AI Economic Preparedness:
A Scenario Analysis for the UK

The case for economic preparedness is not that any particular AI future is certain. It is that technological change could outpace the ability of governments and institutions to respond. AI may raise productivity and national income while concentrating gains among the owners of AI assets and losses among particular workers, weakening established tax bases or increasing dependence on foreign technology and capital. Preparing for these possibilities now preserves the government’s ability to act later without requiring it to predict the direction or speed of change.

The report uses a set of hypothetical future scenarios as a basis to think through the effects AI could have on the UK’s labour market, fiscal system, and international competitiveness. As a first analysis of this kind, it turns qualitative scenarios into quantitative metrics and applies them to the UK to generate directional insights into the transformative impact AI could have on the UK. The report first assesses the UK’s baseline in 2026 to establish how exposed the UK’s economy is to AI transformation. Drawing on the Windfall Trust’s AI Observatory data framework and a quantitative analysis of UK-native data, the report presents the first quantitative assessment of the UK’s vulnerabilities and opportunities in a hypothetical future transformed by AI, and presents for the first time, bespoke policy recommendations.

The analysis shows that today’s economy combines substantial exposure with limited control over the technology, infrastructure and capital through which an AI transformation would arrive. Around 11.9 million people, or 36% of UK employment, work in AI-exposed occupations, accounting for 38% of the national wage bill. Approximately 47% of UK exports are exposed to AI, while the country controls only about 0.5% of global computing capacity. Whether the UK gains from an AI transition depends on how far the productivity gains in exposed work are shared, and on how many of the people in that work lose their jobs or move to lower pay.

The scenarios emphasise why the character of the transition matters. Under a more gradual AI transition, job displacement could be gradual and concentrated: around 1.2 million workers (3.5% of those in employment) are affected under the central assumptions, annual gross earnings losses reach approximately £11 billion, and the fiscal deterioration is about £5 billion a year (~0.16% of GDP). Productivity-led growth could be sufficient to cover that fiscal cost, but the government would still need targeted support for the workers who lose jobs or return to work on lower pay, because the gains and the losses fall on different people: higher total pay does not reach the 1.2 million affected.

Under a rapid AI transformation, different pressures reinforce one another. Between 3.6 million and 7.2 million workers (11-22%) could be displaced within three years, with approximately 3.2 million (~10%) not finding new employment under the central assumptions. Labour-related tax receipts could fall by around £50 billion a year, while recurring benefit spending might increase by about £20 billion, producing a central annual fiscal deterioration of approximately £70 billion (~2.3% of GDP). Rapid growth would not necessarily close this fiscal gap if much of the new income accrues to foreign owners, retained profits or untaxed capital gains.

The report concludes with ambitious and UK-bespoke policy responses to detect and counteract possible adverse effects of AI-led transformations. The conclusion is not a single prediction or programme but a call for early policy action that protects the government’s option space. Some preparations are worthwhile now because they improve the UK’s preparedness under almost any scenario: earlier detection of occupational disruption, fiscal and welfare stress-testing, stronger evidence on AI-related income, and better terms for public support of AI infrastructure. Larger interventions should be designed in advance but activated only when agreed indicators show that disruption is occurring. This approach allows the UK to pursue the benefits of AI adoption while retaining its option value and responsiveness for possible AI shocks.