Public-Service Reform in the Age of AI

Contributors: Oliver Large, Ryan Wain

Progressive politics is at its strongest when it knows not only what it wants to change, but why and how; when it offers a coherent theory of change and reform, not simply a list of policies.

Britain has a long and proud tradition of purposeful renewal in public services. From Attlee’s creation of the welfare state in the post-war period through Thatcher’s introduction of market discipline to New Labour’s focus on equity and performance, each generation responded to crisis with a clear organising principle for reform.

The Blair government had such a theory. Its approach to public-service reform sparked fierce debates, but it was not accidental or episodic. It rested on a consistent set of principles that were applied across departments and adapted to circumstances. Whether in education, employment, health, housing or transport, the central intent was to offer high standards for all, a degree of choice and meaningful citizen engagement, and social policies geared to personal fulfilment and future economic prosperity.

None of this was predicated on dogma or ideological ends in themselves, but it was a necessary pathway to achieving social justice, addressing the world of modernity and supporting people through rapid change.

A shared formula – choice and competition can drive quality – was the route to increasing equity. It pushed poor providers to raise performance and improve efficiency and responsiveness, animating reform efforts and underpinning a strategic plan that delivered the biggest transformation in outcomes in a generation.

Today, that coherence has been lost. Progressive politics is marked instead by fragmentation: different sectors pursuing different reform instincts, often at odds with each other. There is no lack of commitment to public services, but in the absence of a shared theory of reform, rapid, visible, durable progress is absent too.

Renewing progressive politics demands clarity and coherence. But the conversation must go beyond relitigating old debates. The world has changed. The tools available to government have changed, too. Where choice and competition once drove improvement, now data, digital technology and artificial intelligence offer the greatest potential for reform. Used well and in the service of a common goal, they can enable a new model of public services, grounded in modern methods of public engagement and, where possible, citizens’ participation in decision-making. This new model can be transformative for the state, delivering services that are personalised, not standardised, preventative, not reactive, and always on rather than bound by labour alone.

The parallel is important. Just as choice and competition drove higher quality and better performance, data-driven and AI-enabled systems can support the delivery of better outcomes. They allow the state to identify need earlier, tailor support and intervene before problems become crises so it can finally achieve, directly and at scale, what earlier reforms pursued indirectly. Where competition disciplined poor performance, AI-enabled systems can analyse and predict performance in real time, allowing providers to course-correct quickly. Where choice depended on citizens having the information to navigate complex systems, technology can now adapt systems around citizens by default. This transformation requires deep and radical reform, not just adding technology to existing struggling services.

Without a clear view of how this can be accomplished, citizens’ expectations will never be met and their trust will be lost, with progress stalled and fairness foregone. Governments will end up paying a very high price – financially and socially – for failure.

And, in the end, effective delivery of services depends not just on systems and structures but on people who care. Someone’s brother, sister or mother is responsible for caring enough to challenge bureaucracy and to go the extra mile. That is why giving people the skills to meet the challenges of tomorrow is vital – as is ownership of the task of improvement, so that everyone can ask themselves: “Did I do a good job for someone else today?”

A renewed theory of reform is essential. But it must be a modern one. This paper makes the case for just such a modern theory of progressive reform – one that rises to the challenges of our time, matches today’s possibilities to enduring political values and can guide government action across the whole of the state.

Lord Reid of Cardowan and Lord Blunkett

Despite record spending, people in Britain – as in other developed economies – are increasingly frustrated with public services. Long waits, overstretched staff and worsening outcomes have become the norm. Fiscal pressure is mounting, and trust in government is plummeting. It may be tempting to blame shocks dating back to the 2008 financial crisis, but the problem is not one of funding. It is one of vision. A new agenda of reform is needed to build personalised, preventative, always-on services.

Previous reform efforts, however different to each other, had a common foundation of political leadership: a deep analysis of what had gone wrong, clear answers on how to correct it and the will to act on those insights. Clement Attlee’s founding of a welfare state based on fairness through universal access, Margaret Thatcher’s pursuit of efficiency through market discipline, Tony Blair’s focus on equity and performance were each based on a well-articulated theory of change, giving coherence to the reforms they drove.

The conditions of today demand a new reforming agenda and zeal to match the best efforts of previous generations – perhaps even exceed them in boldness. But not since New Labour’s modernisation in the 2000s has a government pursued a coherent theory of change.

In the absence of a core vision, piecemeal promises, headline hiring pledges, pilots and patches can, at best, tackle symptoms, but they do not fix the underlying structural flaws. Government after government has settled for managed decline instead of disruptive but necessary renewal.

This is not a matter of abstract theory or lofty philosophy. Public services that work matter deeply to people – their prospects, their safety, their quality of life. Without ambition and coherence, improvements will be marginal, pressures will continue to build and disillusionment will grow. Without reform, governments face spiralling costs, failing systems and collapsing trust. With it, Britain can pioneer, once again, a new model of governance which restores legitimacy and proves that a state that delivers is an unstoppable force for good.

Only a reform agenda that radically changes the government’s ability to deliver, embracing the transformative potential of technology to overhaul the model itself, can meet the demands of this moment. At the Tony Blair Institute for Global Change (TBI), we call this agenda the Reimagined State.

Today, vast volumes of data coupled with artificial-intelligence capabilities make a fundamentally different kind of governance possible. Capacity can expand without necessarily expanding labour. Services can include an “always-on” layer, available around the clock. Personalisation by default and at scale becomes possible, replacing standardised provision with services that are tailored to each individual and learn continuously from every interaction. Providers can spot risks before they escalate, replacing today’s remedial model of costly intervention at the point of crisis with proactive prevention. In place of rigid command from the centre, professionals can be empowered at the front line with relevant real-time data and intelligent tools, restoring discretion and autonomy while unlocking innovation.

This reform agenda forms the blueprint of a data-driven state: always on, personalised, preventative. It uses intelligence rather than labour alone to deliver results. It learns in real time, adapts as conditions change and delivers better outcomes at lower cost. It delivers fiscal sustainability with higher productivity, expands capacity without expanding labour costs, shifts resources upstream to prevention and embeds continuous improvement into daily delivery.

To make this renewal real, public services need a new operating model built on three foundations. Universal digital ID can seamlessly and securely link every citizen to their entitlements and give them control over their collated data, embedding transparency and consent. Modular public-service platforms, such as a national health platform evolved from the NHS App or a national education platform for truly personalised learning, can give frontline staff smarter tools to work with and allow citizens to navigate services as one coherent system. And a real-time system-intelligence layer can transform millions of interactions into actionable insights for early intervention, continuous improvement and a return to genuine accountability.

Together, these three layers would create the architecture for a new kind of public services. But new infrastructure must be matched by new forms of governance.

In the AI era, government no longer needs to rely on command-and-control management or retrospective inspections. Instead, it must govern by “signal and steer”, using live data to set thresholds, maintain fairness and support continuous improvement. Funding too must evolve: locked institutional budgets should give way to modular, outcome-based allocations that flow to tools and interventions shown to work. The workforce must be re-empowered as innovators, freed from compliance for compliance’s sake, given the skills and autonomy to drive and own improvement through new methods at the front line, and incentivised to contribute to the system’s collective learning loop. Citizens themselves should not be passive recipients of services but co-creators, shaping their service journeys, giving real-time feedback and controlling their data.

This paper sets out how to achieve this transformative reform with a playbook built on four principles:

1. Use AI as a multiplier, to make services scalable and always accessible without simply adding labour.

2. Rewire services around the citizen, through digital ID and data consent.

3. Govern with foresight, using real-time intelligence to act preventatively rather than remedially.

4. Foster public innovation, enabling the workforce to drive continuous improvement from the bottom up.

This is not about bolting new technology onto crumbling foundations. It is about replacing those foundations altogether, creating public services that are always on, personalised by default, preventative rather than reactive and powered by real-time data. Britain was among the pioneers of the 20th-century public-services model defined by universal access. The 21st-century model will be defined by mass customisation – an adaptive state that learns, protects and improves every day.

Here, too, our nation can show the way. This new blueprint can become core to a newly resurgent Britain – a country restored in capability, confident in its future and able to show that effective, empowering, citizen-centred government is possible in the age of AI. Reforming public services is not one policy area among many. It is the foundation of a renewed social contract and the clearest test of whether Britain can escape decline and build a better future.

Public services are running on fumes. Across health, education, welfare and justice, citizens face the same frustrations: long waits, rigid rules, overstretched staff and outcomes that fall short despite record spending. People are paying more yet receiving less, even as their expectations are raised by seamless private-sector experiences. The result is a widening gap between what citizens expect and what government delivers – eroding trust in the state itself.

The root problem is structural. Public services are trapped in an outdated operating model that is labour-intensive, standardised, centralised and reactive. Scaling capacity means scaling staff, standardisation suppresses diverse needs and support typically arrives only after crisis has been reached. Investment has not translated into better outcomes because the model itself is flawed.

This mounting structural strain should have been obvious to anyone paying attention and has been evident in the repeated failures across public services – from prisons and courts cycling through emergency measures to the NHS’s annual winter breakdowns and the recurrent capacity collapses in councils, social care and the asylum system. But recent governments have lacked a coherent agenda for reform.

This absence of direction is striking when set against Britain’s longstanding tradition of renewal. Every generation of leaders has faced its own period of public-service failure and responded with a distinct governing principle.

