to emerge.

For many organisations, the limiting factor won’t be whether AI is capable of producing better outputs more quickly. It will be whether leadership, governance and decision-making can keep pace.

If a delivery team can reach an evidence-based recommendation in half the time, but approvals, funding decisions or governance reviews still follow the same timetable, the overall pace of delivery changes very little. The bottleneck simply moves elsewhere.

Leading public sector organisations are beginning to recognise that adopting agentic AI isn’t just about introducing new technology. It also requires organisations to rethink how decisions are made, how multidisciplinary teams are empowered and how governance supports rapid learning rather than becoming another queue in the process.

That’s a much bigger organisational challenge than selecting the right AI tool.

Human judgement becomes even more valuable

The more capable AI becomes, the more valuable human judgement becomes alongside it.

Agentic AI can analyse research, compare options and generate documentation at remarkable speed. What it cannot do is understand organisational context, balance competing priorities or build trust with users and stakeholders. Those remain fundamentally human responsibilities.

The same applies to service design. AI can help teams reach better-informed starting points, but it cannot replace the conversations that uncover unmet needs, challenge assumptions or explain why a service works the way it does.

Our experience modernising legacy systems has reinforced that lesson. Technology rarely tells the whole story. Every organisation develops workarounds, informal processes and operational knowledge that sit outside the system itself. AI can help explain the code, but it still takes people to understand how services actually operate.

That’s why experienced multidisciplinary teams remain at the centre of successful digital delivery. AI makes those teams more effective. It doesn’t make them less necessary.

A different way of thinking about delivery

The organisations that benefit most from agentic AI are unlikely to be those that simply deploy new tools and expect delivery to accelerate automatically.

The real opportunity lies in changing how teams work. If research can be synthesised in hours rather than days, if prototypes can be generated in a single sprint and if technical options can be explored far more quickly, organisations also need to become better at making decisions, testing ideas and acting on evidence.

For smaller, agile delivery partners like Made Tech, that shift plays to our strengths. We’ve always believed that better public services come from empowered multidisciplinary teams, rapid feedback and continuous learning. Agentic AI doesn’t replace those principles. It reinforces them by removing much of the friction that has traditionally slowed delivery down.

The organisations that will benefit most won’t necessarily be those with access to the newest AI tools. They’ll be the ones prepared to evolve their culture, governance and ways of working alongside the technology. That’s how faster delivery becomes better delivery, and how agentic AI becomes more than just another productivity tool. It’s an opportunity to rethink how digital services are designed, delivered and continuously improved. That’s at the heart of what Made Tech is all about.

Learn more about our public safety and defence expertise and how Made Tech can help your organisation.

The post Taking advantage of the next acceleration curve appeared first on Made Tech.

Public sector organisations are under constant pressure to innovate, modernise services and explore new ways of working. But across government, there’s a growing frustration with ideas that never seem to make it beyond the proof of concept stage.

Pilots are launched, prototypes are tested and early promise gets people excited, only for momentum to stall before anything reaches production. Sometimes that creates scepticism around innovation itself. If services never go live, what was the point?

According to Ben Pirt, Principal Technologist at Made Tech, and Geraldine Mathews, Made Tech’s Client Partner, the answer is more complicated than simply labelling these projects failures.

“Some proof of concepts should bring a stop to a process,” says Ben. “That is exactly what alpha is there for: testing whether something genuinely meets a need before too much time, money or complexity builds around it. The bigger challenge is making sure that the ideas which should move forward are designed for real-world delivery from the start.

“That means building earlier, testing with users sooner and thinking about integration, operational pressures and delivery constraints long before a service reaches production.”

Ben continues: “The whole point of the process is to have some stage gates so you can make sensible decisions as you go. People have been burned by the old-school approach of spending a long time building something and then getting to the end and realising it wasn’t actually what was needed, or it didn’t fulfil the right need, or legislation had changed. This process exists so you can learn that earlier.”

Geraldine and Ben agree that the proof of concept process gives teams the chance to test whether something genuinely meets a need before too much time, money or complexity builds around it.

“Alpha is definitely about identifying that there is a need and identifying the right shape of the thing to build,” Geraldine explains. “By the time you get to beta, you should know that it’s fulfilling a need. Beta should be more about rolling something out gradually and learning the things you can’t know until real people start using it.”

That distinction matters because it changes how teams think about progress. A decision not to move forward after testing is not necessarily a failed project. Often, it is evidence that the process is doing exactly what it was designed to do.

Why good ideas still struggle to become live services

Where things become more difficult is in the gap between a promising proof of concept and a service that can operate successfully in the real world. In government, even relatively straightforward services rarely exist in isolation.

“You almost never build a system that doesn’t integrate somewhere else,” Geraldine says. “Even if it’s something quite small, there’s usually another dependency, another team, another platform involved. That’s just the nature of government services.”

Those dependencies can quickly become the real challenge. A proof of concept may demonstrate clear value, users may respond positively and teams may want to move ahead, but the surrounding environment is not always ready to support it.

“Sometimes it’s operational overload,” Geraldine explains. “Teams just can’t take something else on right now because they don’t have enough people. We’re seeing that all over government at the moment. In other cases, it’s integration, data sharing or legacy systems. Somebody can have a really good proof of concept that would genuinely help citizens or caseworkers, but taking it live becomes difficult because there are too many dependencies in other places.”

Building earlier and learning faster

That complexity is one reason why Made Tech prefers to get tangible services in front of users early rather than spending long periods in theory or planning.

“We don’t stay on paper for a long time,” Geraldine says. “We build, we show, we go.”

That approach reflects a broader shift happening across delivery teams. Expectations have changed. Clients increasingly expect to see working services earlier in the process rather than static wireframes or long discovery documents.

“I think it’s the expectation that things can be done a lot faster now,” Geraldine says. “Clients are hearing that. They’re going to conferences, they’re reading about AI and rapid prototyping and they know it’s possible to move more quickly. It’s not just mock-ups on a wireframe anymore.”

