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.

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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.

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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.

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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.

Adding to this complexity is the assumption that AI can modernise legacy software by itself – a  potential dangerous view of what’s possible if left unchecked. 

This creates a critical challenge: how can public services embrace the future when they’re tied to the past? I’ll explore the practical ways industry can partner with government to navigate these challenges. With a goal to enhance efficiency and leverage the power of emerging technologies, when it makes sense. 

AI, where can it help legacy systems?

First, we must recognise AI’s limitations. While some may still see a silver bullet, it’s far from it. That’s not to say it doesn’t have its merits. AI can be a great support tool, but it’s not the main event. 

For example, AI can be a great advantage in helping teams make sense of complex, outdated codebases in legacy systems. It can support your work by doing tasks like porting part of a code base from one language to another so it’s easier to understand. Using AI in this way can unlock a faster understanding of legacy systems. 

In projects we’ve used it when challenged by old code that didn’t seem to make any sense. By feeding it into an AI, we asked it to summarise what it was doing and rename variables in the code so they made sense. This technique was not just useful in helping our team get to grips with the legacy code, but it also set us up to easily check the outputs coming from the AI were correct. 

Recognising AI risks

While AI can offer usefulness in legacy projects, it’s crucial to inject a dose of caution. Industry must guide government in the risks. This is especially important when they touch critical, long-standing systems that serve the public.

It’s vital to avoid relying on AI when your team doesn’t completely understand what it’s doing behind the scenes. In the context of a complex legacy codebase, if you can’t validate the AI’s internal logic or its outputs, then you can’t validate any AI-generated suggestions or code. That should ring alarm bells. 

We’re not talking about simple prompts to instruct AI to generate, refine and debug a simple web service rather than manually writing it. We’re talking about public sector systems where errors could have severe, real-world impact.

Human expertise remains core in quality control. This brings the critical issue of responsibility. Who’s going to sign off on AI outputs and say, “Yes, that’s correct and I take responsibility if something goes wrong”? 

These are just some risks that come with using AI in government legacy projects that industry needs to communicate clearly and effectively. Our role isn’t just to provide the tools, but to ensure they’re used responsibly. 

Data privacy when AI meets legacy 

A major point I always come back to when it comes to legacy and AI is about where all that data is going to live. We’re talking about old, often intricate systems that hold vast amounts of sensitive data. When we consider using AI to help understand these complex codebases, or to help with migration and testing, we run into a fundamental challenge – data security.

As we’ve seen early on with large language models (LLMs) like ChatGPT, they learn from the data you feed into it. The risks are that it could give information back to other users and effectively leak your private data. In government, where the data involved in legacy systems is often sensitive like citizen information, financial records and critical infrastructure details, the risk is amplified. 

The privacy risk is also not just about customer data, but about keeping proprietary, closed-source code. It might have vulnerabilities and keeping it closed might be the only real line of defence while it’s being modernised. 

We must help government ensure that any AI tools they use have their learning material restricted as a key foundational step. This isn’t a technicality, it’s about preventing data breaches originating from the systems we’re trying to modernise.

Why deep code understanding still matters

This brings us to a current risk in software development. One that has big implications for how we’ll ever truly conquer those legacy systems. There’s a real danger that junior developers are going to become “prompt engineers”. That’s where their job is largely feeding prompts into LLMs, iterating and not really understanding what they’re doing under the hood.

If they’re not coding, how will they gain experience and deep architectural knowledge? This insight is core in helping government organisations on those tricky legacy problems.

Understanding a multi-decade-old, often undocumented, complex system requires more than just prompts. It needs debugging skills, logical reasoning and an engineer’s intuition built from years of hands-on coding. There’s a big risk to the industry’s ability to tackle future complex systems. That includes existing legacy ones and it’s something we need to be aware of and actively working against.

Hands-on coding experience remains a central part of development for making sure government has the skilled workforce capable of not just patching, but modernising legacy IT for decades to come.

