The proportion of UK businesses using artificial intelligence has almost tripled since late 2023, but adoption remains relatively shallow across much of the business base, according to new analysis from the Office for National Statistics.
The ONS said self-reported AI use among UK businesses with 10 or more employees has increased from around 12% to around 35% since late 2023. Larger companies are more likely to have adopted the technology, with 49% of businesses with 250 or more employees using at least one AI technology, compared with 28% of businesses with 0 to 9 employees.
The headline rate shows rapid diffusion, although the depth of use is more limited. The average number of AI technologies used per adopting business has risen only modestly, from around 1.4 to around 1.6 since late 2023. The ONS said this implies relatively limited transformative impact to date for most AI adopting businesses.
Large language models were the most widely used AI technology among businesses with 10 or more employees in June 2026, at 18%. Visual content creation followed at 16%, data processing using machine learning at 12%, and image processing using machine learning at 6%. Robotics and other AI technologies were each reported by 2% of businesses.
Use also varies sharply by sector. Almost three fifths of businesses in information and communication reported using AI, while construction recorded much lower levels, at 13%. Digitally intensive and knowledge based industries were more likely to show both higher adoption and greater intensity of use.
The ONS also found a gap between employee and business level reporting. More than half of employees reported using AI for work or education, compared with around a third of businesses reporting use of at least one AI technology. The difference may reflect informal or individual use of AI tools that is not captured in formal business measures.
Only 10% of businesses with 10 or more employees that use at least one AI technology reported using AI extensively. Fifteen per cent of businesses said more than half of their employees use AI as part of their daily work, although the ONS cautioned that the survey questions are new and future waves should provide further clarity.
Improving business operations was the most common use of AI, reported by more than 60% of larger businesses. The ONS said this has not yet translated into widespread changes in overall workforce headcount.
Workforce effects are nevertheless emerging in specific roles. More than half of businesses report effects on creative or design roles when using visual content AI technologies, while a similar proportion report effects on administrative or clerical roles when using AI image processing.
The analysis creates a more nuanced picture than broad adoption figures alone suggest. Many companies are experimenting, using individual tools, or applying AI to contained processes, while fewer appear to be redesigning operating models around the technology. The gap between access and transformation is now the harder management issue.
Limited adoption can still produce useful gains. Summarisation, drafting, internal search, coding support, customer service assistance, and content production can reduce friction in day to day work. Those use cases do not automatically change business models, margins, decision quality, or customer outcomes. Stronger returns require data readiness, workflow redesign, governance, training, procurement discipline, and management attention.
The skills data reinforces that point. Businesses most commonly reported integrating AI skills through training or retraining existing staff. Among businesses citing lack of AI expertise as a barrier, around 62% reported training or retraining existing staff to integrate AI skills, compared with around 26% of businesses reporting no barriers. Only 11% of businesses with 10 or more employees reported that more than half of their workforce had received AI related training.
That leaves many organisations exposed to a governance gap. Employees may be using AI before formal training, usage policies, data handling rules, and review processes have caught up. Informal adoption can improve productivity, but it can also introduce weak documentation, inconsistent output quality, intellectual property risk, and sensitive data exposure.
Across finance, marketing, and workplace skills, the same pattern keeps appearing: tools are spreading faster than organisations can develop the management systems around them. The capability gap is particularly visible where employees already have access to public AI tools while internal rules remain uneven: AI readiness gap widens at work.
The ONS findings also have policy relevance. The UK appears to be one of Europe’s higher adopters, but productivity gains will depend on whether AI becomes embedded in core processes rather than remaining a collection of individual tools. Training, data infrastructure, sector guidance, and management capability may prove more important than adoption campaigns alone.
The next phase will be judged less by whether companies use AI and more by where, how deeply, under whose control, and with what measurable effect. The adoption curve is steep; the transformation curve is still forming.
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