For Ben, this comes back to a simple principle: learning happens through doing.

“A lot of big consultancies became known for massive discoveries and huge amounts of upfront thinking,” he says. “But you don’t really know if any of it is right until you hit the ground and start testing with real people. You can plan and think as much as you like, but until people are actually using something, it’s all still theoretical.”

Why Made Tech does not believe in wasteful delivery

That same mindset shapes how Made Tech thinks about alpha delivery itself. There is a long-standing belief in some delivery circles that whatever gets built during alpha should be disposable. Ben understands the reasoning behind that idea, but believes it can sometimes create unnecessary waste.

“The intention behind it is right,” he says. “You don’t want people becoming too attached to early decisions because they’ve already invested time in them. But if you’re building something that people are genuinely going to use for testing, it still needs proper infrastructure, it still needs to be secure and it still needs to meet certain quality thresholds. Once you’ve done that, throwing it away and starting again can feel pretty wasteful.”

Instead, the focus should be on remaining flexible enough to change direction when needed, rather than assuming everything built early on must eventually be discarded.

A pragmatic approach to government delivery

That pragmatism comes through repeatedly in how both Ben and Geraldine describe delivery. Rather than trying to fully untangle every complexity at the outset, the focus is on finding something meaningful that can be understood, built and tested quickly.

“A lot of government services have evolved over years and years,” Geraldine says. “Policy is layered, legislation changes and nobody completely understands every part of it. We’re probably better at going in, finding something smaller that we do understand and showing something tangible around that, whether it’s service design or a front end. Then we work our way through the complexity instead of getting stuck in it.”

That practical approach also shapes how Made Tech works with clients more generally. Ben describes it as a willingness to adapt to whatever the project actually needs rather than rigidly sticking to an original scope.

“We’ve historically been good at just doing what needs doing,” he says. “If the shape of the project changes halfway through, then fine, we change with it. It’s a very pragmatic approach.”

What comes through in the conversation is a practical view of delivery. Less focus on innovation for its own sake, more focus on getting something useful in front of users early and learning from it.

The important thing is not that every proof of concept reaches production. It’s more about ensuring that teams learn, early enough, to make the right decision about what happens next.

Learn more about our public safety and defence expertise and how Made Tech can help your organisation.

The post Embracing proof of concept to deliver transformation faster appeared first on Made Tech.

Modernising legacy technology has never been about simply replacing what exists. As we saw in the previous article, the real challenge is understanding complex systems and improving them without disrupting the services that depend on them.

AI is now accelerating that process. It is changing how teams analyse, rebuild and improve legacy systems, but it is not removing the need for careful engineering or clear thinking.

Ben Pirt, Principal Technologist at Made Tech, describes the impact of AI. “It has been phenomenally helpful in understanding a legacy codebase,” he says. “With the right inputs, AI can analyse structures, surface relationships and explain how systems behave in a way that would previously have taken weeks or months to piece together.”

That kind of visibility matters because discovery is often the slowest part of modernisation. Teams need to understand not just what a system does, but why it behaves the way it does, and how that connects to real-world processes. AI does not remove the need for that work, but it can accelerate it significantly.

Speeding up redevelopment without losing control

It is also starting to change how redevelopment happens. In one example, teams used AI to extract behaviours from a legacy codebase and treat those behaviours as a set of specifications. They then used AI to reimplement those behaviours in a new language, bringing tests across at the same time.

The results were immediate. “A week-long test got through a huge amount,” Ben says. “For certain types of work, particularly where patterns are well understood, AI can speed up delivery in a way that would have been difficult to achieve even a year ago.”

That is especially true for more repetitive tasks. Building API endpoints, following established patterns and generating boilerplate code are all areas where AI is already performing well. As Ben puts it, it is “insanely good” at following structured instructions.

It would be easy to see this as a shortcut to solving legacy problems, but the reality is more complex. AI can move things forward quickly, but it can also replicate existing issues just as fast if it is not used carefully.

“If you just say to AI, ‘port this code’, it’ll do it,” Ben explains. “But it might not do it very well.” In that scenario, the risk is that you carry forward the same poor design into a new environment, rather than improving it.

That is why engineering discipline still matters. If anything, it matters more. Strong testing, clear specifications and careful validation are what make AI useful rather than risky.

Just as importantly, organisations need permission to approach AI incrementally. That often means starting in low-risk areas, testing where it adds value, and creating space for teams to learn without feeling they have to bet the service on a single decision. In practice, responsible innovation often depends as much on creating that permission as it does on the technology itself.

Ben describes using AI within a controlled, test-driven approach. Behaviour is extracted, verified against the existing system and then used to guide the new implementation. The AI is not left to decide what “good” looks like on its own; it is constrained by clear rules and expectations.

“If you force it down a rigorous path, you can get extremely good quality,” he says. “Without that structure, the outputs are far less reliable.”

That structure also has to include transparency and governance. If AI is helping analyse, generate or recommend changes to legacy systems, teams need to understand how those outputs are reached, how decisions are validated, and where accountability sits.

Avoiding a new generation of technical debt

There is also a broader question about how AI is used across organisations. As tools become more accessible, it becomes easier for teams to build solutions quickly. That can be a positive shift, but it also introduces a familiar risk. Geraldine Mathews, Client Partner at Made Tech, highlights the concern that organisations may start solving problems in isolation. One team builds something for one part of the service, another team builds something elsewhere, and the overall journey becomes more fragmented rather than less.

“It’s got to be about the full user journey,” she says. “Without that focus, there is a real chance of creating a new layer of technical debt on top of the old one.”

Geraldine continues: “This is where the conversation becomes particularly interesting. AI is often positioned as a way to reduce technical debt, but it may also change what technical debt looks like. Instead of slow, ageing systems, the risk becomes fast-moving, disconnected ones.

“The technology itself is not the issue. The challenge is how it is applied. Without a clear delivery approach, strong architecture and a shared understanding of user needs, speed can work against you.”