Clement Attlee’s government confronted the social and economic devastation caused by the second world war and the moral bankruptcy of interwar neglect. It made universality the organising logic of the state, building provision at scale to guarantee a basic level of security for all citizens. Embracing the idea that the government’s overarching objective must be to beat back the “five giants” of the Beveridge Report – want, disease, ignorance, squalor and idleness – it established the National Health Service, expanded education, introduced social insurance and embarked on a vast programme of public housing. The guiding conviction was that no citizen should fall below a minimum standard of health, income, shelter or education, and that this kind of security was essential to a stable and cohesive society. Attlee’s government also set the parameters of the public-service operating model according to the abilities of the state at the time: a labour-intensive bureaucracy coordinating provision through central planning and mass employment.

Margaret Thatcher’s government confronted a different kind of crisis: the stagnation and inefficiency of the 1970s, when inflation, industrial unrest and fiscal strain had eroded faith in the state’s ability to deliver. To the logic of universality, she added competition and discipline, arguing that public services should mirror the incentives and accountability of the market. Through privatisation, deregulation and the weakening of producer power, her government sought to restore efficiency and individual responsibility. Its defining characteristic was a belief that the state should do less but do it better, creating space for enterprise, innovation and self-reliance to flourish.

Tony Blair’s government inherited creaking public services and a crisis of confidence in state performance after years of underinvestment. It sought renewal through modernisation – preserving the public ethos of Attlee’s post-war model while harnessing the mechanisms of market reforms. Competition was repurposed to drive standards rather than to shrink the state, and choice was framed as a route to fairness. Its defining characteristic was a belief that public services could be simultaneously universal in access and personal in experience, using targets, performance management and partnership to reconcile equity with efficiency.

What unites these different visions is that each brought a new level of coherence and direction to necessary reform. They were not incremental management tweaks but organising ideas rooted in a clear diagnosis of what had gone wrong and a leader’s conviction about how to put it right. Each vision reflected its time and its distinct challenges.

Today’s challenge may be the biggest of them all. The old operating model is breaking down under the weight of demand, complexity and patterns of need it was never built to meet. Just as the Victorians reconfigured the machinery of government in response to the Industrial Revolution, government must now adopt a long-term agenda to redesign how it operates for the digital and AI era – a framework TBI describes as the Reimagined State.

Transforming the state in this way requires a fundamentally different approach to reform: one that uses digital infrastructure, real-time data and artificial intelligence to overcome the structural limits of the old operating model. These tools make an entirely different model of service delivery possible. Instead of being bound by labour constraints, services can be always on. Instead of offering one-size-fits-all provision, they can be personalised by default. Instead of waiting for crises, they can act proactively and preventatively. Instead of relying on centralised decision-making, frontline workers can be empowered with live data and intelligent tools.

In this paper, we set out a reform agenda for the AI era: a new operating model and governing framework that can turn public services into self-improving systems – learning continuously, adapting in real time and delivering better outcomes with greater efficiency. Building on previous TBI work – including Governing in the Age of AI: A New Model to Transform the State and our recommendations of AI-era reform across the NHS, DWP, local government and the Spending Review – this paper consolidates that thinking into a single, system-wide reform playbook fit for the AI era.^{[_],[_],[_],[_]}

By any measure, the performance of public services in the UK has deteriorated over the past decade. According to the Institute for Government’s Public Services Performance Tracker, almost every service – with the exception of schools – is performing worse than in 2010.^[_]

For millions, public services now feel more like a bottleneck than a safety net, as they find themselves stuck in a queue that barely moves – whether that is waiting hours in Accident and Emergency, months for treatment or years for justice (see Figure 1).

The queues, delays and frustrations that millions experience are symptoms of something deeper: an operating model that is no longer fit for purpose. By operating model we mean the structures and processes that govern how services are funded, organised and delivered. The model determines who makes decisions, how staff spend their time and how resources are turned into outcomes. In its current form it leads to backlogs, fails to deliver productivity gains, drains public finances and erodes trust.

First, the model struggles to convert more resources into better outcomes. Between 2019 and 2024, inputs to UK public services rose by almost 25 per cent but output increased only 14 per cent.^[_] NHS productivity illustrates this: since 2013–14, resource use has grown 44 per cent, while activity rose only 35 per cent.^[_] Even with more nurses and consultants since 2019, elective admissions increased less than 10 per cent. Waiting lists are longer and targets are consistently missed. Overall, public-sector productivity is 3 per cent below pre-pandemic levels.^[_]

Second, it consumes ever greater sums of money. The tax burden is near its post-war peak, yet public-service deficits persist. NHS spending has grown 3.2 per cent annually in real terms since 2014–15. In 2023–24, NHS systems recorded a £1.4 billion deficit despite Treasury support. More money is being spent, with less return.

Third, it erodes public trust. Satisfaction levels are at historic lows: only 21 per cent are satisfied with the NHS, 40 per cent with GPs and 23 per cent with the police.^[_],[_] People feel they pay more and receive less, creating a widening credibility gap that strains the social contract.

Covid-19 did not cause this failure; it exposed it. Services have struggled to recover, and many remain worse than pre-pandemic. The model is simply outdated.

Services Are Running on an Outdated Model

The operating model of public services was built first and foremost to deliver a historic breakthrough: universal access. In the mid-20th century, governments transformed provision that had been patchy and voluntary into a system that was national, reliable and fair. Every child was entitled to schooling through a common curriculum, every patient to health care, free at the point of use. Mass provision became the foundation of social equity.

But this achievement was shaped by the constraints of its time. The operating model was built around:

• Labour intensity: Public services relied on large, affordable workforces delivering face-to-face interactions – consultations, lessons, home visits. Scaling access meant hiring more people.

• Standardisation: Services were designed for the “average” citizen. In education, that meant a uniform curriculum in age-segmented classrooms; in health, standard pathways rationed by professional gatekeeping. Individual needs were often overlooked, leaving many to experience the system as unresponsive or even exclusionary.

• Reactive demand management: Services intervened only after problems were visible. The NHS centred on hospitals treating illness rather than preventing it. Over time, this reactive mindset became hard-wired, with funding channelled into acute crises rather than prevention.

• Slow information flows and weak feedback loops: With paper records and delayed data, the centre lacked real-time visibility of service performance. Alignment across a large system could only be achieved by centralising control – pulling decisions upwards and suppressing local autonomy. The result was a model that prioritised uniformity over responsiveness.

Productivity in labour-intensive sectors cannot rise easily: a nurse can still only treat one patient at a time and a teacher can only attend to so many pupils. As wages rise to remain competitive with more productive sectors, service delivery costs increase without matching output – a dynamic known as Baumol’s cost disease.^[_]

This is why the record spending and staff growth today have not translated into better outcomes. A labour-intensive model drives costs up while productivity flatlines, leaving citizens paying more for less – and losing faith in the system itself. Recent public-sector pay crises underscore this reality. The government has faced immediate pressure to settle disputes simply to keep essential services operational.

The fiscal pressures facing public services are therefore structural, not cyclical. Baumol’s cost disease means that labour-intensive services inevitably become more expensive over time. As The Productivity Institute highlights, governments face a stark choice under this model: either spend ever more simply to maintain existing levels of provision, or rely on periodic efficiency drives that temporarily contain costs but ultimately erode capability, service quality and workforce morale.^[_] Escaping this trap requires changing the operating logic altogether, rather than pushing the existing one harder.

The Institute for Government identifies chronic underinvestment, rising and more complex demand, and workforce attrition as common pressures acting across services today.^[_] Our argument is that these are not external shocks, but the predictable consequences of a system still trapped in the constraints of another age.

Underinvestment stems from the operating model’s failure to use resources efficiently: because it is labour-intensive and geared towards meeting immediate pressures, funding is consumed by day-to-day firefighting. Rising complexity of demand clashes with a model built for mass, standardised provision, which struggles to respond to diverse needs or provide personalised support. And the workforce, still treated as the primary engine of delivery, is stretched and burned out; it is expected to do more with outdated tools, rigid processes and growing caseloads. What look like external pressures are in fact symptoms of an outdated system.

Any serious public-service reform agenda must begin from this fact, because without replacing the model itself, no amount of additional funding, better management or piecemeal reform will reverse the decline.

The Private Sector Is Redefining Service Expectations

While public services remain locked in labour-intensive, standardised models, private-sector services are breaking free from these constraints. By embedding AI technologies into their operations, they have unlocked new capacity, raised productivity and transformed the customer experience. The lesson is clear: services are not condemned to stagnation. With the right operating model, technology can be harnessed to do more, better, for less.

Fast-food chains illustrate the shift. After decades of flat productivity, the sector saw sharp gains after 2020: sales per employee rose around 15 per cent, driven not by cutting staff but by augmenting them.^[_] Mobile apps, online ordering and AI-driven demand forecasting streamlined transactions, reduced queues and enabled staff to serve more customers in the same time.

Other sectors illustrate how AI has built intelligence into everyday services. Navigation apps such as Google Maps and Waze use machine learning to turn billions of individual data points into a live map of the system, so that each journey updates the picture for everyone else. Streaming platforms such as Spotify or Netflix rely on recommendation algorithms trained on vast interaction data sets to personalise content, so every user receives tailored suggestions while the system as a whole keeps improving.

Amazon and Uber use real-time optimisation algorithms to dynamically allocate resources – whether that means routing stock through warehouses or matching drivers to riders in peak hours. Tesla vehicles continuously generate telemetry that feeds into AI models for autonomous driving, so the fleet learns collectively from every mile driven. Consumer-health ecosystems such as Apple Health or Fitbit depend on AI signal extraction: raw biometric data is filtered and interpreted into alerts or insights, shared in a privacy-preserving way that still adds value across populations.

