Are three in ten UK businesses now using AI?

How the ONS reached the estimate

The result comes from Wave 165 of the Business Insights and Conditions Survey. The questionnaire was live from 21 September to 4 October 2026. The ONS sampled 38,592 businesses and received 9,933 responses, producing a 25.7% voluntary response rate. It weights responses so that the estimates better represent the business population within scope, and it labels the release official statistics in development rather than fully accredited official statistics.

That denominator is central to interpretation. Nearly three quarters of sampled businesses did not respond. Weighting can correct for known differences such as size and industry, but it cannot guarantee that responding and non-responding firms have the same appetite for AI or interpret the question identically. The ONS also warns about sampling variability and non-sampling error. The bulletin provides confidence intervals in its datasets, while rounded headline percentages can conceal small changes that are not statistically meaningful.

Coverage is also narrower than the entire economy. The business survey excludes several activities, including agriculture, finance and insurance, public administration and defence, public health and education, and electricity and gas supply. It is not a household or worker survey. Comparing its 30% estimate directly with a measure from employees, public services or a differently defined business sample would mix populations and questions.[1]

Adoption differs by business size, technology and industry

Businesses with fewer than 10 employees dominate the UK business population, and the ONS estimates that 29% of them used at least one AI technology. Among businesses with 10 or more employees, the estimate was 38%, three percentage points higher than in June 2026 and 26 points higher than in September 2023. The size gap matters: a single percentage for all businesses should not be read as a description of the average workplace or the experience of the average employee.

Among businesses with 10 or more employees, text generation using large language models was the most commonly reported technology at 21%, followed by visual-content creation at 19%. These categories can overlap because a business may use more than one technology. They also describe types of use, not a hierarchy of business value. The release does not measure accuracy, time saved, return on investment, security controls or whether employees were authorised to use the tools they reported.

Information and communication businesses had the highest reported AI adoption, at 60%. That concentration is plausible because software, data and digital-service firms often have both relevant skills and accessible use cases. It also cautions against treating a national average as a benchmark for every sector. A manufacturer, retailer, accommodation provider and professional-services firm face different data, regulation, capital and workflow constraints.[1]

What the workforce answers show—and what they do not

The ONS asked AI-using businesses with 10 or more employees which occupational groups were most affected. Thirty-eight per cent selected administrative and clerical roles, while 24% selected creative or design roles. The answers indicate where respondents perceive effects; they do not say whether the effect was positive, negative or large, and a business could identify a role because its tasks changed without any job disappearing.

On headcount, 6% of AI-using businesses with 10 or more employees said AI adoption had reduced their number of employees, while fewer than 1% reported an increase. Most did not report a headcount effect. These are percentages of responding business units in the relevant subgroup, not percentages of jobs or people. The bulletin does not provide a count of posts lost, the timing of changes, a non-AI comparison group or a causal design that separates AI from demand, restructuring, wages and other business conditions.

For workers and managers, the practical implication is to investigate tasks before predicting occupations will vanish. Administrative work may be exposed because text drafting, summarising and information retrieval are easy to trial, yet adoption can also shift work toward checking, escalation, client contact and process redesign. Employers need workload and quality measures, incident reporting and worker consultation if they want to know whether a tool removes drudgery, moves risk or simply adds review work.[1]

What business leaders and policymakers can use now

The survey is useful as a repeated national signal. It shows that reported AI use has spread well beyond a small group of technology companies since 2023, while the latest overall result offers no evidence of an accelerating quarter-on-quarter rush. That combination argues for practical adoption support—skills, procurement, data governance and evaluation—rather than assuming access to a chatbot automatically produces productivity.

For small firms, the relevant policy problem may be less whether a tool is available than whether the business can test it safely and decide if it is worth the time. Guidance should distinguish low-risk drafting from decisions about customers, employees or credit. Training should cover verification, confidential data, intellectual property and fallback routes. Vendors and advisers should be asked for task-level evidence and total operating costs, not only demonstration performance.

For government, the result is a baseline to connect with stronger evidence. Business surveys can track reported use quickly; administrative data, longitudinal panels and controlled workplace studies are better suited to measuring investment, output, wages, employment and distributional effects. Publishing breakdowns and uncertainty consistently will matter as adoption becomes more heterogeneous and the boundary of what respondents call AI keeps moving.[1]

The evidence limits

This is a large and timely official survey, but participation was voluntary and the response rate was 25.7%. Non-response bias can remain after weighting. Self-report can also produce both over-counting and under-counting: respondents may label ordinary automation as AI, may not know which services contain AI, or may omit unofficial employee use. Changes in awareness can move the reported figure even if underlying deployment changes less.

The estimates describe businesses, not establishments, workers or economic output. They do not measure intensity, model quality, spending, productivity, revenue, working time, safety incidents or distribution of gains. The reported headcount answers are especially easy to overstate because the unit is a business response rather than a person and because no counterfactual identifies what would have happened without AI.

Finally, the exclusions limit generalisation. Finance, public administration, defence, public health, public education, agriculture and energy supply are not represented in this business estimate. These sectors include some of the most consequential and regulated AI uses. Separate evidence is needed before the national business percentage is applied to them.[1]

What would change the assessment

Confidence that business AI adoption is still accelerating would rise if subsequent waves show a sustained increase outside sampling uncertainty, especially across small firms and sectors currently below the information-and-communication industry. A stable questionnaire and published confidence intervals are essential; otherwise a wording or category change can be mistaken for economic change.

The stronger claim—that AI is improving business performance or causing net job change—requires different evidence. Linked longitudinal data should compare adopting and non-adopting firms before and after deployment, record the technology and task, and measure output, hours, pay, hiring, displacement and quality. Independent workplace studies should also test whether gains persist after implementation costs and human review are counted. Until those results exist, the ONS release supports a conclusion about reported reach, not effectiveness or causation.[1]