AI is an accelerator, not a shortcut

At the same time, expectations are rising quickly. Organisations are seeing demonstrations of AI rewriting legacy systems and naturally begin to expect similar results. As Geraldine notes, “the expectation is going to be very high now”.

“That creates pressure to move faster, but it also increases the importance of getting things right. Delivering quickly is only useful if what you deliver is coherent, maintainable and aligned with how people actually work.”

This is also where risk assessment becomes practical. Rather than asking whether AI should or should not be used, the better question is where it is appropriate, where human oversight should remain, and how risks can be reduced through staged delivery. That is often where Made Tech works closely with clients, assessing the service, identifying suitable use cases, and proving approaches safely before scaling them into live environments.

Geraldine continues: “There is also a question about how far AI can go in redefining legacy modernisation. Some suggest that older systems can simply be translated into modern stacks with minimal effort. While that is technically possible, it risks missing a key opportunity.

“Porting a system does not improve it. It changes the environment it runs in, but it does not address the underlying design issues or the mismatch with user needs. Without that deeper work, the same problems are likely to resurface.”

That is why the fundamentals remain the same. Understanding users, designing around real workflows and building systems that can evolve over time are still central to successful modernisation. AI can support that process, but it cannot replace it.

In practice, the most effective use of AI is as an accelerator rather than a solution in its own right. It helps teams understand systems more quickly, test ideas more thoroughly and deliver certain types of work more efficiently.

What it does not do is remove the need for judgement. As Ben puts it, the best engineering practices still apply, and in many cases, they become even more important when AI is involved.

Looking ahead, the role of AI in legacy modernisation is likely to evolve quickly. The organisations that benefit most will not be the ones that adopt it fastest, but the ones that use it most thoughtfully.

In that sense, AI does not change the goal of modernisation. It changes how effectively that goal can be achieved, provided the focus remains on building systems that genuinely work for the people who rely on them.

Learn more about our public safety and defence expertise and how Made Tech can help your organisation.

The post AI is an accelerator, not a shortcut, when tackling legacy systems appeared first on Made Tech.

Spend any time inside a policing or justice organisation and you start to see the same patterns. People switch between systems, copy information from one place to another, and rely on spreadsheets just to build a complete picture. None of it feels deliberate, but it has quietly become the way work gets done.

That is what technical debt looks like in practice. It is not an abstract IT issue, but something that shapes the working day and slows people down. In already stretched environments, that friction quickly becomes a real problem.

The instinct, when things get this tangled, is to start again. Replace the systems, wipe the slate clean, and build something new. It sounds decisive, but it is rarely the safest or most effective option.

In many cases, the better approach is more measured. Modernisation does not have to mean starting from scratch, and new does not automatically mean better. The organisations seeing the most success are often the ones improving what they already have, rather than replacing it wholesale.

What technical debt really looks like

Ask people to define technical debt and you will get a range of answers, but Ben Pirt, Principal Technologist at Made Tech, puts it simply. “It ultimately just looks like code that you can’t really maintain. Once systems reach that point, everything becomes harder, from making small changes to finding people who understand how things work.”

Ben adds: “What is often overlooked is that age is not the only factor. Some systems have been running for decades, while others are only a few years old but already difficult to work with.

“I think the issue comes from neglect, whether that’s over a long or short timescale. Most systems were built with good intentions, but as standards move on and expectations change, they are not always updated to keep pace. Over time, they drift further away from what users actually need.”

Where legacy systems hit hardest

That gap tends to show up most clearly in frontline roles. Geraldine Mathews, Made Tech’s Client Partner, describes environments where caseworkers move constantly between systems that were never designed to work together. Each system does its own job, but there is no single view, so people are forced to piece information together themselves.

“If you wanted to know one thing, you logged into that system. For something else, you logged into another, and never the two should meet. The result is duplication, repetition and a steady loss of time. Users just want IT to work for them, be intuitive and solve the whole problem,  often in difficult and complex situations.”

Geraldine continues: “They really just want to be sitting down, looking someone in the eye. IT should support that work, not take time away from it.”

Part of the reason this situation develops is structural. In the commercial world, products improve because organisations compete for users. In government, internal systems do not face that same pressure, so once they are delivered, they can remain unchanged for years.

“They’ve got to be maintained,” Ben explains. “Without that natural incentive to evolve, systems tend to stagnate. Over time, workarounds build up and the gap between what the system does and what users need continues to grow.”

Why evolution beats replacement

At that point, a full rewrite can seem like the obvious answer. It offers the promise of a clean start and a chance to fix everything in one go. In reality, it introduces significant risk, especially when the existing system is still handling critical work.

There is also the challenge of understanding what the system actually does. In many cases, documentation is limited and knowledge has moved on. Switching everything off and replacing it in one step leaves very little room for learning or adjustment.

A more practical approach is to evolve the system in place. That does not mean preserving everything, but it does mean understanding it properly before making changes. As Ben puts it, “It’s not a legacy IT problem,” and starting with technology alone misses the point.

The first step is to understand how people use the system today and where it causes friction. That means combining technical analysis with user research and service design. Only then does it make sense to decide what needs to change and how to change it.

Reducing risk without stopping the service

Risk is always part of the conversation, especially in services that handle large volumes of sensitive data. The way change is delivered makes a big difference here. Incremental approaches allow teams to test, learn and adjust before committing fully.

Ben describes work on a system where the team analysed existing behaviour, then tested a new version against real data. They ran both systems in parallel and gradually shifted usage across, monitoring the results as they went.

“We compared it against millions of real examples,” he says. “In some cases, we even discovered issues in the original system that had gone unnoticed.” The transition itself was so smooth that “no one noticed”, which is often the best possible outcome.

This approach also brings users into the process earlier. Instead of delivering something new at the end of a long project, teams can show progress as they go and gather feedback along the way. That creates a stronger sense of ownership and improves adoption.

For people who have used the same systems for years without being consulted, being involved makes a real difference. It turns change into something they are part of, rather than something that happens to them.