These technologies not only lift output per worker but also create a more personalised, responsive service environment.

As a result, citizens’ expectations have shifted. People now take for granted speed, convenience and personalisation – same-day delivery, instant banking alerts and tailored streaming recommendations, for example. Over 70 per cent expect personalised interactions and feel frustration when they do not get them.^[_] In that context, waiting weeks for a benefit decision or hours in an A&E queue feels not just inefficient but unacceptable.

The technologies that have transformed private-sector services are readily available to the public sector. AI, digital platforms and real-time data pipelines could help frontline staff work more effectively, personalise support, anticipate problems before they become crises and learn continuously from every interaction. The barrier is not capability but an operating model designed for another era – labour-intensive, standardised and reactive. Unless that model is reformed, public services will continue to fall further behind the standards that citizens already take for granted in the rest of their lives.

There Is No Public-Service Reform Playbook

Despite the scale of today’s challenges, Britain has no coherent playbook for reform. Health defaults to centralised performance management; targets and league tables meant to jolt the NHS into improvement sit alongside ambitions set out in the 10 Year Health Plan for England to shift care from hospitals to communities, move services from analogue to digital and reorient the system from sickness to prevention. Education, by contrast, is shaped by instinctive conservatism, with government wary of disrupting a system that has raised attainment but is now sustained by exhausted staff and has lacked a defined path to improvement since academy status became near universal. These contrasts expose divergent theories of change and the absence of a unifying philosophy of reform.

The last time Britain had a unifying framework for public-service reform was under New Labour’s modernisation agenda, which took shape in the Blair government’s second term. Faced with persistent underperformance in public services, the government developed a reform model built around four mutually reinforcing pillars:

1. Performance management: This meant clear national standards, measurable targets, rigorous inspection and corrective action for failing providers, underpinned by the Prime Minister’s Delivery Unit and Public Service Agreements. High-performing organisations earned greater autonomy, while corrective action was taken for failing providers.

2. User choice: Families and patients were given more say in education and health care, challenging the producer-led model of the post-war settlement.

3. Competition: Funding followed users, creating quasi-markets in health and education to incentivise efficiency and innovation.

4. Diversity of supply: Charities, social enterprises and private providers were brought in to expand capacity of state monopolies.

This model could be applied across sectors to raise standards systematically. It worked: outcomes improved and satisfaction rose as delivery became the focus of government.

That coherence, however, was lost after the 2008 financial crisis. Fiscal retrenchment displaced reform zeal, and the architecture that had bound the reform agenda together was dismantled. The Prime Minister’s Delivery Unit was downgraded and eventually replaced by successor units, and Public Service Agreements were scrapped. Performance management became more fragmented and reactive, with successive governments cycling through delivery units, dashboard metrics and mission statements – none of which replicated the grip or coherence of the Blair-era model.

Other elements of the playbook survived in places, but no longer as part of a unified framework. In health, the Health and Social Care Act 2012 entrenched provider competition, only for it to be reversed a decade later in favour of integration. In education, the logic of earned autonomy was lost as academy status became near-universal. By the 2020s, the way in which improvement was sought across services had become incoherent: different sectors were governed by different logics, with no cross-government alignment.

Yet reviving the playbook wholesale would not work. Its core mechanisms were designed to raise performance inside the legacy operating model of the late 20th century. They could make that model work better for a time, but they could not change the model itself. The task today is not to restore the old architecture but to replace it.

In the absence of such a replacement, government has reached for alternatives. Programmes such as the £100 million Test, Learn and Grow initiative are deploying multidisciplinary innovation squads into communities to co-design services with councils, frontline staff and residents.^[_] The government’s Blueprint for Modern Digital Government sets out a six-point plan to join up services, strengthen data infrastructure and accelerate AI adoption. Meanwhile, the ten-year NHS plan acknowledges the broken operating model and aims to rewire the health service around prevention, digital tools and patient empowerment, while bringing back performance-management elements from the New Labour era.

The test-and-learn ethos brings welcome agility, but its strength in piloting small-scale innovations becomes a weakness when there are no pathways to scale, diffuse lessons or embed change in the system’s operating logic. The Blueprint for Modern Digital Government recognises the urgency of tackling the state’s digital deficit, but it is fundamentally a technology strategy rather than a reform playbook: it outlines how to modernise infrastructure without reimagining the institutional architecture of public-service delivery.

As TBI has argued, the NHS plan has the right vision for transforming health care – a genuine attempt to disrupt from within through digital tools and prevention – but it has been criticised for lacking a credible delivery plan.^[_],[_] Its ambition is confined to health.

Each alternative points in the right direction, but none offers a coherent vision for how to update the outdated operating model across public services so that they can deliver consistently, equitably and sustainably at the scale modern societies require.

Britain is stuck between nostalgia for past reform models and substitutes that cannot scale. The challenge of our time is not to squeeze marginal gains out of a legacy model but to replace the operating logic of the state itself. The task is to build a genuine playbook for the AI era – one that rewires public services to be adaptive, coherent and capable of continuous improvement across the whole system.

The structural failures of the UK’s current public-service model are no longer in doubt. The question is not whether to reform, but how. Asking the system to do more, with less, inside an architecture that resists scale, learning and personalisation will not work.

AI, together with wider digital infrastructure, offers a way to break free from the constraints that have so far shaped public services.

Crucially, this is not just about automating existing processes or bolting AI onto outdated workflows. Using AI to speed up paperwork in the NHS or add a chatbot to Universal Credit still leaves the old operating logic intact. Instead, the true potential lies in using AI to replace the legacy operating model altogether – shifting from optimisation of the old to the creation of something fundamentally new.

AI enables a different logic of delivery: one that is scalable without proportional labour, personalised by default, proactive rather than reactive, and empowering for both citizens and professionals. In place of the legacy model, AI makes it possible to design a new operating model through these four core transformations.

Always on: From Constrained by Labour to Scaling With Demand

In the current model of public-service delivery, professional time is the ultimate bottleneck. A GP can only see a fixed number of patients in a day. Scale has therefore meant hiring more staff, an approach which is increasingly unsustainable under fiscal pressure.

AI enables a different path: augmenting professionals to both expand capacity and provide a layer of round-the-clock access. This allows existing staff to do more without increasing their workload, raising productivity, and enables service users to receive some forms of support at a time that works for them.

AI systems can help match resources to demand in near real time, as intelligent triage systems prioritise cases so that scarce professional time is directed where it has the greatest impact. Automated case-handling and documentation free staff from the administrative grind that often consumes hours of their day. Smart scheduling matches demand to availability in real time, ensuring that appointments or visits are allocated with far greater precision.

The result is a shift from labour scarcity to cognitive abundance. AI acts as a digital multiplier, taking on the repetitive, the predictable and the informational, so that professionals can focus on what only humans can do: exercising judgement, building trust, offering care and making ethical decisions. A GP supported by AI-powered triage can devote more attention to the most clinically complex patients. A probation officer aided by predictive analytics can monitor larger caseloads without losing sight of emerging risks. A teacher working alongside an AI tutor can deliver personalised reinforcement to every learner, without adding to their own workload.

The impact of AI extends beyond augmenting professionals to enhancing services themselves. Once intelligence is embedded directly into delivery systems, services can operate continuously rather than intermittently – becoming, in effect, “always on”. Citizens would no longer need to wait for office hours or appointments, or navigate queues to get help. AI-enabled systems, including in the near future agentic systems, can answer questions, guide users through applications, offer tailored learning support or provide initial health advice around the clock.^[_] These systems supplement professional time rather than compete with it, extending the reach of services in ways that were once unthinkable. Embedding AI would not mean that every aspect of provision is accessible around the clock, but would add a layer of access that takes the strain off the system and provides immediate support where it is needed.

This shift is already under way. In health care, autonomous physiotherapy systems approved for clinical use are providing continuous musculoskeletal assessment, treatment and monitoring – operating day and night, without the scheduling limits of human staff.^[_] By sustaining care between appointments and automatically updating recovery plans as new data arrive, these systems keep patients progressing even when professionals are offline. In this sense, they exemplify how AI can make a service “always on”: delivering guidance, feedback and adjustments in real time while clinicians focus on complex or high-risk cases.

This kind of early deployment shows how intelligence can be built into the service process so that capacity expands continuously rather than episodically – not by adding more workers, but by making professional expertise persistently available. Crucially, this is not just about optimising today’s workflows. By relieving the workforce bottleneck, AI unlocks new service models that were previously impossible within the cost and staffing constraints of the 20th-century architecture: a personalised tutor for every child, proactive welfare triage for every household, early intervention for every at-risk individual. In effect, AI allows services to scale in two directions at once – by boosting what professionals can do, and by making more possible without them.

Breaking the labour bottleneck is the foundation of fiscal sustainability. Once services can scale without proportional labour, they can deliver rising output without rising cost, addressing the structural cost disease that has long haunted public provision.

Personalised: From Standardisation to Customisation by Default

The opportunity goes beyond output measures, because quality can be improved at scale, too. Historically, public services were designed for the “average user” and delivered through standardised, one-size-fits-all models. This was not a failure of imagination but a function of necessity: industrial-era systems had to deliver for millions with limited professional capacity and slow information flows. Standardisation was the only way to achieve scale.

But this logic has costs. Services often over-deliver to some, under-serve others and intervene too late or in the wrong way. Citizens experience the system as blunt, inflexible and unresponsive to their actual circumstances. Professionals are forced to shoehorn diverse needs into rigid categories, knowing that the fit is poor.