A measured approach to AI in government

While not an exhaustive list, these are some of the ways industry can help government with legacy challenges in a way that makes responsible use of emerging technologies. AI isn’t a magic bullet. Its usefulness lies not in replacing human expertise, but as a support. 

AI’s potential can only be realised if we proceed with caution and clear understanding of its limitations. Given the fast-moving nature of AI, staying up-to-date with the latest developments is a challenge, making a cautious and clear understanding even more critical. 

By making sure human knowledge remains at the core of responsible AI implementation, we can build a more efficient and secure future.

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The post The public sector’s legacy burden: can AI truly help? appeared first on Made Tech.

Q: You started in an infantry role in the Army, what sparked your shift towards digital and technology?

That’s right. I joined the Army as an Infantry Officer – very much at the pointy end of the organisation. Then I was selected for my first technical job around the adoption of Bowman – which was the Army’s adopted digital tactical communications.

I was responsible for its introduction into the first armoured infantry battalion and its first deployment in Iraq. I was then promoted to Major and did a masters degree in information management technology. This set me up to build business applications for the Army.

When it comes to digital, I was responsible for overseeing the build of some of the software that the Army currently uses in its legacy portfolio. Back in 2012, this was the best that was available. Fast forward 12 years and it’s definitely not the best that’s available anymore…

Q: What career path did you take after leaving the Army and how did it lead you to Made Tech?

When I left the Army and before Made Tech, I went into consultancy with quite a generalist delivery approach. But found that when I looked back at the work I’d done, it had all been about digital delivery.

I realised that whether I intended to or not, I found myself to be something of a technologist. That was the work I enjoyed doing and the next logical step for me. 

What I found most exciting about Made Tech was that joining was an opportunity for me to help an organisation that had lots of good experience delivering digital programmes, in what I described as an agile-native environment in government. Being able to bring that into defence organisations along with my experience and different perspectives is something that I think defence really needs. 

Q: What’s your approach to digital in defence?

The reality is that everything’s a computer nowadays. Right from your toaster through to armoured fighting vehicles. For me this is one of the key points where defence should sit up and take notice.

By thinking about capability in terms of the underlying information system within it, you start to separate out the hardware and software elements and the platform and data structures too. All those things we take for granted as digital natives.

Once you start to think in that way, you come up with a very different approach to tackling big problems.

Q: You touched on legacy technology, let’s talk a little bit more about that. 

Legacy creates a problem across defence. For example, when you look at the Army, they still roll around much of their technology in vehicles designed in the 1950s and introduced in the 60s and 70s.

There’s some old bits of kit knocking around. The traditional approach is to use it till it falls apart, then build a new one because that’s what we’ve always done with vehicles. That doesn’t work with digital technology – we need more of an evolutionary approach. 

They didn’t think much about how they would integrate or keep pace with the wider industry and developments in technology. That oversight now creates a big problem in the sector and defence needs to fix it.

Q: Beyond legacy, what other challenges are we seeing in defence? 

Well, we’re now at a nexus point where the expectation of the user – the next generation joining the military or the next generation of civil servants – have expectations of what digital technology should look like. The realities of defence’s digital environment really mismatch those expectations.

That impacts a number of areas. First it’s lower workforce happiness, which feeds into the second one which is that it also impacts productivity. So you’ve got a less productive workforce and not a particularly happy workforce. That makes everything about the business harder to do. 

However, the thing is that having lots of legacy platforms operating in their own silos means that there’s lots of opportunities to improve things. Artificial intelligence (AI) is really hard to apply to defence because their data is not organised in a way that allows them to take advantage of it.

Defence organisations have got to get their digital information systems into a position where they can properly exploit the opportunities available in the wider market – like AI. 

Defence is starting to chip away at this stuff. As they do, the data they release is going to help others to justify doing more. But at the moment, because most haven’t sorted the data side, they find it difficult to justify their work and show the productivity benefits.

Q: How do you think the industry can bring in these new ideas – and implement them well?

One of defence’s biggest challenges is how to get new blood, new organisations and new, innovative ways of doing things into an organisation. If you’re looking at the same old suspects, big primes—and by big primes, I’m talking about the big defence engineering contractors— we’re not seeing the change we want and we’re missing out on innovative talent.