Dealing with uncertainty

One of the challenges with this kind of work is uncertainty. Legacy systems often contain hidden dependencies and behaviours that only become visible once you start digging into them. That can make it difficult to define exact timelines and costs upfront.

“There are huge amounts of unknowns,” Ben says. “The most effective projects tend to acknowledge that reality rather than trying to plan around it. They rely on experienced teams who can adapt as new information emerges.”

There is also a growing recognition that technical debt is not just an operational issue. As systems age, they can introduce security risks as well. The longer technical debt is ignored, the harder it becomes to address. What starts as an inconvenience can gradually turn into a barrier to change and, in some cases, a source of real risk.

This all points towards a more balanced view of modernisation. It is not about defending legacy systems, but it is not about discarding them either. The goal is to improve what exists in a way that is controlled, sustainable and grounded in real needs.

That approach can reduce costs by extending the life of existing systems, while improving performance where it matters most. It also lowers risk by avoiding disruptive, all-or-nothing change.

For organisations working in policing, justice and across government, that is often the more responsible path. It delivers progress without unnecessary upheaval, and it keeps the focus where it belongs, which is on the people using these systems every day.

Learn more about our public safety and defence expertise and how Made Tech can help your organisation.

The post Modernising the legacy estate: reducing technical debt without starting from scratch appeared first on Made Tech.

This post is part of our Delivering Public Safety Outcomes at Pace series.

A few standout digital services have changed what people expect from technology. Whether that’s Uber reducing the friction of booking a ride, Netflix recommending the next big Korean hit or Amazon recommending the must-have accessory to complement your last purchase, today’s digital world feels simple and intuitive.

Once people get used to that kind of experience, it quickly becomes the benchmark for everything else they use and public services are no exception.

For teams delivering vital services, that shift in expectation creates a difficult balance. Citizens and businesses want services that feel quick and easy to use. At the same time, public sector delivery sits alongside policy requirements, operational realities and tight budgets. According to Sarah Ward, Delivery Director at Made Tech, designing around user needs is not about ignoring those constraints, but is about navigating them carefully while still building something people will actually use.

User needs meet policy and operational reality

One of the biggest challenges is often cultural rather than technical. Many public sector organisations still carry the memory of large IT programmes that ran over budget or failed to deliver what they promised. Those experiences continue to shape how new services are approached today.

“Our customers are still scarred by decades of IT delivery challenges,” Sarah says. “They still struggle with the concept of a minimum viable product that is then iteratively delivered and improved.”

Launching something that is not yet fully complete can feel risky in that environment, particularly when funding cycles are structured around fixed annual budgets. Departments worry that if something launches unfinished, the money will disappear and they will be left with a half-built system. The key, however, is to consistently build and demonstrate the value creation that the product and service bring to citizens.

James West, Made Tech’s Industry Director for Public Safety, Security and Defence, believes part of the answer lies in rethinking how digital services are viewed in the first place. “Too often services are treated as large, one-off programmes rather than products that evolve,” he says. “The service design and the product you build are never finished. Ultimately, you’re balancing the needs of citizens, what the service users need and what government can actually afford. These decisions continuously change over time, and that is right.”

That balancing act is the inflection point where many of the real delivery decisions happen. Policy teams may define the outcome they want to achieve, but translating that into a service that works for real users requires constant adjustment. James argues that the traditional model, where policy defines the requirements and then hands them over to delivery teams, often creates friction.

“Policy do their bit, then they fence it over to delivery,” James says. “And there’s a constant to and fro between the two sides.”

For James, the answer is not more process but closer collaboration, with digital thinking embedded much earlier in policy design.

Why early delivery matters

Even when teams agree on the approach, releasing something early can feel uncomfortable. Sarah recalls one project where an organisation agreed to launch a new digital service even though it did not yet match every feature of the system it was replacing.

“I think trust is huge,” Sarah says. “That’s the only way we got through it.”

The decision was not universally popular. As feedback started coming in, stakeholders questioned whether the organisation had moved too quickly.

“They did wobble,” Sarah says. “They nearly rolled back because they didn’t like the negative feedback. This is where experience, guidance and trust with proven operators really come to the fore.”

But launching early also revealed something not highlighted during user research. Once users started interacting with the service, one request kept appearing in the feedback.

“One of the things users really valued was widgets on their phone,” Sarah explains. “That hadn’t come up in any of the research we’d done.”

Because the service had been released to a small group first, the team could respond quickly. The team built a simple widget over a short sprint cycle and released it before the full rollout.

“That’s the value of launching early,” Sarah says. “If we’d waited, we wouldn’t have known.”

For James, examples like this illustrate why approaching programmes differently enables success. “When projects become too big – or the delivery partner is too big – they slow down decision-making and make it harder to respond to what users actually need.

“Big doesn’t mean best,” James says. “Big usually means status quo.”

Adoption is the real test

Ultimately, the success of a service is not defined by the scale of the programme that created it. It is defined by whether people choose to use it.

That is why listening to users once a service launches is so important. On one project, Sarah and the delivery team received thousands of pieces of feedback from people using the service. The Data and Insights team was prepared and quickly grouped the feedback, which was used to prioritise improvements.

“We could say we’ve heard you,” Sarah explains. “Here are the top things people are asking for, and here’s when they’re coming.”

This kind of transparency helps build confidence while services continue to develop.

Process or impact?

For James, there is also a broader question about how the UK approaches digital delivery in government. James believes departments sometimes prioritise process and safety over impact.

“We are now in a world where the speed of technology change requires leaders to act with more entrepreneurial spirit in service delivery, while acknowledging regulation and guardrails to ensure public trust is maintained,” he says. “We need to operate as leaders, not managers, to create a thriving future.”

That cautious mindset can slow progress at a time when expectations continue to rise. According to James, other countries have moved faster by treating digital services as ongoing products rather than fixed projects. It is, however, fair to acknowledge that some countries cited as examples don’t have the same issues around legacy debt that we have in the UK.