AI makes personalisation the default mode of service delivery. With real-time data and adaptive algorithms, services can adjust intensity, timing and content to each individual. A student struggling with a specific concept can receive targeted reinforcement, while another who has mastered it can move ahead. A patient can follow a care pathway tailored not just to their diagnosis but to their lifestyle, risk profile and comorbidities. A jobseeker can receive proactive nudges and training opportunities customised to their needs and aligned with their local job market. Vulnerable citizens can be flagged through integrated service interactions, triggering tailored outreach before problems escalate.

This shift builds personalisation into the system at scale. Instead of treating everyone as average, services flex to diversity in real time. The benefits are twofold: citizens receive support that actually matches their needs, and resources are deployed with far greater precision, reducing waste and increasing effectiveness.

Just as importantly, personalisation fuels system-wide learning. Every interaction generates data: which interventions helped which types of students, which treatment pathways worked best for which patients or which employment-support strategies succeeded in which local labour markets. Instead of relying on infrequent evaluations or national averages, the system can continuously observe what is effective in real time.

Aggregate and anonymised data in these feedback loops allow frontline professionals, service providers and policymakers to understand what works, for who and under what conditions. They make it possible to refine interventions dynamically. A welfare programme can adapt its approach based on the characteristics of households it helps most. In education, teaching sequences can be improved by tracking which explanations unlock progress for different learners. A health service can adjust protocols by analysing how different patient profiles respond to treatment. Importantly, with the time and resources freed up by AI, public servants now have the space to reflect on, absorb and put into practice these lessons learned.

In this way, personalisation becomes not just a feature of delivery but the engine of continuous improvement. Services learn from every case, policy becomes evidence-led by default and the system as a whole gets smarter the more it operates – driving more targeted interventions, more efficient use of resources and better outcomes across the board. The current doom loop is replaced by a positive self-reinforcing cycle.

Preventative: From Reactive to Proactive

The existing model of public services is structurally reactive. Support typically arrives only once a problem has fully materialised – a patient is admitted to hospital, a pupil is already falling behind, a family is out of work or an offender is already back in custody. This orientation towards late intervention is not a matter of neglect but of design: with limited information and scarce resources, services are forced to respond to visible need rather than anticipate risk. By the time the system acts, harm has occurred, costs have escalated and options have narrowed.

This built-in reactivity is the main reason why, despite long-standing consensus on the value of prevention, public services have failed to move decisively in that direction. But AI capabilities, adopted at scale, enable a decisive move from reactive firefighting to proactive delivery. With predictive analytics, pattern recognition and cross-system data, public services can identify risks earlier and act before crises escalate. A child showing early signs of disengagement or rising absence in school can receive timely support to re-engage rather than waiting for exclusion. Health data from wearable devices can alert clinicians to deteriorating conditions before an emergency admission is needed. A welfare system can be on the lookout for households at risk of debt spirals and provide guidance or financial support before they fall into a crisis that is ultimately more expensive to resolve.

This proactive orientation has financial as well as social benefits. Public spending today is consumed by crisis response – hospitals operate at full capacity as preventable diseases escalate into acute conditions, schools intervene only once failure is visible and welfare payments flow only once people are already out of work. By redirecting resources upstream, AI enables an investment model that reduces long-term costs while improving outcomes. Forecasting demand, reallocating resources dynamically and addressing risks early prevents expensive acute interventions, aligning capacity with need and freeing up resources for prevention.

In health alone, modelling by TBI shows the scale of what prevention could deliver.^[_] Cutting the incidence of six major diseases that keep people out of work – cancer, cardiovascular disease, chronic respiratory illness, diabetes, and mental-health and musculoskeletal disorders – by just 20 per cent would raise GDP by £20 billion within five years and £26 billion within ten. The resulting fiscal dividend – through higher tax receipts and lower benefit payments – would reach £10 billion a year by 2030 and £13 billion by 2035. When it comes to obesity, TBI modelling suggests that broader access to anti-obesity medications could generate cumulative fiscal benefits of around £52 billion by 2050.^[_]

Proactivity is inseparable from real-time accountability. Services can only prevent effectively if they can see problems as they emerge. AI makes this possible by continuously tracking interactions across the system. Delays in case processing, spikes in absenteeism or uneven access to health care can all be detected early and seen in the context of benchmarking against similar providers, allowing corrective action upstream without imposing unrealistic expectations. For leaders, live dashboards and AI-powered suggestions provide system-wide grip and evidence-based advice; for frontline staff, they offer immediate feedback that supports learning and adaptation.

This shift is profound: accountability ceases to be retrospective and compliance-driven – based on audits or inspections after harm has occurred – and becomes a live, operational function of the system. In effect, services gain the capacity to see around corners and adjust course in real time.

The benefits are twofold. For citizens, problems are anticipated rather than endured, with support arriving earlier, more appropriately and often less intrusively. For the system, resources are used more efficiently, with costly acute interventions replaced by targeted, preventative action. AI therefore makes possible what the legacy operating model could not: a state that is not only more responsive, but proactively protective.

Empowering the Front Line: From Command and Control to Informed Autonomy

The original operating model pulled decisions upwards because the centre lacked visibility. Slow data flows and paper records meant one way to ensure alignment was to reduce autonomy on the front line. Teachers, doctors and caseworkers were expected to follow rigid protocols, while central administrators issued targets and directives in the absence of real-time intelligence.

AI removes this trade-off. With live data, predictive tools and digital co-pilots, frontline professionals are empowered to act with greater discretion and confidence. A social worker can be alerted to early warning signs across multiple agencies. A GP can draw on AI-generated summaries of a patient’s history and risks. A teacher can access real-time insights on pupils’ progress. Each is able to make better decisions locally, while still feeding into system-wide intelligence that gives the centre the oversight it needs.

At the same time, AI helps to renew the workforce itself. By automating repetitive tasks, from documentation to scheduling, it reduces the drudgery that drives burnout. Professionals are freed to focus on the parts of their roles that are relational, complex and meaningful: the exercise of judgement, empathy and ethical decision-making. Instead of being stretched ever thinner, staff are surrounded by intelligent support that makes their jobs more sustainable and rewarding.

The result is a new balance: autonomy and alignment no longer cancel each other out. The centre gains a clear picture of performance, while professionals regain the discretion to tailor their work to the realities they face. AI therefore enables both a return to professional agency and a new settlement for the workforce: one that recognises their value while equipping them with the tools to succeed.

The Possibility of a Different State

The case for reform is clear: the existing operating model of public services cannot keep pace with the demands placed upon it. What AI offers is not another round of marginal adjustments, but the chance to build a different state altogether – one that can scale without ever-expanding labour, adapt to individuals rather than impose uniformity, anticipate risks instead of waiting for crises and empower professionals instead of constraining them.

Taken together, these shifts mark a profound change in how public services could work. A system that was once labour-bound, standardised, reactive and centralised can become always on, personalised, proactive, preventative and professionally empowering. For citizens, this means services that are more responsive, humane and effective. For professionals, it means renewed capacity to focus on the judgement, relationships and ethical decisions that matter most. And for society as a whole, it means a state able to learn continuously, and to deliver fairness and resilience at a scale and quality that was previously unimaginable.

In short, if the 20th-century state was about building universal access, the 21st-century state must be about building mass customisation. That is what AI makes possible.

Delivering services using an AI-era operating model requires more than new tools. It requires a new model of public-service infrastructure. The public sector cannot unlock the potential of AI through isolated pilots or incremental upgrades to legacy IT. It needs a coherent, interoperable foundation that rewires how services are accessed, delivered and improved.

We set out three mutually reinforcing layers that we believe are required to provide this foundation. Each layer builds on the last, reflecting both current achievability and the UK’s proximity to implementation:

1. Digital ID is the starting point and the connective infrastructure of public services. It links individuals to their data and entitlements across systems, giving them seamless, personalised access while reducing friction. This is the most immediately realisable step, with functioning models already in place in countries such as Estonia and Singapore.

2. Modular public-service platforms come next. These provide the shared infrastructure through which staff and citizens engage with each other and with services. With flexible workflows, pluggable tools and common standards, platforms make services more adaptive, scalable and capable of integrating AI into everyday delivery. For example, a national education platform could provide every learner with a personalised learning pathway to support teacher-led schooling in and out of the classroom, while enabling schools to select from a range of AI tutors and learning tools that plug into the core system.

3. A real-time system-intelligence layer is the most ambitious step. By aggregating de-identified signals generated through millions of daily interactions on platforms, it would equip frontline teams, local leaders and national policymakers with live feedback, enabling continuous learning, early intervention and dynamic resource allocation. For example, in health it could track referral flows across providers to spot bottlenecks before they escalate, while in welfare it could flag patterns of claimant disengagement from job-search and employment-support interactions that signal rising risk.

Each component directly enables the four shifts needed for reform: digital ID makes personalisation by default possible, platforms extend that personalisation while also creating always-on access and empowered frontlines, and the intelligence layer delivers proactive, preventative services.

Digital ID: The Connective Infrastructure of the State

The first and most immediate step in building AI-era delivery infrastructure is the creation of a secure, consent-based digital ID. Digital ID forms the connective infrastructure of the Reimagined State – the foundation that links people to their data, entitlements and interactions across government. Rather than a single app or login, it is the data layer that enables public services to recognise individuals, integrate information responsibly and act intelligently on their behalf. It is the prerequisite for seamless, personalised and trustworthy delivery at scale.

