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.