He does, however, believe that effective progress is still possible if organisations focus on outcomes and impact. “Start smaller, build the minimum viable product, release it and learn from it,” he suggests. “You might get some things wrong, but the overall cost will be lower and the service will improve faster.”

For Sarah, the practical advice is straightforward. “Be clear about the outcome you want to achieve. Define what a minimum viable product looks like. And be honest about what will come later. 

“Don’t be afraid to go with a minimum viable product. You learn so much once people actually start using it.”

Designing services around user needs does not mean ignoring the realities of government. It means working within them while keeping the focus firmly on what matters most.

James concludes by providing a trio of key takeaways that will drive technology change and enable leaders to put citizens and businesses at the heart of public services:

1. Policy is Digital and Digital is Policy: the two areas need to align further for future success
2. Adopt a product thinking approach to service delivery
3. Accelerate delivery and bring users closer via fast sprints and feature releases

“This is about leadership. Be clear on the outcome, give teams the space to deliver and stay close to what users actually need. If you do that, everything else starts to fall into place naturally.”

Learn more about our public safety and defence expertise and how Made Tech can help your organisation.

The post Putting citizens and businesses at the heart of public services appeared first on Made Tech.

An aligned strategy

What stood out the most was the level of alignment across discussions. Whether the sessions focused on data, AI, or national changes, one message came through loud and clear: technology and innovation are vital for fixing policing’s biggest challenges.

For the public, this means getting a consistent service. For officers, it means finally having a joined-up view, which is a significant shift when you have 43 separate forces.

The new National Policing Digital Strategy 2025-2030 makes it clear that tech needs to connect directly with actual police work. It also emphasises that law enforcement needs to collaborate closely with the tech industry to solve problems efficiently and cost-effectively.

Working together and collaborating

A significant theme throughout the summit was the need to collaborate more effectively with industry and universities. The current plan is to open up the NPCC Problem Book to a broader audience and utilise a crowdsourcing approach to find solutions proactively. I believe this “test and learn” method will greatly assist police in achieving their digital goals.

We also heard a direct challenge to tech suppliers: build interoperable products that work together seamlessly. This is what many of our clients are asking for right now. There’s also a shift away from long, complex procurement processes towards more early conversations, through pre-tender market engagement, to build better working relationships with industry partners.

Fixing data first

Data was another central talking point of the summit, and rightly so. Many discussions focused on the need to fix the fundamental data problem. If your core data is messy, even advanced AI won’t work well. Natasha Angwin, Data Policy Lead at UK Home Office, and Aimee Smith, Director of Data at the Metropolitan Police, both emphasised that data must be treated as a key national asset, and its quality, standards, and ability to integrate with other systems must be improved.

The Reality of AI

As you’d expect, AI was a hot topic, examining both how it boosts productivity and the ethical concerns it raises.

Case studies clearly showed how AI tools can speed up tasks for police or how autonomous agents might aid decision-making. However, the message was clear: AI will aid decisions, not replace sound human judgment.

On a personal note, being from Staffordshire, I was proud to see how many of the digital innovations being tested across the country were happening right there first.

The Final Hurdle: Making It Happen Locally

The national strategy is set, and the excitement was real. The biggest issue now is ensuring that every individual police force buys into this national plan, as they still have the final say on how it is delivered locally.

The idea of a National Centre for Policing and pushing for a single direction for the entire Criminal Justice System (CJS) are essential steps to get everyone on the same page.

The summit successfully established a clear and consistent path. Now, the real work of translating this unified vision into practical, local action begins.

Find out more about our national safety and public security industry expertise.

The post Turning Vision Into Action: My Reflections on the NPCC Innovation and Digital Summit 2025 appeared first on Made Tech.

Q: What were your first roles in tech and how did they shape your career?

I began in a traditional technology consultancy, where I spent a year coding in Oracle/SQL which provided a strong technical foundation. From there, I moved into business analysis, project management and eventually programme management.

Along the way I worked as a product specialist for Siebel, a CRM tool that predated Salesforce. I was in their fast moving consumer goods team. During this time I spent my 20’s flying around the world looking after projects.

I came back to the UK as I was at a stage in my life where I wanted to get married and start a family. So I joined a consultancy and had projects in the UK which were great. 

While I was on maternity leave, Vodafone bought that consultancy I was with because of our expertise in mobile device applications. I spent 14 years under Vodafone doing various different roles. One of which was helping out on their seal implementation. We worked with Accenture on that. It was the biggest programme for them in Europe at the time.

I left to join the civil service and moved into public sector leadership at the Department for Transport (DfT). After that I did a bit of time at Deloitte, and now here I am at Made Tech.

Q: What made you want to make the shift back to working with government departments full time?

Although I had worked alongside the Big 4 for much of my early career, I had never actually worked for one. So, I was thrilled to receive a job offer from Deloitte. While there I spent 18 months at the Home Office on a project to replace one of the legacy applications within the Future Borders portfolio – which was really interesting. 

I was leading a large blended team of Deloitte people, other consultancies and civil servants. It was challenging but also fun and exciting, and I really enjoyed it. I then decided to dedicate myself to full-time public sector delivery. I was really pleased to join Made Tech where it’s our sole focus. The passion for public sector and delivery expertise in our Made Tech teams has made my first few months really enjoyable. Long may it continue!

Q: Thinking about your work with the public sector, which project had one of the biggest impacts?

One of my favourite projects was when I worked at Vodafone. We rolled out mobile devices to adult and children’s social care in Nottinghamshire County Council. 

The reason I really loved it is because a lot of the people in that role are women with families. They previously worked on fixed office computers with specific deadlines. Many would be out for an assessment on a child or adult and have to race back to the office to write it up within 48 hours or 24 hours – depending on the severity. 

By empowering women with mobile technology they could choose when to do their writeup. They had the option to go home and pick their kids up from after school care for example. Being able to give all parents, but predominantly women, the flexibility to work like that was really positive and made a huge difference to their lives. I’m proud of that one. 

