Digital Economy Dispatch #280 -- Why the "Fastest AI Adoption in the G7" is the Wrong Goal

I have been watching the UK government's AI ambition grow considerably in recent months. And I find myself in an unusual position: broadly supportive of the direction of travel and yet increasingly concerned about the route being taken to get there.

Last month, Chancellor Rachel Reeves set out what she called the defining economic choice of our era. AI, she argued, is the technology that will determine whether Britain grows or stagnates, and the government's ambition is unambiguous: the fastest AI adoption in the G7. It is a serious commitment, made in a serious setting. The OECD's estimate that AI could add 1.3 percentage points annually to UK productivity, worth around £140 billion per year, is not unrealistic if the conditions are right.

And yet. According to the UK government’s own research published in January 2026, only 16% of UK businesses currently use AI in any meaningful sense. More striking still, 80% of businesses neither use AI nor have any plans to. That is not a foundation for G7 leadership. It is a baseline that the most optimistic reading of current policy trajectories would struggle to transform in the timeframes the government has in mind.

The Wrong Diagnosis

The government's framework for closing this gap has four strands: build compute capacity, invest in homegrown AI development, unlock public and private sector data assets, and create regulatory sandboxes through the new AI Growth Lab. Each of these is a reasonable thing to do. However, none of them, individually or combined, will shift the adoption rate in the way the ambition requires.

The reason is straightforward. Infrastructure does not adopt itself. Better models, faster compute, and more permissive regulation create the conditions for adoption. They do not generate it. Adoption requires organisations to change how they work: how they commission technology, how they build capability, how they measure outcomes, and how they integrate AI into processes that were not designed with it in mind. That is a coordination problem, not an infrastructure problem. And the policy levers it requires are quite different from the ones currently being pulled.

There is a signal in the data that the government should be taking more seriously. Recent Lloyds research found that more UK businesses are planning to invest in AI training than in AI technology itself. On the surface, that looks like caution. I think it may be wisdom. Organisations investing in capability before tools are, implicitly, recognising where the real bottleneck sits. It is not access to AI that is holding them back. It is the organisational readiness to use it well.

A Lesson We’ve Already Learned

Britain has solved a problem very like this one before. When the Government Digital Service was established in 2011, the challenge was not that good digital tools did not exist. They did. The problem was that every department was procuring, evaluating, and deploying them independently, producing fragmentation, duplication, and a market signal too diffuse for suppliers to build confidently against.

GDS worked not because it built better tools, but because it consolidated demand. The Digital Service Standard meant that what good looks like became a shared answer rather than a departmental guess. Procurement frameworks gave suppliers a stable, legible market. Shared outcome metrics meant that progress could be measured in something other than activity. Within five years, the UK was first in the UN e-government rankings and had saved over £4 billion through structural reform.

The adoption rate moved because the coordination problem was solved, not because the tools improved. That distinction is the key to understanding what AI adoption policy is currently missing.

The AI Growth Lab is, in spirit, the right instinct. Cross-economy sandboxes and sector-level testing are serious mechanisms. But sandboxes are by definition bounded and temporary. They generate evidence. What translates that evidence into scale is a demand-side architecture that organisations of all sizes can navigate without the bespoke evaluation and legal resources they simply do not have.

What Would Help

Three things would make a material difference to the AI adoption trajectory, and none of them require new legislation or large capital commitments.

First, a shared outcomes framework that defines what successful AI deployment looks like, not in terms of deployment counts or investment volumes, but in terms of measurable productivity and service improvement. The AI Opportunities Action Plan progress report tells us that 38 of 50 commitments have been delivered in year one. That is encouraging. But delivery of commitments is an input metric. What are the output metrics? If we cannot answer that question with precision, we are measuring the wrong thing.

Second, procurement consortia that allow mid-sized organisations, particularly across the public sector, to access AI solutions without the transaction costs that currently make independent evaluation prohibitive. This is how the Digital Marketplace worked. It is how a coordinated AI procurement architecture could work too.

Third, sustained investment in demand-side capability: the commissioning skills, the product management disciplines, and the governance literacy that organisations need to be good buyers of AI, not simply recipients of it. The British Chambers of Commerce has already warned that two thirds of UK firms report skills shortages. The Lloyds data tells us where firms think the gap sits. Policy needs to follow that logic.

The Ambition is Right. The Mechanism is Missing.

None of this should discourage us. Britain has real structural advantages: depth of research talent, a common law tradition that enables flexible contracting, and a public sector large enough to anchor demand at scale if it chooses to. The £140 billion productivity prize is achievable, in principle.

But the path from 16% AI adoption to G7 leadership runs through coordination, not acceleration. The last time Britain faced an adoption problem of this kind at scale, it built the Government Digital Service. The question now is whether we have the institutional imagination to do something equivalent for AI: not another initiative, but a real architecture for demand. That is the work. The ambition we have. The mechanism we are still looking for.

This question is at the heart of my new book, Making AI Work for Britain, to be published on 28th April by London Publishing Partnership. The argument is there in full alongside what a real demand-side architecture for AI might look like.