The start of 2025 heralds a hard time for many Western nations, with the outlook for the public sector peculiarly bleak. Demographic shifts are boosting request for public services just as taxation revenues plateau and the labour force starts to contract. The result? Governments are under pressure to do more with less resources.
Many of the usual policy fixes no longer look viable. taxation rates are already at post-war highs. Public debt hovers close evidence levels. Large-scale immigration – erstwhile considered a welcome safety valve – faces increasing electoral opposition. And now, as if things weren’t already bad enough, the bond markets have begun to lose confidence.
Faced with these pressures, government leaders are one more time turning towards technology as their “get out of jail free” card. If social care, administration, and another civic functions require staff and backing that are no longer available, why not swap or supplement people with software that works 24/7 without overtime or demands to work from home?
The allure of technology
Given this landscape, possibly it’s no surprise the UK government has launched a 50-step plan designed to turn the UK into a powerhouse for artificial intelligence (AI).
Yet many questions are already being asked about how it will be achieved. Appeals to the magic of technology are barely new. Governments have been promising an imminent and extremist “digital transformation” of the public sector for over 30 years (Figure 1).
But possibly this time is different?
Opportunities and risks
Amid the sea of grim news and increasing policy challenges, 1 possible bright place stands out – the emergence of a fresh generation of AI, with the possible to aid support and improve the work of the public sector. Its proponents contend these systems will shortly replace – or radically augment – most types of cognition work.
The pitch to government is straightforward. Artificial intelligence is the only ace left in a deck full of bad cards. It offers a possible escape from looming labour shortages and, more importantly, a way for governments to keep – or even grow – vital services despite budgetary pressures and a shrinking workforce.
However, the correct consequence to this techno-solutionist optimism is simply a cautious “maybe”. Rolling out advanced technology is not in itself going to deliver public sector reform.
Government departments and agencies inactive operate on principles that developed in the age of dense industry. The problem-solving approaches baked into the core of state planning, policymaking, decision-making, and administration don’t align well with modern technologies and practices.
While it’s possible AI could contribute to a transformation of the public sector, it’s not going to happen unless there’s besides an overhaul of government culture, organisation, and plan processes. Otherwise it risks becoming yet another in a long series of promising technological fixes that neglect to deliver.
Linear versus circular
For over 3 decades, the UK government has tried to modernise its operations with digital tools and practices. But 1 reason these “digital transformation” initiatives have fallen short of their full possible is due to the fact that governments have not modernised their structures and operating models. They proceed to plod along in a traditional, linear fashion.
To gain value from technologies like AI, the state must decision on from its paper-era, top-down, one-shot planning. Governments request to learn from best digital practice and embrace a more effective, iterative approach to policymaking – experimenting, learning, and adapting.
The challenge of public sector adoption of AI mirrors well-known issues with earlier technologies, specified as web and mobile. alternatively of genuine transformation, government has simply replicated its existing organisations, processes, and transactions online – missing opportunities to rethink how policies and public administration are conceived, designed, delivered, and continuously improved.
Governments stay structured around a model that’s barely changed since Henry Ford developed the linear assembly line in Detroit shortly before planet War One. Everything happens stepwise. Governments follow a carefully choreographed routine, reminiscent of Ford-era production lines. Each squad handles a predefined task before passing the work forward, leaving no area to revisit earlier assumptions as the organisation learns.
Manifestos set out broad policy promises, frequently influenced by a political party’s favoured “think tank” or the latest attention-grabbing tabloid headlines. These policies are turned into government and fleshed out by departmental policy specialists before being handed off to operational and commercial teams.
It can take months or even years before the first technologist becomes involved, and longer inactive before a policy is ready for the rude awakening of public testing. This rigid, “waterfall” progression stifles the iterative process of “learning by doing” that is standard in successful digital organisations.
The digital iteration model
Digital organisations are structured around a completely different model. They implement an first solution as shortly as possible, and then iterate and improve it based on users’ interactions and feedback. alternatively of trying to foretell all result at the start, they experimentation and learn from real-world experience.
Unfortunately, this iterative approach clashes with the rigid, top-down nature of government policymaking. While digital organisations trust on continuous testing, feedback loops, and outcome-focused learning, most public institutions stay bound by linear, policy-first processes. Manifestos form government before any real-world validation occurs, and hierarchical structures stifle the experimentation essential for genuine transformation.
With 2 specified different, profoundly opposed models, it’s small wonder the past 3 decades of digital transformation programmes have made specified slow progress.
Fundamental conflict
Why do the 2 models disagree so fundamentally? At root, it’s about the illusion of predictability.
The outlook of a politician or policymaker rests on a shared belief that the outcomes of plan decisions can be anticipated in advance. Manifestos seldom contain hypotheses or shades of grey. They presume a stable, mechanical reality that can be manipulated with a top-down, frequently ideological “solution” agreed in advance.
This mindset is partially founded on the logic of the electoral system. In theory, parties ask voters to agree to policies in the abstract before delivering their applicable outcomes.