Q: Let’s talk about your time at the Department for Transport building their digital services team

I joined the newly established digital services team at DfT. I spent 5 years focused on creating the team, setting up project management and digital delivery.

It was a really interesting and challenging thing to do, especially coming into the civil service. It was a significant shift from the private sector. I quickly realised how different government operations were, especially concerning budgets and the annual spending cycle.

Q: That’s a lot of adaptability, what are some of the biggest challenges you’ve faced?

Project delivery is hard. Digital delivery is a really tough thing to get right and it often goes wrong. You can automate parts of it but ultimately it’s a set of people with a desire to get the thing done. 

I’ve seen a lot of projects that have stalled over the years across different technologies and sectors for different reasons. It can be really heartbreaking when you’ve worked on something for a long time to not see it actually go live.

However, staying positive – I think that compared to when I first started my career, delivery progress is much quicker and services go live much more frequently now. That’s the benefit of using agile methodology.  Focusing on delivering the features that have the greatest value first, taking regular feedback and rapid iterations have all helped improve digital delivery.

Q: How do you get through those challenging moments?

I’ve spent a long time in this career, and I’m at my best when I’m challenged, pushed out of my comfort zone and learning new things—that’s when I’m most excited. I’m best when I’m on a learning curve.

Some advice for those moments? Just say yes and figure it out later. While that may not work every time, I often try to have that in my mind. When someone asks you to step outside your comfort zone, adopting a ‘just say yes’ mindset helps you figure it out along the way.

Q: Finally, what made Made Tech stand out as your next step?

The thing that attracted me to this role was the split between delivery and growth. I joined because the role lets me support delivery, lead delivery teams and contribute to the organisation’s growth.

The growth trajectory we’re on at Made Tech really motivates me. I’m excited by the opportunity to work in digital delivery and specialise in the public sector, watching it grow and mature.

If you’re also motivated by the idea of creating change in the public sector, take a look at our open roles. 

The post Creating change in public sector delivery appeared first on Made Tech.

In my work helping organisations stretch their thinking about what’s possible, I’ve come to realise that cultural resistance is a universal issue. Whether in policing, justice, or border security, people react to change in 2 main ways. Clinging to the familiar and outright resisting anything that disrupts their comfort zones. Or in contrast, runaway change agents who enthusiastically embrace every idea, often without focus or practicality. Let’s face it – AI isn’t about small tweaks. It demands a seismic shift in how we work.

Cultural resistance in public safety is holding back AI

The challenge with AI isn’t just about adoption. It’s about trust. People don’t fear change itself – they fear what it means for them personally. Will it make their job harder? Will it strip away tasks they enjoy? Or worse, will it make their role obsolete? On the flip side, people embrace change when it improves their lives. This is why AI needs to be introduced through carefully chosen use cases that show immediate, tangible benefits.

For instance, I’ve seen early wins with AI in policing and asylum management. AI tools are improving facial recognition, reducing fraud, and prioritising domestic abuse calls. These aren’t just nice-to-haves. They’re significant improvements that directly impact frontline workers and the public.

Fix the basics first

However, basic issues need fixing first (laying the groundwork Jim refers to) before we can fully see AI’s benefits.

• Data maturity: Many public safety organisations lack a clear understanding of their current data landscape. What’s the quality of the data? Where is it stored? Who has access? These are basic questions that even trip up private sector organisations.
• Leadership confidence: Confidence is often lacking. The fear of the unknown leaves leaders hesitant. Without a clear grasp of AI or strong use cases, progress is limited.
• Use case prioritisation: AI’s potential can feel overwhelming. By focusing on a few high-impact use cases, we can start to build momentum and demonstrate the benefits.

Focus on use cases that solve real problems

The first step in making AI work is rooting out use cases that tackle real problems. My recent review of AI use cases across justice and emergency services, including the Ministry of Justice, Home Office, and 43 police forces, revealed an exciting opportunity. While many organisations are currently exploring AI through proofs of concept, many still need help to move towards enterprise-level deployment. The review brought home to me how crucial it is to avoid getting stuck in endless pilots.

Using AI in public safety to solve practical, everyday challenges – like improving police response times or monitoring offenders – can drive real-world change. 

And there are already some promising examples of AI making a real difference. For example in policing, AI is being used to analyse 999 and 101 calls related to domestic abuse, quickly identifying threat levels to prioritise responses and enhance safeguarding. What’s even more impressive is that some of these solutions are working without relying on extensive historical data – which is often where data quality issues cause delay. If you haven’t already read it, I recommend checking out my previous blog, Challenges in Public Safety Services, for a deeper dive into the issues that AI must address in this space.

The right technology for the job

Once you’ve overcome the cultural hurdles and identified the right use cases, the next step is ensuring the technology can deliver. AI needs to be flexible, scalable and able to integrate with existing systems. In national security, for example, AI could dramatically enhance intelligence gathering – but only if it’s robust enough to handle real-time data and operate across different agencies.

Technology also needs to work for the people using it. Frontline workers rely on tools that are intuitive and reliable. If AI systems are clunky or don’t fit with existing workflows, they won’t be used effectively. Public safety organisations must collaborate much more closely with tech providers who understand their unique needs and challenges. Building these strong relationships not only helps to deliver more practical solutions but also speeds up great outcomes.

Driving meaningful transformation in public safety

One of the most exciting shifts that AI is bringing, is how it challenges traditional leadership hierarchies. Historically, frontline staff – whether in the police, border force, or probation – have had to wait decades to rise through the ranks before they’re in a position to address the issues they encounter daily. By the time they have the influence to drive change, the problems may have evolved, or the opportunity to act has passed. 

AI is cutting through that cycle. It’s enabling those on the front line to identify and act on solutions now. It’s empowering users with low code tools and basic Large Language Models which fundamentally change the way services are delivered. This isn’t a threat to leadership, but a chance to rethink how we take advantage of that knowledge and spontaneous development.