Defence needs to adopt more innovative commercial approaches and dynamic frameworks. This would allow more experimentation, similar to the GDS ways of working. Smaller, innovative companies could then come in and expose the wider market to the true breadth of the defence ecosystem – which those primes currently hide.

This also requires a significant mindset shift. We need to do more in the discovery space and start talking about alphas, minimum viable products, and running betas—all things we’re used to in many other areas of government.

We just need to accelerate that thinking into not just the back-office of defence, but also into battlefield capabilities. 

Q: What strategic shifts could defence organisations introduce to optimise their approach to digital?

Defence must focus on services, not platforms. Currently, they prioritise large platform procurement in their discussions. Their system is designed to build these platforms. And again, this design, rooted in old manual processes, is part of the problem.

How did you build a tank in the 1950s? You do it like this.

That’s how procurement systems are set up in defence. But that doesn’t work if you want to evergreen an information system. You’ve got to think about roadmaps rather than detailed requirements documents. 

This allows teams to iteratively work to set up a minimum viable product. You’re then at the point where you can deliver something and iteratively deliver all those ‘nice to have’ requirements afterwards. That way we get working software in the hands of the user much more quickly.

We can keep improving that software so they’re not paying for a new platform in 10 years time. By continually improving and updating the platform you get better and better software and a better user experience.

This requires a change to the procurement system and a change to the way defence thinks about projects and programmes. It needs to be much more about service management and incremental upgrades to get something out in the world much quicker. 

In this post I mention the need to digital information systems into a position to properly explore opportunities like AI. If you’re looking for a framework to help you navigate those challenges, download our whitepaper, Laying the groundwork for AI.

The post The future of defence is digital 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.

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

The strategy should be delivery. I’m just not sure it is any longer. 

A GDS revolution

GDS started back in 2011, the same year The Lean Startup was published, the time in which Facebook and Twitter dominated social media. GDS brought the Silicon Valley startup mentality to UK government. Adopting an approach of test-and-learn and getting something useful into the hands of users as quickly as possible.

The first launch of GOV.UK had a big alpha logo slapped on it and looked nothing like any other government service at the time. The GDS Story describes how the team gave the site “a west coast look” to deliberately create something that felt radically different to government services of the time. 

I remember sharing the GOV.UK launch with the leadership team of the business I was with back then. It felt seismic. The government had just leap-frogged all of the private sector clients we were working with. They launched a modern digital service with the intent of testing it with real users, iterating and improving. The code was open sourced and the team behind it blogged about their approach, embracing now common concepts such as regular releases and minimum viable product (MVP).

The innovation in the delivery of UK public digital services since would suggest that this absolutely was the revolution moment we needed. Among a small group of others, the UK is still looked to on an international stage for a progressive approach to digital service delivery in government.

What happened to modern digital delivery?

Since those early days of GOV.UK the principles and approaches that made the delivery of those trailblazing GDS services successful have been documented, trained and incorporated into activities such as the service assessment. Through the Service Manual, an incarnation of those GDS ways of working has been largely adopted as the default way of delivering digital services across government. Digital departments from Whitehall to Sheffield have posters on the wall with GDS-originated slogans such as “show the thing”, and referring back to my personal favourite, “the strategy is delivery”. 

The culture that made GDS so successful in the early days was pioneered by “internet era” digital leaders that were focused on getting useful services into the hands of users. I think some of this important culture has been lost as we’ve tried to distil early success into a manual with defined phases, defined team shapes, and stage-gate assessments. If culture eats strategy for breakfast, we’ve not focused enough on how we really build and foster a culture of modern digital delivery, of continuous improvement and above all – a militant focus on delivering something useful for our users as quickly and as cost effectively as possible.