Every interaction with government begins with the question of “who is this person?” and “what do we know about their circumstances?”. In today’s data-rich but insight-poor environment, the state may already, in some part of it, hold answers to these questions but nonetheless demands that they are answered repeatedly, sometimes inconsistently, and often through duplicative processes – multiple logins, forms and verification checks across departments. A secure digital ID solves this not simply by creating one new access point – such as a smartphone app – but by providing a consistent identity, attributes and service-delivery backbone that underpins all of them. It allows information about an individual to move safely between systems, so that services can start from context rather than from scratch. It also allows an individual to exercise previously impossible levels of control over those flows of information to better shape their own experience of interacting with government.

Digital ID functions as the hidden architecture of the state – the wiring that connects services together and allows them to operate as one coherent system. It makes possible the shift from reactive to proactive delivery, because it enables data about entitlements, life events and needs to be securely linked and used to propose, and even pre-approve, services. When done well, digital ID becomes the foundation for personalisation, integration and trust.

In doing so, identity shifts from a mechanism of access to a source of empowerment. A well-designed digital ID allows citizens to see and manage their verified information, and consent to how it is used across the state. Exchanges can be made transparent, visible to the citizen and reversible: users can check when their data have been accessed, by which service and for what purpose – and can withdraw consent whenever they choose. In this model, privacy and transparency are not afterthoughts but built into the system’s architecture from the start.

International examples show what is possible. Estonia and Singapore have demonstrated how identity infrastructure can underpin seamless, personalised services across government. The United Kingdom is closer than it appears: the government’s One Login programme already provides a starting point. Expanded into a full national digital ID – linked to a secure digital wallet for credentials such as driving licences, benefit entitlements or Disclosure and Barring Service (DBS) checks – it could form the backbone of an integrated, citizen-centred state.

For citizens, digital ID acts as the structural layer that keeps services joined up and responsive. A job loss, change of address or birth of a child could automatically update relevant systems and trigger eligibility checks, without the citizen having to repeat their story or resubmit documentation. Instead of the citizen being the integration point, the system integrates around the citizen.

For government, digital ID supports the data-rich foundation that AI requires. Once individuals can be securely linked to their verified records across domains, services can deliver smarter triage, earlier intervention and more tailored support. Professionals can rely on accurate, up-to-date information rather than incomplete or duplicated records, enabling faster, more informed decisions.

The fiscal case remains strong. TBI’s paper The Economic Case for a UK Digital ID shows that a national digital-ID system could deliver net benefits of around £2 billion per year to the Exchequer, with setup costs covered within three years. But as TBI’s recent paper Time for Digital ID: A New Consensus for a State That Works makes clear, the stronger case for digital ID today is driven by public frustration with slow, fragmented services and a growing demand for radical change in how the state works. Digital ID responds directly to that demand. It provides the connective infrastructure needed to fix services in practice – enabling interoperability and making genuinely joined-up, personalised public services possible at scale. Without it, reform remains fragmented. With it, the state gains a secure, data-rich foundation for seamless, adaptive and continuously improving public-service delivery.

Modular Public-Service Platforms: A New Front Line of Delivery

If digital ID is the connective infrastructure of the state, then platforms are the new front line of delivery. Today, public services are still too often delivered through isolated institutions – schools, hospitals, departments – each with its own systems, data and rules. This fragmentation hampers coordination, drives up administrative overheads and fails to reflect the complexity of citizens’ lives, which rarely fit neatly into bureaucratic categories or institutional boundaries. Platforms offer a way out of this maze.

Modular public-service platforms are best understood as shared digital infrastructures that integrate a wide range of AI-enabled tools within a common framework. Much like the app-based architecture of iOS, government can supply the core “operating system”, which defines the rules of the road and establishes guardrails around what can and cannot be done, as well as offering default modules. Frontline providers and users can substitute or augment them with third-party alternatives as long as they are integrated with the platform.

This creates a unified but flexible environment in which multiple providers can plug in, collaborate and innovate, while citizens navigate services through a seamless front end. In the platform model the government oversees the ecosystem – setting standards, ensuring safeguards and curating a flourishing marketplace of solutions that continuously improve public-service delivery.

For staff, platforms provide an integrated suite of tools, intelligent automation and real-time visibility of user needs, freeing them from repetitive tasks and enabling them to coordinate more effectively across services. In practice, this might mean teachers using adaptive lesson-planning assistants that automatically flag gaps in understanding and suggest tailored materials, or social workers drawing on AI co-pilots that consolidate case information from health, education and housing records into a single view. In health care, clinicians could rely on digital scribes and triage assistants that streamline documentation and surface real-time insights about patient flow across local systems.

But empowerment here is not only technical; it is cultural. By automating routine tasks and giving professionals richer insight, platforms rebalance roles so teachers, doctors and caseworkers can exercise judgement and discretion rather than operate as administrators of rigid workflows. Over time, as trust and capability grow, the suite of safe and secure tools would also expand to cover a wider range of tasks – from planning and scheduling to diagnostics, communication and data analysis – with professionals increasingly able to compose their own tool sets from a trusted ecosystem of verified applications. This renewal of agency, both technical and cultural, is essential if public-service work is to remain sustainable, innovative and attractive.

For citizens, platforms make services feel connected and responsive because tools operate within a shared digital environment and can exchange relevant, permissioned information. A parent supporting a child through an AI tutor, a patient using an AI symptom checker or a jobseeker working with a guidance assistant may enter through different interfaces, but once inside the platform those tools can recognise common context. For example, confirmed identity, eligibility, learning level, health status or employment goals do not need to be re-entered, and verified changes in circumstances can be reflected automatically across services.

As progress is recorded – a child mastering a concept, a patient completing a treatment pathway, or a jobseeker acquiring skills, securing interviews or entering work – other tools can adapt their recommendations accordingly, adjusting the difficulty of learning content, escalating a health interaction to a clinician or shifting employment support from job search to in-work progression.

Instead of fragmented encounters with separate services, citizens experience a continuous, adaptive journey in which support evolves as their situation changes. The platform stitches these interactions together in the background, creating a coherent experience shaped around the individual rather than institutional boundaries.

Crucially, platforms are not about standardising every service; they are about standardising the conditions for innovation. Governments retain control of the core infrastructure, standards and safeguards. Contestability occurs at the right level: specialist apps, AI tutors or diagnostic tools can compete and improve within a shared ecosystem, rather than through wholesale duplication of services. This creates the conditions for a flourishing market in which external providers can compete to offer the best tools, driving innovation and improving quality within a shared ecosystem.

Platforms are also what enable public services to be “always on”. In the analogue model, access was rationed through staff time: citizens queued for appointments, offices shut overnight and help arrived only within working hours. On modular platforms, however, many forms of support can operate continuously because AI tutors, symptom navigators and guidance tools live inside the same infrastructure. A child can get homework help at 10pm without needing a teacher, someone worried about a health condition can receive safe initial guidance without waiting for a GP appointment, and routine processes – eligibility checks, applications, updates and triage – can run continuously without requiring a professional to be physically present. None of this replaces teachers, doctors or caseworkers, but it provides citizens with an additional channel that is far less bound by labour and takes pressure off the system. Platforms therefore lift labour as the ceiling on availability, allowing citizens to experience public services as continuously present and responsive, rather than intermittently accessible.

The potential is already evident in real-world deployments. In May 2025 the government confirmed that the National Parking Platform (NPP), initially developed through trials in Greater Manchester, will now be delivered nationwide by the British Parking Association in collaboration with major operators including RingGo, JustPark and PayByPhone. The platform enables drivers to pay for parking in all participating car parks through their preferred app, ending the confusion of multiple systems and creating a single, consistent experience across the country.

For local authorities, the NPP provides a shared digital spine that reduces duplication, simplifies procurement, and gives real-time visibility of demand while leaving councils in control of pricing, enforcement and revenue. For users, it means shorter car-park search times, fewer fines and easier integration with wider mobility services such as electric-vehicle charging or Blue Badge provision. For the sector, its open-access, not-for-profit design encourages fairer competition and innovation through application programming interfaces (APIs) that allow new apps and services to plug into the core system. Crucially, the NPP illustrates how platform-based reform can convert a fragmented, frustrating service into seamless infrastructure – national in scope but locally governed, cheaper to run and better aligned with citizens’ needs.

If this approach can streamline something as routine as parking, its potential is far greater in domains that sit at the heart of public service. A national education platform could give every learner a personalised learning pathway, while allowing schools to select from a marketplace of AI tutors and content tools that integrate with the core system. In health, smart navigation through the NHS App could unify triage, referrals and self-care tools, enabling patients to move seamlessly between GPs, pharmacies, hospitals and digital providers. As our analysis of AI navigation has shown, deploying intelligent triage at scale could free up 29 million GP appointments annually and deliver £340 million in productivity gains by reducing inefficient and unnecessary pathways.^[_] But, as separate TBI analysis sets out, the bigger prize is in what the NHS App itself becomes.^[_] Rather than remaining a single-purpose interface, it can evolve into a platform: the universal entry point through which patients access integrated care pathways, personalised prevention services and a marketplace of accredited digital health tools.

This means that navigation is no longer peripheral but foundational to the health system, combining user-facing simplicity with system-wide intelligence, and making health care itself “always on”: available around the clock, embedded into everyday life and continuously learning from interaction. Done right, the NHS App could embed prevention, triage, diagnostics, treatment and follow-up in one consistent digital journey, underpinned by a single record and NHS identity for each patient, transforming it from a transactional service into the infrastructure of modern health care.