By capturing the insights of frontline workers and prioritising their input, we can create innovation roadmaps and AI use cases that address immediate needs and drive meaningful transformation. This approach shifts power, (better described as actionable knowledge) towards those closest to the problems, allowing solutions to emerge faster and with greater relevance and governance.

Transformers….. leaders in disguise

In addition to the 6 archetypes that are outlined in the whitepaper, I liked the idea of adding a ‘transformer’ archetype. Transformers are the leaders who make AI adoption a success. They identify the right use cases, champion technology that works, and create a culture of innovation., transformers are the bridge between strategy and action. They ensure AI isn’t just a buzzword but a tool that solves real problems and makes a difference on the ground.

For me Transformers are crucial because they do three key things:

In sectors like public safety, where hierarchical leadership is still common, transformers are key to unlocking the potential of AI. They take the insights from the people closest to the problems – frontline police officers, probation teams, or border agents – and provide leaders with the confidence to act and operate in the wild west.

How to move faster for public impact

If public safety and national security are going to benefit from AI, we need to be intentional. That means focusing on use cases that matter, choosing technology that fits, and building a culture that supports change.

For public services to deliver on key manifesto pledges we need to move faster than we’ve been able to before. This requires leaders to step up, think differently, and break down barriers between technology, civil service, policing, and probation. Collaboration is the only way this is going to work.

One practical step is running workshops to define and prioritise AI use cases. But these can’t just involve senior leaders. As I’ve just mentioned, the need for frontline police officers, probation officers, and others working directly with the public to shape these discussions.  In that way we make sure that the solutions we develop are grounded in real needs and deliver actionable results.

By prioritising collaboration and practical outcomes, we can make AI a powerful tool for tackling the challenges we face.

Discover the full picture of AI readiness

To find out more about the pillars of AI adoption and explore the archetypes that drive success in public safety and national security, download our whitepaper Laying the groundwork for AI.

Ready to take action? Get in touch to arrange an AI readiness assessment or a use case workshop. Together, we can define practical, impactful solutions that make a real difference.

The post Building AI readiness in public safety appeared first on Made Tech.

The pressure on public services

To meet the needs of society, we need services that are modern, secure and easy to change. But the problem remains – our public services are beyond stretched. 

Not only are they under immense pressure due to growing communities, but money is tight. And current spending is unsustainable. Balancing budgets and meeting citizen expectations can feel like walking a tightrope.

A £22 billion reality check

Against the backdrop of an audit last year, a £22 billion black hole was found in the public finances, highlighting the rocky nature of public service funding. It’s becoming increasingly clear that the status quo is no longer viable.

It’s no surprise that this is reflected in dramatically declining public trust. I’ve spoken before about the “Netflix effect” of instant service. As a quick reminder, this is where the public expects the same seamless digital experiences they get from Amazon and Netflix. Government services often fail to meet these high expectations. We want information at our fingertips, with connected, real-time insights and communications. That’s a stark contrast between the digital experiences we’re used to and the services we receive from our critical public organisations.

Data maturity and the way forward for public safety

Making digital and data perform in this way in public safety and justice organisations is difficult. Anyone who has worked across government departments knows there are inconsistent standards. From department to department there’s a varying degree of data maturity.

Combine that with often siloed data and this seriously impacts service delivery and transformation. There’s a very real need for cross-department data sets to talk to one another if we want to help better manage support in our communities and reduce cross department failure demand and service delivery costs. 

All this to say that the pressure is mounting on local authorities, policing, ambulances and the wider civil service we all rely on each and every day. 

6 strategies for transformation

That’s a lot of complex challenges our public safety services are facing. How do we go about creating meaningful change from the inside? I’ve put together a list of 6 crucial areas to focus on when it comes to transformation that can help organisations do more. 

1. Data interoperability for a joined-up experience

Interoperability is the ability for different systems to communicate and share data with each other. To build a truly efficient and cost-effective service, data interoperability is crucial. To get started with this, you need to understand your data strengths and weaknesses. Then you’ll be in a better position to see where and how you can automate sharing and provide secure API access with relevant teams. By better connecting systems across departments we can support better human decision-making. This way of working also streamlines service delivery and creates an experience that puts users first.

2. Dare to imagine a legacy-free future in public safety

The future of public services needs to be free from the unmovable constraints of legacy systems. Legacy technology locks your data in inflexible platforms that are rarely designed around users. To accelerate transformation, we must challenge the status quo. Change means reimagining how services can be delivered in a modern, tech-driven way. 

While this may sound daunting, start with a small slice of your service. Modernise and migrate it, then ideally launch that slice into the world – have real people using and testing it. The benefits this brings are many. It promotes and de-risks your approach and gets value out there much quicker. It also allows you to make smarter use of automation and AI tooling (more on that below). 

One thing has always been true, transformation requires boldness and bravery. This means embracing cloud solutions to create services that are faster, smarter and more user-friendly.

3. Automation in public safety

Across the public sector we have yet to scratch the surface of creating capacity through automation. As services move more towards digital solutions, automating repetitive tasks frees up a team’s valuable time. They can then focus their time on more high-impact activities. Automation really is an effective way to enhance delivery while supporting wider transformation.

Master data management (MDM) software can help you here. This is a great piece of tooling to use that helps improve data quality, reduce errors and increase efficiency. Ultimately MDM works with your data governance to make sure all data is accessible, secure and usable.

4. AI and ML for smarter service delivery 

Once you have your data in order, the integration of artificial intelligence (AI) and machine learning (ML) tools can help you further. When done right, these tools are all backed by research driven by citizen’s needs – helping you deliver and make decisions at pace, in real-time. 

It can seem daunting getting started with laying the groundwork for AI. So we’ve put together a handy framework to help you understand and navigate these challenges when it comes to transformation. I also discuss how to prepare for AI in my building AI readiness in public safety blog.

5. An outcome-first approach

At the risk of sounding cliché, transformation is a marathon – not a sprint. It’s tempting to wait for the perfect solution, but real change comes from prioritising action, so you can see incremental impact. Don’t let the quest for perfection delay progress. Focus on delivering value and iterating as you go. A great way to get started on this is through prioritised and time-boxed innovation.