Back to working services in the hands of users

There are a number of things we need to address to avoid digital becoming the new legacy and to deliver better value digital services for society:

1. Understand how to adapt the discovery, alpha, beta, live phases

Leaders need to build teams that are confident and empowered to adapt and tailor the phases defined in the Service Manual. The by-the-book application of the discovery, alpha, beta to live lifecycle can see teams cranking the handle and focusing on their next assessment without constantly reminding themselves of what they’re really trying to achieve. Highly performing teams should always be questioning how they can adapt and improve how they’re working to reach their destination faster. 

2. Be clear what the destination is

As obvious as it may sound, the whole team needs to be clear on the outcome it is trying to deliver for users. Too often we see teams started with a vague and potentially sprawling problem that can cause endless discovery to try to understand where to even start. Leaders should be clear on the impact they’re expecting the team to deliver, for who, and by when.

3. Right sized and shaped teams

The Service Manual provides a good starting point as to the skills, and I choose the word “skills” over “roles” deliberately, required to deliver digital services through its lifecycle. Teams should be shaped to deliver the specific outcome – and with as few people as possible. Bloated teams create overhead and unnecessary work. Leaders need to develop more T-shaped people who are driven by the desire to deliver working services and are interested in building the breadth of skills to do this. A researcher performing some business analysis, or a delivery manager putting a product hat on should be seen as a positive thing.

4. Deliver outcomes at pace

Teams should behave more like resource constrained startups with a limited time and budget in which to deliver real end-user impact. Leaders should create a culture that values at-pace delivery, where services are expected to be in the hands of users and being iterated within weeks, not months and years.  

5. Back to lean and agile basics

In digital delivery, for me there are 2 things that have really stood the test of time – the agile manifesto and the lean startup. The absolute simplicity of statements such as “working software over comprehensive documentation” and “adapting to change over following a plan” encapsulates so much of what can make digital delivery successful if they’re properly embraced. The lean startup MVP approach remains a compelling guide for reducing waste and iterating to something useful quickly. Rather than training teams in what a beta team should look like, leaders should be coaching their teams to really understand and embrace these core principles.

Let’s make the strategy, delivery – again

The way we’re delivering government digital services needs to change. We can do better. We’ve done it before. We need to put more emphasis on strong digital leadership – real visions for our digital departments and what we want our culture of delivery to be. We need to coach and train our teams not just on the Service Manual processes, but on the now decades old core lean and agile principles. We need our teams to feel dissatisfied until they’ve delivered a working service into the hands of users.

The strategy is delivery. The delivery of working and useful services into the hands of users.

The post Is digital the new legacy? appeared first on Made Tech.

Yet, every six months she must print off reams of documentation and financial statements and present these in person at the local council offices; a trip that leaves her extremely exhausted and frustrated as other government services allow her to provide statements electronically.

Katie is not alone. 

Katie is one of the thousands of people affected by outdated systems and processes for adult social care applications and the regular financial audits that assess how she’s spending her allowance.

A service that is already difficult to access, made more difficult by these processes.

So, what’s stopping councils from using systems that better support people like Katie?

Why are these processes stuck in the past?

Having spoken to several council employees about the process of validating and reviewing documentation in person and the need for paper copies, it would seem that this method has stemmed from concerns about authentication and fraud.

A long-standing wariness about digital systems’ abilities to recognise fraud seems to have cast a shadow over reviewing the options available when it comes to document request and validation software in the modern age. The abilities of systems to review, process and verify documentation based on learned data (such as identifying tampered documents, reviewing signatures and extrapolating data) and to cross-check information with pre-existing databases (such as name, address, previous address history etc) has moved on in leaps and bound in the past decade. In the past year, we’ve seen a huge shakeup to the capabilities of AI – and yet, government systems are relying on functionality that’s, in some cases, decades out of date.

Because of the improvements in request and verification software, these paper-based, in-person verification processes pose a greater security and confidentiality risk; compared to computers and AI, the human eye can more easily be fooled and data protection can be breached due to incorrect storage of paper records.

According to the SSRG International Journal of Computer Science and Engineering, there is clear evidence that AI driven systems are far more efficient at identifying people and fraudulent documents to a greater degree of accuracy than compared to our human abilities. 