These examples point to something larger: the foundations of a platform state.^[_] Instead of fragmented programmes and siloed systems, whole domains of public service could be rewired around shared digital infrastructure. A social-support platform could bring together benefits, housing and employment services into one coherent journey, a justice platform could unify courts, prisons and rehabilitation into a continuous pathway, and a skills platform could support people as they move between education, training and work – from jobseekers reskilling after redundancy to workers progressing in employment and employers seeking matched talent. Others could follow in skills, care or licensing. Each would be distinct in purpose but united by the same operating logic – common rails that cut duplication, adaptive pathways that flex to individual circumstances, and feedback loops that allow the system to learn and improve with every interaction.

The precise costs and fiscal gains of platforms cannot be fully modelled in advance, but the direction of impact is clear. Platforms reduce duplication across departments, lower procurement costs and allow innovations developed in one domain to be reused in another. By consolidating core functions such as case management, payments and scheduling onto common rails, governments avoid the expense of repeatedly rebuilding similar systems. Shared infrastructure also makes it easier to upgrade security once, rather than patching vulnerabilities across dozens of bespoke systems, and to negotiate better terms with vendors by concentrating demand. Beyond these efficiencies, platforms enable automation and smarter triage at scale, reducing administrative overheads and ensuring that professional time is directed where it has the greatest impact – both of which have significant long-term fiscal benefits. Most importantly, they enable a decisive shift towards prevention as the system’s default stance – identifying risks earlier, directing support sooner and reducing the costly cycle of crisis-driven intervention.

Unlike digital ID, which can be delivered relatively quickly, moving public-service provision onto fully fledged platforms is a more ambitious, longer-term goal. But important elements of this model can already be built today for specific use cases – as the Oak lesson-planning platform demonstrates in education or as emerging navigation tools do in health.^[_] These early exemplars show that even partial platforms can transform how services are accessed and delivered long before the entire architecture is in place. But even as government builds what it can now, it must keep sight of the full transition ambition: platforms are the organisational scaffolding of AI-era public services. They shift delivery from siloed, institution-led and time-bound models to services that are adaptive, user-centric and always on. Without platforms, AI-enabled services will remain fragmented and marginal; with them, the state can function as an ecosystem – responsive to context, continuously improving and built around the needs of citizens rather than the convenience of institutions.

Real-Time System Intelligence: The Nervous System of the State

If digital ID is the connective infrastructure and platforms are the new front line of delivery, a real-time system-intelligence layer is the analytical nervous system that lets the state see, learn and act in the present tense. This kind of intelligence is already evident in tools that once would have seemed like science fiction: Google Maps, for instance, draws on the movements of millions to give every driver a live, adaptive picture of the road network.

A system-intelligence layer would bring that same logic to public services – transforming fragmented, episodic information dumps into real-time collective awareness. Its purpose is not to centralise control behind a single dashboard, but to create a distributed capability that converts the millions of daily interactions happening on platforms into actionable insights at the right tier – frontline, local, regional and national – while protecting privacy by design.

At its core is an event-to-signal pipeline. Every authorised platform records a set of common “observable moments” – enrolments, referrals, triage decisions, drop-offs, resolutions, delays. These raw events are translated by AI models into signals that people can use: early-warning flags, capacity alerts, equity gaps, recommended “next best actions” and estimates of likely impact.

Crucially, identifiable data never leave the originating platform. Signals travel upwards in a de-identified and minimised form. Frontline workers can see personal details only where this is necessary to support an individual case. Analysts and managers see aggregated or pseudonymised signals relevant to system performance, not named individuals. Humans cannot drill down to identifiable records unless they are explicitly authorised to do so for a defined operational purpose.

Where context must be preserved, matching happens inside the platform using selective disclosure and small-numbers suppression, ensuring that the individuality of circumstances is not lost while privacy is maintained. On the ground, this intelligence changes how services work. In education, a cluster of learning-gap signals for Year 7 pupils can automatically prompt the platform to generate offers of targeted practice, book short tutoring sessions and notify the special educational needs coordinator – without exposing full records to staff who do not need them. In health, chronic-condition alerts emerging from patient-reported data and remote monitoring can trigger a same-day nurse review, enrol a patient in a telehealth programme and pre-position pharmacy stock in the relevant postcode. For local leaders – academy trusts, integrated care systems, local authorities – the same signals aggregate into live views of bottlenecks, unmet demand and equity gaps. A spike in bullying-risk flags across several primaries can prompt a trust-wide pastoral review, push proven anti-bullying modules to teachers overnight and arrange targeted webinars for parents before the next school day. Because every intervention is tagged and re-observed, the system quickly learns which actions relieve pressure and which eventualities require escalation, allowing leaders to reallocate resources dynamically rather than wait for the next planning cycle.

Above these tiers, a federated national analytics hub turns de-identified feeds into system-wide learning. Policy teams see patterns early – for example, a regional fall-off in childhood immunisations before it tips into an outbreak – and can deploy national levers: surge funding, workforce reserves, rapid regulatory guidance, targeted improvement teams and updates to standards. Accountability becomes operational and continuous rather than retrospective and episodic. Outcomes, disparities and delays are visible as they emerge, support can be targeted proportionately and the centre gains timely grip without centralising raw data.

Trust and safety must be built into the system from the start, not added as an afterthought. Privacy is protected at source: only the right people can see the right information, sensitive details are hidden when numbers are small and data move through selective disclosure rather than open sharing. Every signal the system generates can be explained – showing where it came from and how confident it is – and fairness checks are built in so that outcomes can be monitored across different groups and places. Crucially, humans remain in charge. The system highlights risks and recommends next steps, but it is trained professionals who make decisions and take action.

The intelligence layer makes continuous improvement a lived reality rather than an aspiration. By tagging every intervention – extra tutor hours, a revised triage script, a rapid-response team – with what was done, by whom and to what end, the system generates a rolling evidence base of “what works” at a granular level. Post-intervention signals show whether outcomes improved and for whom. High-performing micro-interventions are then surfaced automatically to comparable units – “services like yours cut missed appointments by 15 per cent after adopting reminder X” – so local innovation becomes collective knowledge, rather than remaining trapped in isolated pilots.

In this way, the state becomes a learning organisation at scale. A local tweak to reduce missed appointments, a school strategy to close attainment gaps or a clinical pathway trialled in one trust can, if effective, spread nationally within weeks rather than years. Feedback loops make it possible to retire failing approaches quickly while amplifying successful ones, ensuring that public services improve continuously through collective intelligence.

An early example of such a system-intelligence layer already operates in England’s education system. The Department for Education’s (DfE) national attendance-data infrastructure now ingests daily registers from over 20,000 schools and “plays back” live analysis at school, local-authority and national levels through the View Your Education Data portal.^[_] By turning attendance from a backward-looking census into a live operational signal, it enables strategic prevention: risks are seen in days, not terms, and the front line receives the same visibility as the centre, allowing proportionate early intervention rather than retrospective enforcement.

The system operationalises prevention through benchmarking and banding rather than ad-hoc thresholds – grouping absence in five-percentage-point bands (0 to 5 per cent, 5 to 10 per cent, etc.) and flagging pupils approaching persistent absence (more than 10 per cent) or severe absence (more than 50 per cent). It also generates benchmarked attendance targets, allowing schools and councils to understand not just where they stand today but what good looks like for peers with similar characteristics. This shifts the conversation from compliance to improvement: schools can see the realistic band they should be operating in, compare themselves with both national and “similar school” averages, and spot patterns such as Friday spikes or cohort-specific trends that warrant action. Because this intelligence is distributed, not centralised, the DfE has effectively equipped the front line to innovate. For example, Oldham Council used daily attendance data to link Year 6 attendance histories with secondary-school admissions and flag pupils at risk of poor attendance before they started Year 7. Council teams then conducted more than 350 pre-transition home visits, providing tailored support – from arranging transport to securing uniforms – and achieved a four-percentage-point uplift in attendance among the targeted group.^[_]

These kinds of individual successes can, in turn, be captured centrally and diffused through the rest of the system. In practice, the attendance system functions as a live, federated feedback loop – translating routine administrative data into actionable insights for strategic prevention and continuous learning.

These gains are not only educational but economic. Pupils persistently absent in 2006–07 earned around £10,000 less per year by age 28 than their peers.^[_] The DfE estimates that recent attendance improvements – five million extra school days and 140,000 fewer persistently absent pupils – will protect over £2 billion in future earnings. By turning attendance data into early, proportionate action, the intelligence layer does more than raise attainment: it strengthens the fiscal foundations of the system itself. When services can identify risk early and intervene preventatively, they reduce the downstream costs of failure – translating real-time data into both improved life chances and long-term fiscal resilience.

The result is a self-improving loop: frontline teams receive actionable feedback that is timely, relevant and directly linked to decisions they can take, local leaders coordinate with live evidence, and national policymakers steer with real-time insight into what works, where and for whom. The system as a whole becomes accountable in the present tense, proactive and preventative, innovative by design and continuously improving through the spread of proven practice.

Laying the Groundwork for AI-Era Public Services

The future of public services depends on more than new tools – it depends on building the foundations that allow transformation at scale. Digital ID provides the connective infrastructure through which citizens can move seamlessly across services. Modular platforms supply the adaptive front line with the tools to empower both staff and citizens. Real-time intelligence creates the system’s nervous system, converting millions of interactions into actionable insights for professionals, leaders and policymakers.

