Government technology: the past, present and future

The main topic at DigiGov Expo 2026 had everyone talking: how do we successfully implement rapidly emerging technologies to create positive outcomes for citizens?

With future technologies emerging at such a fast pace, everyone was reflecting on how technology was implemented in the past and how much of the present landscape is set up for the next wave of tech.

The answer seems obvious, but the ‘how’ remains. How do we ensure outcome-focused projects and people get the data foundations right and deliver collaboratively, transparently and iteratively through modern technology for the benefit of citizens?

Never start with the technology

In the past, focusing on technology rather than outcomes has been one of the biggest issues. It is important to remember that policy creates intent, technology creates capability, but neither automatically guarantee positive outcomes.

Speakers said government’s AI Playbook has got it right, stating that AI is a means, not an objective. The problem you are trying to solve should always be the starting point and outcome-based thinking needs to be the golden thread that runs through the entire service. Teams are seeing benefits in moving from big transformation programmes to a product operating model, with clear ownership of a product, where technology choices are focused on the outcome.

You must think about an AI solution in six ways:

What is the problem?
Is AI applicable?
Task-level efficiency
Organisational change
Service improvement
Public value

Speakers also brought up digital transformation versus modernisation, or the ‘how’ versus the ‘what’. There is a difference between modernising technology and redefining service delivery models using technology for better outcomes.

The goal is to bring policy, frontline and digital resources together around the development of a product that focuses on the outcome for citizens. By empowering teams to take charge of these outcome-based projects, being rigorous about the outcome measurements, and always thinking about the end user, the problems become easier to solve using technology.

Transformation starts and ends with people

Government is on a culture change and transformation journey, not simply a technology journey. Culture and skills, rather than technology, will decide whether change succeeds. Panels kept returning to one question: how do you engage the workforce to drive their own change? The cultural shift needs to ensure empowered individuals are brought in together, all focusing on a particular goal. Resilience is also key. If tech fails in the future, staff will still need to have the knowledge to intervene manually.

There are often fears over how AI will impact roles. Allowing staff to be the architects of AI transformation puts them back in control. Whether individuals have never used rapidly-emerging technology or are innovators, how do you bring everyone on the technology journey in the correct way?

The answer throughout panel sessions: everyone should be a digital leader and think about how they can use digital to really transform operations. The focus for leaders should be becoming an early adopter, innovating, and embedding a learning culture built on citizen outcomes.

Trust and transparency

Panellists all agreed that technology is not usually the challenge. The challenge is trust and interoperability, getting organisations to trust the ecosystem where they share, consume, and provide data. Trusted, secure data is important as a foundational layer.

There is also a lack of public trust in technology and AI, and in the workforce building technology solutions. DigiGov speakers saw co-production as the solution to building public trust. They championed involving stakeholders wherever possible, being open and honest about limitations, testing rigorously, and making sure people are brought along at all stages of the journey. Government teams need to set standards around data, AI and new technology to ensure trust is earned, focused around the UK’s five published AI regulatory principles: safety, security and robustness; transparency and explainability; fairness; accountability and governance; contestability and redress.

Psychological safety is important for staff. If you give them the ability to be transparent when tech fails, teams can build and learn from these failures to ensure future success. This is often hard in government organisations centred around public money, as staff are pinned to time, cost, and performance.

It is therefore important to remember that communication is part of successful delivery. Building trust by being open and transparent with external and internal stakeholders leads to more successfully implemented technologies.

Data foundations: quality, ownership and sharing

Quality data is the precondition for everything. Speakers urged that data is fundamental to innovation, and bad data equals bad AI. Having the ability to spot poor data is important, but so too is thinking about data needs and requirements in the legislation from the beginning, as often this is thought about too far into a project.

After years of locking data down, asking staff to maximise its value is a major mindset shift. Speakers mentioned the need for better data sharing across the whole system and consistency in recording data. Data has been collected in many different ways that do not tie together, leading to different answers and results when analysing, and creating the lack of trust mentioned above. For any data project:

understand WHY you are collecting data.
only capture data once.
use the data many times.
ensure the data architecture is in place and make it discoverable and easier to consume across government.
ensure the data is maintained correctly after use.

Government organisations need to have one source of truth for technology, understand how to use and trace the data, the quality of the data, and how much it can be trusted. Government is still designing datasets without considering all uses, values, and potential connections. What teams are missing is design, both of datasets and of their future use.