Digital databases also offer greater security because private details are visible only to those who need to see them and records can be stored, deleted and redacted as required by data protection policies.

Luckily, in recent years, we’ve seen other government services like the HM Passport Office offering a digital service that’s capable of verifying photos and documents online without human intervention – and yet, we’ve not seen this kind of technology being rolled out across other services.

Why haven’t councils been able to modernise?

I feel sorry for councils who are having to grapple with outdated technological infrastructures from legacy systems and budget constraints. As well as being tied into lengthy contracts with their software suppliers, they often do not have the budget to develop their own secure online submission portals or the resources to rescope and migrate away from what they already have.  

The software package that was (probably) purchased 10+ years ago may well have worked then, but without innovation, expansion or improvement, it’s now failing to keep up with the changing demands of service users and their increasing dependence on instant access, digital portals. 

This is because these systems are often scoped once and deployed. So long as they were good enough at the time of launch, revisiting the platform to see if improvements can be made to the service and functionality is often a low priority.

There is a degree of due diligence on the part of councils though. I’ve noticed that people will question if the system is still fit for purpose if/when renewal of that system is on the horizon. But complicated pricing structure and the hassle of exploring new options often leads people to stick with what they have. I don’t blame them. Having been in their position before, sometimes it feels like the easiest option is to just stick with what we have instead of going into a period of consultation, scoping and procurement. 

A lack of innovation and incorporation of new technologies, though well-intentioned, form barriers for residents and continue to drain social workers’ time – what’s needed is the addition of software and systems that allow social workers and residents to more efficiently supply and assess documentation, and here’s why…

The impact on our community

The issue of accessibility during the application and audit phase undoubtedly has the biggest impact on those who are dependent on the allowance for their independence, freedom and social lives. For those less able-bodied, in-person processes are a difficult hurdle to overcome. 

Two-thirds of adult social care users are over 65 – with all of those dependent on some sort of support for:

• Accommodation
• Physical and mental health
• Transport

You could argue that in these cases, the system fails to accommodate the very people it’s supposed to serve.

We cannot assume that everyone has access to:

• Transport
• A printer or scanner
• A person of good standing in the community
• Identification

Anyone in this position is caught in a catch-22. Needing adult social care support but unable to apply or navigate the system due to inaccessibility to technology, transport and community – the very services they require the funding for.

Take James, for example, he says, “Trying to get help is a minefield, it was like I’m going round in circles. There’s either a lack of services, or when I can find someone who can help the waiting list is already so long. I know other people who need care who have difficulty finding who to approach or where to go for assistance. The information isn’t easily available. It’s not advertised enough and some of the websites aren’t up to date.”

A call for inclusive innovation

There’s no doubt in my mind that reforms to these processes are required. Ideally, there should be options for assessment and verification to be done in person or online to accommodate the diverse needs of those reliant on adult social care payments.

This is why we created our Evidence software.

We believe that by allowing social care workers to automate the processes required to facilitate online financial assessments and direct payments, they will have more time to support residents who need to provide this same information in person. 

With the process of requesting and validating ID, documentation and other eligibility evidence through a digital portal, social care workers’ time can be optimised so they can offer both options to their residents.  

We’ve also done away with complicated pricing structures, add-ons and the like. The kind of thing that often stops social care managers from considering a new product or replacement system.

By providing a choice, councils would be seen to be more inclusive and accessible, putting the choice into the hands of those who deserve to have these options open to them; rather than having the choice made for them. 

If you’d like to learn more about Evidence and its use cases across social care, financial assessments, housing and more, fill out the form below.

Book a demo

During our call, we will:

• understand your challenges and needs
• showcase the features that align with your needs and processes
• discuss how it can help streamline and reduce the cost of your processes
• share upfront and subscription costs

The post Breaking barriers: navigating adult social care in the digital age appeared first on Made Tech.

Okay, but why does the public sector need it?

It’s no secret that the public sector has had years of under-investment in technology. The difficult truth is that legacy systems and outdated technology means organisations are held back from modernising and making public services easier to access and use.