Together, these layers make possible a profound shift in how services operate. They remove professional time as the system’s bottleneck, enabling scale without unsustainable labour and associated cost. They allow personalisation to become the default, adjusting pathways and service offers to each individual. They turn the state from reactive to preventative, intervening before crises escalate. And they empower frontline professionals with real-time feedback and intelligent support, renewing discretion and trust at the point of delivery.

Most importantly, this infrastructure creates a state that can learn continuously from its own operation – a self-improving system that becomes smarter, fairer and more responsive the more it is used. The task now is not simply to adopt new technologies, but to build the foundations that make a different model of public-service delivery both possible and sustainable.

Public services cannot be transformed by technology alone. The architecture described in earlier chapters – secure digital ID, modular service platforms and real-time system intelligence – provides the foundations for change. But only when paired with new ways of governing and delivering does that architecture form a true operating model for the AI-era state.

Principles of AI-Era Governance and Delivery

System Stewardship

In the AI era, the role of central government shifts from command to stewardship. Departments still set policy and standards, but they do so through shared digital platforms and live data, not static plans. Ministers see performance through real-time dashboards, and intervene by refining standards and rules governing escalation, or releasing targeted support, rather than issuing blanket directives. A revitalised delivery unit would draw continuous intelligence from across services, detect pressure points early and mobilise cross-departmental teams to resolve them, with a view to the top priorities of the government of the day.

This “signal-and-steer” model maintains coherence while leaving space for local adaptation, making government faster to learn and more consistent in its grip.

Accountability

Accountability moves from after-the-fact inspection to continuous visibility. Targets are expressed as dynamic and benchmarked thresholds embedded in digital systems: safety, equity and outcome indicators that trigger support or review when crossed.

Frontline staff can see their own impact not just through interactions with the people they serve but through verified outcomes data, leaders can spot risks as they form and citizens can view transparent metrics through public dashboards.

Inspectorates evolve into assurance partners – validating data quality, checking thresholds are fair, providing early warnings rather than post-hoc judgements, and facilitating improvement support from expert teams or peer networks.

Accountability remains real and rigorous, but it operates in the present tense.

Adaptive Intervention

Improvement becomes continuous, not episodic. Frontline teams receive live alerts when performance drifts, local authorities help coordinate peer support and national leaders escalate only when systemic risks emerge. Every action – from a school’s timetable change to a hospital’s new triage rule – can feed tagged data back into the system, creating a real-time evidence base of what works. Because these loops operate constantly, failure is caught early and success spreads fast.

Adaptive intervention turns a top-down performance regime into a living improvement network – self-correcting, proportionate and quick to learn.

Funding

Public money must flow through the same adaptive logic as delivery. Budgets move from rigid institutional allocations to modular, outcome-linked components. Core infrastructure receives stable investment and is not allowed to drift into legacy IT, while specific tools and interventions compete on verified performance. Frontline managers can redirect funding to the AI tutor, triage system or workflow tool that demonstrably works best. Treasury oversight focuses less on inputs and more on return: what difference each pound made, not simply where it was spent.

Funding thus becomes the engine of continuous improvement.

Workforce

Frontline staff remain the human core of public services, but their roles are redefined. AI co-pilots handle routine documentation and scheduling, predictive tools highlight risk, and real-time dashboards show progress and bottlenecks. Beyond automation, workers are equipped with the tools and intelligence to do their jobs better – giving teachers, doctors and caseworkers real-time insights into needs, capacity and outcomes. This combination of intelligent support and live feedback not only increases effectiveness but also tackles burnout, releasing staff from administrative overload and restoring the space for professional judgement, empathy and complex problem-solving – the tasks that cannot be automated.

Every adaptation a worker makes is captured and shared through the system’s learning loops. In this model, frontline professionals become public innovators – continuously experimenting, refining practice and feeding insights back into the system. Their local adaptations no longer vanish as isolated fixes but are validated, scaled and reused through shared digital platforms. In effect, the public workforce becomes a distributed innovation network, generating continuous, bottom-up improvements.

Citizen Co-creation

Citizens are enabled and empowered. Through digital-ID portals and service dashboards, they can see clear, case-level progress through public systems – whether that is the status of a licence application, movement through the NHS referral pathway, or a child’s attainment and support across the education system. They can adjust preferences, update information and provide structured feedback that feeds directly into system learning. Their consent governs how data are used and their interactions help services adapt in real time. Citizens can also opt in to test new features or kinds of services, contributing to national renewal through close engagement with government teams driving the next generation of reform.

Participation becomes continuous and practical, without imposing an administrative burden. Citizens shape their journeys through simple, embedded interactions – confirming information, setting preferences or giving quick feedback as they use services, rather than through separate forms or consultations. They help to improve the system for others as a by-product of everyday use. Trust is rebuilt through daily transparency, convenience and shared agency.

Education: Governing and Delivering Reform in Practice

Education shows how these principles combine into a coherent operating model.

Education today remains constrained by an outdated operating model. Classrooms are bound by timetables and physical walls, teachers are overloaded with paperwork, and learners are channelled through standardised curricula and exams. Support often arrives too late, once a child has already fallen behind or even failed high-stakes assessments, affecting their life chances permanently. Meanwhile, teacher shortages intensify, workloads expand, and attainment gaps persist and grow across geographies and incomes.

By applying AI-era governance and delivery, education can be rewired to become a dynamic learning ecosystem – always on, personalised by default and preventative.

Elements of this model were first set out in TBI’s 2023 report The Future of Learning: Delivering Tech-Enabled Quality Education for Britain, which outlined how technology could underpin personalised and high-quality education at scale. The AI-era operating model described here develops that concept into a full system architecture – linking identity, platforms and intelligence into one coherent approach.

Digital Learner ID

Every pupil would be issued with a secure digital learner ID, a persistent identity that links them to their learning history, entitlements, and progress across providers and phases. This replaces fragmented records with a single passport through the education system, owned by parents at first and transferred over time to the young person as they progress through their education. Teachers see a student’s trajectory in real time, pupils retain a portable record of the knowledge, skills and competencies they have mastered, rather than just courses completed or grades received, and parents gain a transparent window into learning journeys.

National Education Platform

The national education platform (NEP) provides the modular infrastructure for teaching and learning. It hosts content libraries, AI tutors, assessment tools and scheduling modules within a single interoperable environment which schools can configure to their context. A baseline, always-on AI-tutor would be provided by the government; schools and teachers could access a marketplace of certified third-party tools to switch out baseline tools.

For students, the NEP makes personalisation the default: adaptive tutors adjust to mastery, diagnostics recommend next steps and content is differentiated by ability or interest. For teachers, other modules automate low-value administration and surface live insights on class performance, allowing them to focus on coaching, mentoring and rich pedagogy. For the system, it provides scale: innovations proven in one school can be plugged in elsewhere without rebuilding infrastructure.

System-Intelligence Layer

Every interaction with the platform generates anonymised signals: engagement, mastery, attendance, wellbeing. Aggregated at class, school, trust, local authority and national levels, these create a live intelligence system. Dashboards flag emerging gaps, safeguarding risks or spikes in absenteeism. Interventions are tracked, tagged and evaluated, so the system rapidly learns what works and scales it.

Figure 5 shows how this intelligence layer would transform education governance for all stakeholders (from students and parents through to national policymakers) by using real-time insights to inform responsive actions.

In this model, accountability is not delayed until after exams or inspections; it is built into daily practice. Each tier of the system sees relevant insights in real time, acts on them and contributes to collective learning.

In this way, education becomes a self-improving system. Each learner’s journey feeds data into collective knowledge, each intervention is observed, validated and scaled, and innovation anywhere can become an asset everywhere. Schools cease to be isolated silos and become nodes in a connected ecosystem that learns continuously with its learners.

The shift in architecture must be matched by a shift in governance: how decisions are made, funding flows, work is organised, improvement occurs and citizens participate. Figure 6 illustrates how the core principles of AI-era governance and delivery apply directly to education.

Beyond Education: Reformed Public Services Across Domains

While education serves as a vivid example of what AI-era public services could achieve, the same operating logic applies across the public sector. The value lies not in isolated reform, but in a coherent model that transforms multiple services simultaneously and allows them to work together around citizens’ needs.

Health

The NHS App would evolve into the core platform of an “always-on” health system. By linking a secure digital health ID, which verifies identity and manages consent, to the single patient record, which holds a person’s clinical information, the system can give every citizen seamless, safe access to their data, entitlements and personalised prevention services. This infrastructure would reduce duplication, connect primary and secondary care, and allow trusted third-sector and private providers to integrate more easily, helping to break down outdated silos and put prevention at the core of the NHS offer.

Intelligent triage of clinical pathways through the app could free up millions of GP appointments and direct patients to the right care first time, while AI co-pilots would automate documentation, accelerate diagnostics and surface early-warning risks – restoring clinicians’ time for complex judgement and relationships.

A real-time intelligence layer built on app interactions would turn system performance into live feedback, allowing leaders to spot bottlenecks early, reallocate resources dynamically and hold services accountable against outcomes rather than retrospective audits – shifting the NHS from reactive crisis management to proactive, personalised and continuously improving care.

Welfare

A social-support platform could integrate benefits, housing and employment services around a single citizen record. Rather than expecting people to navigate multiple systems, eligibility for services would be assessed automatically and support triggered by life events – a lost job, a new child, a rent rise – turning welfare into an always-on safety net.

Predictive analytics would identify households at risk of hardship and offer proactive outreach, while personalised pathways would connect claimants to skills training, financial guidance or local support automatically matched to their circumstances and local economic potential.

Caseworkers, equipped with AI co-pilots, would have instant visibility of needs and progress, allowing interventions to be tailored and dynamic rather than rule-bound and delayed.