Architecture, legacy and avoiding lock-in

Government is already well aware of the accumulated risk of technical debt, the cost of legacy systems and the failure to invest in updating and changing them. This is made even harder with AI in the mix. The further along the AI journey, the harder it will be to untangle.

Speakers argued for addressing legacy technology whilst building the foundations, reimagining and rebuilding services with natural language processing and AI in mind. Government organisations should not push the boundaries and progress with AI and leading-edge technologies until the old systems are rebuilt.

The Lego brick architecture concept is so important here. Rather than building monolithic systems and technologies, panellists discussed using building blocks to ensure a cohesive whole-system approach where individual parts can be easily maintained and replaced when necessary without reinventing the whole. However, insufficient integration between existing systems and infrastructure can hold back digital transformation.

A lack of ownership of enterprise architecture was also a hot topic. This, plus teams often ‘sleepwalking’ into vendor lock-in, are causing future legacy issues. Staff must ensure they are informed, aware customers, knowing the language, what to avoid, and how to stay in control.

Governance, risk and honest failure

To date, there have been many hard-won lessons in technology during complex government transformation. One speaker stated that, across all industries and countries, 70% of digital transformations fail to hit expected benefits in the business case and a quarter fail to deliver even half.

Every day, staff are making the decisions to initiate a new programme of work for digital products, or underpinning existing services with more digital technologies. But who takes accountability, how much risk is allowed, and is it okay to fail?

Panellists discussed failing fast, trying different technologies and tactics until success, so you end up with more value. Yet, in the public sector, every penny counts and risks are magnified. There must be a balance, enabling innovation at the edge, but ensuring the foundations enable safe change and delivery of new technological solutions.

Failure of innovation is, therefore, acceptable if you learn from it. Knowing when to stop or shift is important, as is giving the freedom to do things at pace, with the right guardrails. However, risks should always be paired with safe governance and technology foundations.

Collaboration across boundaries

The hardest public service problems cannot be solved by one team, profession or organisation. Government needs to ensure collective ownership of solutions, outcomes and technology. Everyone can see when change needs to happen, but all stakeholders view the solution through a different lens. These can all be legitimate perspectives, but without a shared view of the outcome and the needed transformation, change programmes will fail.

Speakers discussed how the proposed (currently paused) changes for local authorities will take years to achieve. In the meantime, collaboration across organisations and teams will be key. There is an opportunity to learn and build from each other during change, merging systems, structures and bringing people together. Government devolution of power and decision-making, moving into communities, also creates opportunity for collaboration across the public sector.

There is a desire for people to work as a collective to solve government’s biggest problems. As tech is moving at a rapid pace, reusing good ideas and work can help staff implement new technology and processes.

It is also important to bring suppliers together with government to learn from both sides, as suppliers have a key role in the matrix of the public sector. Government can outsource technology and projects, but cannot outsource internal understanding and capabilities. Suppliers must therefore build this understanding during their contracts from the beginning, retaining internal technological knowledge to build on future solutions.

AI: realistic expectations and retained capability

Digital, data and technology should be at the centre of government’s future decision-making.

Speakers argued that the benefit of AI is still catching up with the rhetoric, stating that it has been a year and a half since the AI Opportunities Action Plan, but how much of the productivity that government predicted has actually happened? Where has the investment gone? What has been overlooked? Are the gains worth the costs?

The pace of technology, especially AI, appears to be moving faster than individuals’ understanding of requirements and ability to thoroughly vet new systems. Organisations must think about what is underneath the AI, how it is being assured, and what happens if you get the wrong outcome. Panellists also discussed the supply chain around AI not just being digital services, often requiring a look at the entire infrastructure chain.

Modern technology is always discussed in relation to efficiency gains, time and cost savings, but what benefits do these bring and who benefits from them? This needs to equate to additional public value, such as shorter wait times, or serving more citizens. In summary, you should always bring this back to the outcomes.

Throughout the two days at DigiGov, one message rang loud and clear: digital transformation succeeds or fails because of people, trust and data foundations rather than technology. Start with the outcome, keep it simple, own your architecture, be honest when things go wrong, and remember that no individual, team or department can do it alone.

RedRock’s consulting services can help you tackle your most complex challenges through clearly-defined problems and outcomes, across people, process and technology.

Molly Sheldrake Content Manager