The digital world, on the another hand, operates on William Goldman’s principle: “Nobody knows anything”. Since no-one can full anticipate what will work in practice, digital organisations trust on extended investigating and feedback. Insights into real-world users’ experiences enable them to continuously improve their products and services, and to fine tune their own interior organisational structures, operations, and processes.
No wonder attempts to insert iterative reasoning into the state’s linear approach have failed repeatedly. The 2 systems’ basic assumptions are fundamentally opposed.
Blockers to the adoption of AI
So, why does this long-standing mismatch substance to the adoption of AI in government? due to the fact that it further amplifies the conflict between old school predict-and-control and the newer model of experiment-and-learn.
Because AI’s outputs – and the user behaviours that form them – are probabilistic and inherently unpredictable, developers can’t specify outcomes in a one-shot plan. They must gather real-world feedback, refine the model’s prompt or training data, and course-correct in consequence to how users interact.
This iterative process is the exact other of trying to predefine everything in a manifesto. It relies on investigating and adapting in real time alternatively than adopting a dogmatic solution at the start.
To benefit from this technology, policymakers and transportation teams have no choice but to embrace an iterative, evidence-based approach. They must quit the discredited conceit they can specify final outcomes at the very beginning, before the process of “learning by doing” always gets started.
Policymaking’s missed opportunities
Governments’ linear approach to policymaking inevitably locks in questionable assumptions and constraints long before policy always makes contact with the real world.
It’s an approach that generates immense missed opportunities – generalist politicians and officials frequently have small thought of how technology could make alternate ways of designing and delivering better policy outcomes.
Worse, the state’s linear mindset amplifies risk. Far besides frequently the unintended consequences of policy decisions only come into focus much later. By that point, policies have long been fixed, making it highly hard to rethink the strategy to mitigate emerging harms.
Governments are understandably afraid with ensuring fairness, equality, and accountability erstwhile adopting emerging technologies. This can besides best be managed with a “learning by doing” approach that embeds legal and ethical review and user feedback loops into all phase of the process.
Why it matters
Governments’ top-down, department-centric, project-based approach to procurement exacerbates these problems. Funds are allocated for a one-off silo effort. But technologies like AI require ongoing investment, tuning, and adaptation. all initiative must navigate the never-ending flow of fresh and improved models with ever-evolving capabilities. These fast improvement cycles mean there’s no specified thing as “job done”.
In short, the machinery of democratic government – manifesto promises, top-down policymaking, one-off budgets, department- and project-centric procurement, and lengthy implementation cycles that frequently only engage method expertise well downstream – is fundamentally mismatched with the technologies remaking our world.
The state’s mindset is inactive anchored to the age of dense manufacture and linear process automation alternatively than transformation and reform. Meanwhile, synthetic intelligence is catapulting an unreformed state into a future that, until recently, seemed confined to the pages of discipline fiction.
If there is good news here, it’s that technology businesses and democratic governments share at least 1 thing in common: they both search to discover what people need, and to supply it to them rapidly and effectively (Figure 2).
Figure 2: The fusion of policymaking and digital practicesThe real value of fresh technologies like AI isn’t in automating yesterday’s bureaucracy, but in reimagining and democratising the policymaking process from the ground up.
How governments should respond
If governments are to harness AI’s possible to address their social and economical challenges, they request to:
Show humility in political discourse: political leaders and parties should present policy ideas as questions or hypotheses – alternatively than promises. Politicians should cease to pretend they know everything in advance. “Learning by doing” is essential to designing and delivering better policies and public administration.
Involve technologists from the beginning: decision method experts and transportation teams into the earliest stages of policy ideation, conception and design, ensuring that applicable feasibility and iterative investigating and learning form and inform policy options.
Implement continuous, adaptive funding: decision distant from one-off lump-sum allocations. make budgeting models that fund initiatives on an ongoing basis, allowing for continuous iteration and improvement independent of current organisational silos.
Build cross-functional teams: break down bureaucratic silos so that policy, operations, commercial, and technology specialists work together from day one, fostering a shared culture of experimentation and learning.
Iterate policy development: treat policy proposals as hypotheses to be tested, alternatively than foregone conclusions. Introduce milestones for real-world feedback and incorporate that data into policy refinement to improve policy outcomes.
Develop user-centred performance metrics: shift from static targets to metrics that measurement how well policies meet user needs and deliver the intended outcomes. Continuously adjust strategies based on real-world performance data.
Technologies don’t be in a vacuum. Each fresh wave brings its own organisational and social implications. If Western governments are serious about delivering the reforms they’ve been promising us since the 1990s, they request to embrace technology into the heart of a much more fundamental structural transformation of the machinery of government.
Linear-sequential methods were transformative for manufacturing in 1913, but ill-suited to a digital planet powered by technologies like AI. It’s time to abandon the century-old assembly line mindset and adopt modern, iterative processes at all level.
Perhaps the biggest contribution that AI will make is in triggering the long-promised overhaul of governments’ structure and operations. By forcing leaders to face and shed their industrial-era assumptions, it could prove to be the catalyst for delivering the long-promised “digital transformation” of the state – helping governments meet not only the formidable challenges they face in 2025, but well beyond.