Justice

A justice platform could connect courts, prisons and probation services into one continuous service journey. Digital ID would reduce duplication in case management, while real-time dashboards would track caseload bottlenecks and reoffending risks across regions. AI tools would support judges and probation officers with tailored sentencing recommendations and risk assessments, making decisions both more consistent and more transparent.

At the same time, AI could make elements of the justice system “always on” through, for example, round-the-clock digital co-supervisors that stay in contact with offenders between human appointments, providing reminders, guidance and early alerts when risks arise. Preventative interventions – such as targeted rehabilitation programmes – could be scaled nationally through the same platform, turning reactive punishment into proactive protection.

Taken together, these examples show that the transformation is not sectoral but systemic. The same infrastructure – digital ID, modular platforms and real-time intelligence – creates the conditions for always-on, scalable personalisation, proactive intervention and continuous improvement in every service domain. Instead of a patchwork of fragmented bureaucracies, citizens experience a coherent state that recognises them across services, life stages and circumstances, and learns continuously from their interactions.

Public services are at a turning point. The 20th-century operating model, built for mass provision and labour-based delivery, has reached its limits: it cannot deliver speed, personalisation or sustainability in the face of rising demand and fiscal constraint. The evidence is clear – performance is deteriorating, trust is eroding and incremental fixes only paper over the cracks.

What is missing is not more pilots, more staff or more funding commitments, but a coherent playbook: a governing model that binds together the promise of AI, the lessons of past reforms and the foundations of a self-improving state. The last such playbook was New Labour’s reform agenda of the 2000s, which created coherence by combining performance management, user choice, competition and diversity of supply. But that model was tied to the constraints of its time – labour-intensive delivery and analogue-era data – and it cannot meet the demands of today.

The task now is to build a new reform agenda, one that moves beyond patchwork initiatives to rewire the state around the principles and infrastructure described in this report. The theory of change underpinning this agenda is straightforward: if the operating model of the state is rebuilt around digital ID, interoperable platforms and real-time system intelligence, then AI can act as a multiplier of human capability – making services scalable without proportional labour, personalised by default, proactive rather than reactive and continuously self-improving through live feedback loops.

In this way, AI becomes the enabling architecture of a new social contract: a state that learns as it operates, empowers its workforce through data and intelligent tools, and restores public trust by proving that government can once again deliver.

Fiscal sustainability has too often been pursued by cutting inputs – budget freezes, hiring caps and efficiency drives – that reduce capacity without changing the underlying operating model. These measures leave intact the labour-intensive, reactive systems that generated the financial pressure in the first place, while asking fewer staff to do the same work.

The AI-era approach reverses this logic: it makes sustainability an outcome of structural reform, not retrenchment. By enabling services to scale without proportional increases in labour, to act preventatively rather than reactively and to improve continuously rather than remain static, the AI-era operating model links better outcomes with lower long-term costs. It delivers fiscal discipline through productivity, not austerity – replacing short-term savings with enduring efficiency gains built into the fabric of service delivery.

The age of AI makes delivering a different kind of public services – always on, personalised, preventative and adaptive by design – a possibility. The following playbook sets out four core principles and recommendations for achieving this:

1. Use AI as a Multiplier

To make services always on and scalable without simply adding labour, use AI as a multiplier of human capability.

AI releases the system from the bottleneck of professional time. Intelligent triage, co-pilots and predictive analytics automate the repetitive and the routine so professionals can focus on judgement, empathy and complex decisions. Scaling no longer means more staff – it means smarter use of their expertise. AI makes services continuously available, responsive at any hour and far more productive.

Recommendations:

• Pilot modular public-service platforms that tackle one well-defined problem each in a few key public-service areas – initially with AI tutoring, smart health navigation and employment-support tools – to give citizens personalised, always-on assistance, and seamless access across education, health and welfare.^[_],[_]

• Deploy AI co-pilots, intelligent triage and predictive analytics within these platforms to support professionals – automating routine tasks, reducing burnout, and enabling more time for judgement and care.

• Create a public-service experimentation lab to embed live A/B testing – where different approaches are trialled in parallel to identify which delivers the best outcomes – alongside validation of new tools and workflows, and the development of AI model performance benchmarks.

• Train and empower public-service professionals to work confidently with AI and real-time data – starting by making middle leaders in education, health and job centres the first generation of AI leaders driving adoption by creating tailored year-long training programmes, with paid time allocated to complete them.

2. Rewire Around the Citizen

To make services personalised, they must be rewired around the citizen, not the institution.

A universal digital ID and modular service platforms allow people to move seamlessly across health, education, welfare and justice. Instead of navigating bureaucratic silos, citizens access a coherent system that adapts to them. They gain visibility and control through dashboards, preference settings and transparent data use. The state becomes something that wraps around the citizen – learning from each interaction and improving with every use.

Recommendations:

• Accelerate and expand the government’s One Login programme into a full national digital-ID system, providing every citizen with a secure, reusable identity linked to existing attributes. Position digital ID as the connective infrastructure that links citizens securely to all public services, enabling seamless, personalised access across health, welfare, education, justice and beyond.

• Develop a “user preferences” mechanism embedded in digital-ID systems for citizens to exercise greater control over their data and how they access services, both offline and online.

• Develop dashboards for citizens across health, education and welfare, giving individuals oversight of their journeys, nudges and real-time feedback.

• Institutionalise citizens’ involvement through an opt-in “beta testers corps” to co-design, test and validate new service features.

3. Govern with Foresight

Making services preventative requires foresight, not hindsight.

AI allows leaders to see problems before they escalate. Real-time intelligence, predictive analytics and dynamic funding enable early interventions and continuous adjustments. Governance becomes anticipatory: spotting emerging risks, reallocating resources before crises occur and testing solutions in real time. Fiscal and accountability systems shift from retrospective control to live prevention and adaptive learning. The state becomes proactively protective rather than reactively corrective.

Recommendations:

• Rebuild accountability around live dashboards and dynamic thresholds, with inspectorates such as Ofsted and the Care Quality Commission providing continuous assurance and early support rather than retrospective judgement.

• Pilot system-intelligence capabilities in education (tracking behaviour patterns and early warning signs), health (spotting referral bottlenecks), welfare (flagging claimant disengagement from job-search and employment-support interactions) and justice (tackling reoffending rates).

• Reform the Spending Review into a dynamic, outcome-based process in which funding flows to tools and interventions that deliver verified results, as TBI has set out in a previous paper.^[_]

4. Foster Public Innovation

Making services continuously improving requires a culture of innovation across every tier of the public sector. Continuous improvement depends on innovation – which depends on giving people the tools, data and permission to act. AI co-pilots, modular platforms and real-time intelligence enable professionals to test, adapt and refine what works in real time. Frontline teams need the capacity to experiment safely, local leaders need the autonomy to reconfigure services, and central government needs the visibility and stewardship to scale proven success. They also need the state to invest in training, ensuring that the entire public-sector workforce has a strong understanding of what these platforms can help them deliver and the practical skills to use them well for common benefit. Only then will innovation at the front line be genuinely widespread, fully unlocking the potential of frontline staff who go into public service because they care – and who would now have much greater ability to serve their fellow citizens, alongside greater ownership of the task of improvement.

Innovation must therefore flow through the whole system – bottom-up and top-down – turning every public servant into an active contributor to learning and improvement. The centre’s role is not to command, but to steward: curating standards, diffusing insights and ensuring fairness. In this way, the state itself becomes a learning organism – a self-renewing system that improves every day through the collective intelligence of its people.

Recommendations:

• Revive the Prime Minister’s Delivery Unit as a digitally enabled stewardship centre, drawing on real-time system intelligence to monitor outcomes, troubleshoot failures and diffuse best practice across the system.

• Reboot the Test, Learn and Grow programme, creating mixed teams with forward-deployed engineers focused on improving priority service outcomes, supported by real-time data and live tracking at the centre. Deploy these teams towards the use cases identified as the first modules of the new public-sector platforms.

• Recognise and reward frontline innovation, offering financial incentives and fast-track progression to frontline staff as successful local solutions are scaled across the system through shared digital platforms.

• Develop dedicated training programmes for frontline staff and middle leaders to become confident AI users and informed buyers of AI-era digital tools.

The AI-era playbook is not about bolting new technology onto crumbling foundations; it is about replacing those foundations altogether. The reforms outlined in this paper – the introduction of universal digital ID, modular platforms and real-time intelligence, leading to reformed accountability, enabled workforces and empowered citizens – are mutually reinforcing. Together, they create a state capable of continuous learning, adaptive governance and genuine personalisation.

The lesson of history is that reform only succeeds when there is coherence. New Labour’s modernisation programme demonstrated how a unifying framework could lift performance across services. The absence of such a framework since the financial crisis has left services fragmented, exhausted and unable to improve. The opportunity of the AI era is to rediscover coherence – but this time on terms fit for the 21st century.

The prize is immense: a state that scales without simply expanding labour, that prevents crises before they escalate, that empowers both citizens and professionals, and that learns from every interaction. The risk of inaction is equally stark: inexorable drift leading to unsustainable costs, declining performance and plummeting trust.

This is therefore not a plan for incremental changes but a new reform agenda focused on rebuilding the architecture of the state around self-improvement, coherence and citizens’ empowerment. If the 20th century was defined by building universal access, the 21st century must be defined by building mass customisation. With the right vision, investment and stewardship, Britain can seize this moment to rewire the state for the AI era, ensuring that public services once again meet the expectations and needs of its citizens.