At the end of last year I reflected on the benefits when it comes to this iterative way of doing things – which still stands true. By quickly testing initiatives and evaluating their effectiveness, we naturally transition to an outcomes-first approach. With this comes incremental and measurable results that matter and can have a huge impact on society.

6. Multidisciplinary teams are the secret to success in public safety 

Public service transformation requires diverse expertise. Building cross-functional teams with a mix of skills, from technology to policy-making, is key to progress. Clear roles and responsibilities within these teams enable efficient execution and quick wins.

Our national security and public safety teams are full of passionate people excited about creating change. Don’t underestimate the desire to upskill. You can overcome tech limitations and skill gaps with tailored training and technology roadmaps.

During any transformation project it’s also important to get these teams in front of stakeholders. Engaging other departments can open the door to the golden ticket of knowledge sharing. In a sector where people may have limited understanding of digital, data and technology – fostering trust is crucial. By connecting people and showing the ways transformation brings value, you create a desire for change and a culture that embraces it.

Creating change means being bold

My parting advice when it comes to public service transformation is that we must be brave. Leadership in this space means looking to the future and not being afraid to get things ‘wrong’.  That’s where the best solutions are made. 

Throughout this post I spoke a lot about data and AI. If that’s an area you want to get started with but feel overwhelmed, download our new whitepaper on laying the groundwork for AI.

Laying the groundwork for AI

Unlock your AI potential: Discover your archetype, master the 3 pillars of data maturity, and learn from real-world transformations in our latest whitepaper, Laying the Groundwork for AI.

Download the whitepaper

The post 6 steps to public safety transformation  appeared first on Made Tech.

Here are some of my takeaways from the event.

Why is progress in AI in public safety so slow? 

A major point of frustration for many in the room was the slow pace of progress on AI projects. What should take 6 weeks for a discovery phase or proof-of-concept (PoC) often stretches to 6 or 7 months. 2 key issues seemed to be causing this delay.

• Lack of open APIs: The absence of open APIs in policing prevents the seamless flow of data. When combined with vendor lock-in, this restriction hampers information sharing between agencies and stifles the scalability of AI applications.
• Fragmented IT systems: We know that many police forces operate with their own IT systems, leading to a lack of standardisation. This fragmentation makes it difficult to integrate AI technologies across different forces, as there is no unified approach to data management.

Building a fail-learn-fast culture

To drive progress, I believe we need a cultural shift that embraces the concept of ‘failing fast’ safely and securely. This mindset encourages organisations to experiment with new ideas and strategies without the fear of failure. By adopting this approach, public safety organisations can quickly test AI initiatives and evaluate their effectiveness.

One great example from the event was an AI assistant that analysed data and improved the handling of domestic violence calls. This initiative achieved a remarkable nearly 30% reduction in call time, allowing officers to respond more effectively and better support survivors of domestic abuse. This successful PoC is now being rapidly scaled to help with other types of calls. The key learning themes from this relate to being clear on the business problem to be solved and effectively managing business change. Another important aspect is gaining trust and buy-in from operational users.

The need for a strong data governance model

Attendees emphasised the urgent need for a robust data governance model. As it stands now, while police forces do share data, the interoperability across local, regional, and national levels is far from seamless. For example, data often moves from local police to the CPS and then to the MoJ. But issues with data quality and flow can limit the effectiveness of these processes. I feel strongly that removing the silos and improving insights is crucial for better case management and decision-making, especially regarding the victim and offender journey.

So there’s a clear need to improve data literacy and skills, define secure data handling in the AI world, and ensure data flows smoothly between agencies. To my mind, an inter-agency data and AI strategy is the answer. The emphasis should be on setting standards for new suppliers around open but secure APIs and data schemas that allow seamless integration and quicker time to value.

Rethinking traditional metrics and structures

I couldn’t help but reflect on the need to rethink how police forces are funded and evaluated. Traditional metrics don’t incentivise innovation or support digital projects. There was a strong consensus in the room that at a regional or national level we need to shift our focus to outcomes. We should look at how technology can support officer efficiency rather than solely focusing on a higher number of officers. To enable this, the current funding and governance model perhaps needs to change.

Enhancing transparency in AI decision-making

As AI continues to be integrated, we all discussed how important it is to understand the methodology behind AI-driven decisions. It’s not enough for AI to just provide answers. Decision-makers must know how it got to those answers. This transparency is a critical part of building trust with the public.

One of the exciting ideas discussed was fostering closer ties with academic institutions. I believe academic research plays a key role in exploring future use cases for AI and developing ethical frameworks. Collaborating with academia can ensure that AI applications in policing and justice are both ethically sound and evidence based.

Everyone’s role in AI governance

Something else that struck me during the course of the day was the need for stronger leadership from senior executives in AI and data governance. Traditionally, technology decisions have been left to Chief Information Officers (CIOs) or Chief Data Officers (CDOs). However, given the ethical and societal implications of AI, it’s going to become essential for senior leaders across all departments to be involved in these decisions. AI is no longer just a technology issue. It’s a strategic concern that impacts the entire organisation.

Strong leadership is also vital for scaling AI projects. I noticed a significant gap between innovation and enterprise-level implementation. This often indicates a lack of strategic enablement. Policymakers and senior leaders need to take a more active role in ensuring effective rollout and leverage national economies of scale.

The  future of policing

Attending this event emphasised the vast challenges ahead, but it also gave clear direction on where to focus our attention. I left feeling that the future of policing will require bold decisions, innovative thinking and a cultural shift towards sharing and transparency. I’m excited about the journey ahead.

To find out more about my views on the challenges in this sector, take a look at my blog on ‘The top 3 ways to address challenges in the public safety sector’.

Laying the groundwork for AI

Unlock your AI potential: Discover your archetype, master the 3 pillars of data maturity, and learn from real-world transformations in our latest whitepaper, Laying the Groundwork for AI.

Download the whitepaper

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