The need for transformation is really pressing and the benefits are potentially huge. From unlocking new opportunities for better services to easing the shift to online services for citizens. Digital transformation can help us to not only deliver new services but cut the costs of maintaining legacy applications. This type of change can also help the public sector rethink how they operate and drive growth agendas with the added flexibility that better digital services bring. Ultimately digital transformation is about creating better digital services for the public.  

How to tell if you’re ready

Looking for some clues that your organisation might need to start their digital transformation journey? Look out for some of the most common challenges across government such as the difficulties citizens can have with accessing public services online, from long waits on phone lines to waiting for important appointments to arrive in the post. 

It’s well established that there’s a significant amount of ‘tech debt’ still in place across government that could also be a clue. We see this in old legacy systems built using custom code that’s maintained by large system integrators. And unfortunately some of these integrators don’t have much interest in helping the public sector modernise.. 

Anything to be aware of?

We’re not saying a transformation journey comes without its challenges. While they may be complex to solve, they’re simple to understand. Some of the most common blockers we see are:

Underfunding: One of the biggest reasons the public sector finds itself struggling with transformation is underfunding. A commitment to better funding will help free up organisations to be flexible, innovate and provide services that the public can access the way they want to.

Leadership: Leaders need the vision to support transformational change in their organisations. It’s worth noting here that not every organisation will see this as a challenge, but it is something we see often. 

Cultural resistance: Building a culture of change can be tricky, particularly when working with organisations that have a ‘that’s the way we’ve always done it’ mentality. But by focusing on capabilities, skills and behaviours we can foster a sense of ownership in shaping a future organisation. 

Technology challenges: With legacy technology and data silos that have built up over the years, leaders can find at first they don’t know the scale of the challenge. And unfortunately that means it’s often easier to renew that existing contract with those large system integrators we mentioned earlier. 

Where to start: The first step is always the most intimidating. When support costs are spiralling, efficiency cuts are biting, staff have low morale and are voting with their feet; services are suffering. It can be really daunting for leaders to work out where to start. 

And what about pitfalls?

Transformation programmes can be complex and very expensive. The scale of what we need to do can be enormous and more significant than you might first think. It’s important to remember that overrunning on cost and timescales is common across many large public sector transformations. 

But there are things we can do to mitigate this, for example setting out a clear vision for what needs to be achieved is essential. Team this with breaking the project down into manageable steps gives us space to ‘fail fast’, learn and recover quickly. 

Huge monolith programmes should be consigned to history. We need to break the cycle and replace these systems with modern open source online services that can be easily and cheaply updated when needed. But it’s not only that, adopting an agile delivery capability combined with a sense of the realities of accountability, associated assurance and approvals for spending public funds is essential.

How is it different from legacy application transformation?

We’ve been talking about legacy application transformation for a while, but digital transformation differs. Legacy in applications or infrastructure has been around for a long time, but digital transformation incorporates both of these as part of a wider piece of work. 

With digital transformation we make sure to give support where a team truly needs it. Whether that’s across the whole journey from a strategy POV or assisting with particular parts of the transformation, for example the shift from on prem to Cloud, organisation redesign or data migration. In short, it’s not all or nothing. 

The future of digital transformation

The future for digital transformation is bright! Today, the advantages for transforming public services are much better understood. The level of collaboration between departments and the supply chain is better than ever and we’re seeing a real commitment to tackle the issues, share learnings and build on real change in the system.

Understanding where to start is the first hurdle. Bringing specialists on board can help take away some of that apprehension. Our team have deep sector knowledge, many having been long-term civil and public servants often on the receiving end of poor delivery. With a skill set on setting the strategy and platform and delivery for a business-led, technology-enabled transformational change we can help set you up for success. 

But first things first, we’re happy to chat and help you understand where to start.

If you want to know more, you can hear us talk about digital transformation and the public sector at Digital Leaders on the 14 November. Find out more and get your tickets today.

The post Digital transformation 101 appeared first on Made Tech.