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Businesses are using AI more often, but adoption is uneven and the risks are not resolved. OECD data for member countries with available figures show firm use rising from 8.7% in 2023 to 20.2% in 2025. At the same time, 73% of surveyed UK businesses handling digitised data said they would be uncomfortable with that data being used to train external AI models. These figures describe different populations and concerns; they do not show that every business is adopting AI or that concern is rising everywhere.
What the latest adoption figures show
Across OECD member countries with available data, 20.2% of firms used AI in 2025, up from 14.2% in 2024 and 8.7% in 2023. The OECD reported 2025 growth of 42.4%, slower than the previous pace. These are not global adoption rates: countries without available data are outside the series. The OECD identifies ICT and professional and scientific services as sectors with the highest reported use, and suggests that widely available general-purpose tools may have contributed to growth; that is a possible explanation, not proof of cause. OECD, January 28, 2026.
The U.S. Census Bureau’s Business Trends and Outlook Survey (BTOS) offers a frequent, nationally representative measure of U.S. business use. From December 14, 2025, to May 3, 2026, reported use ranged between 17% and 20%; expected use over the following six months ranged from 20% to 23%. In the collection period ending May 3, 37% of firms with at least 250 employees and 32% of firms with 100–249 employees reported using AI. Less than 20% of firms with four or fewer employees reported use. Census found use increased among firms with at least 20 employees over the reviewed period, while the change among firms with fewer than 20 was not statistically significant. U.S. Census Bureau, May 26, 2026.
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1Clear out junk files and repair common Windows errors2Scan for outdated or missing drivers - takes under a minute3Repair Windows errors before they cause bigger problemsIn the UK, the Office for National Statistics (ONS) found that 30% of businesses used at least one AI technology in September 2026. ONS described the result as broadly stable from June and 20 percentage points above September 2023. Use varied by size: 51% among businesses with 250 or more employees, compared with 29% among those with fewer than 10. The information and communication sector had the highest reported rate, at 60%. ONS, October 8, 2026.
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Why the percentages do not match
An adoption rate is only meaningful alongside its survey population, definition of AI, and reference period. A firm-level estimate counts businesses; a worker survey counts people; an employment-weighted estimate describes the share of workers employed at firms that use AI. These measures can all be accurate while producing different percentages.
A Federal Reserve comparison of three public U.S. surveys illustrates the distinction. It reported about 18% firm adoption at year-end 2025, about 41% of workers using generative AI for work in November 2025, and an employment-weighted estimate that 78% of the labor force worked at firms that had adopted AI. The first asks whether firms use AI, the second whether workers use generative AI for work, and the third weights firms by the workers they employ. In the worker survey, 12% said they used generative AI daily and 35.2% had used it at least once in the prior week; neither figure is a share of businesses. Federal Reserve Board, April 3, 2026.
Even one survey’s rate can shift when its question changes. In November 2025, Census broadened its wording from AI used “in producing goods or services” to AI used “in any business function.” The new scope includes uses such as drafting emails. Comparisons across that change should be treated cautiously rather than read as a clean increase under an unchanged definition. For a fair comparison, check the geography and dates, whether the unit is a firm or worker, whether the question covers generative AI or AI technologies generally, the time window and threshold for “use,” firm-size weighting, and any wording changes.
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What businesses are worried about
Control over business data
The clearest measured concern in the available UK evidence is external use of business data for model training. In the DSIT-commissioned UK Business Data Survey, 73% of businesses handling digitised data said they would feel uncomfortable with their business data being used to train external AI models: 48% were very uncomfortable and 25% somewhat uncomfortable. Only 18% said they would feel comfortable. This finding concerns external training on business-owned data, not every AI application. The survey had 4,450 participating businesses and fieldwork from October 2025 to January 2026. Its separate finding that 41% used AI applies only to businesses that handled digitised data, not all UK businesses; by that survey’s measure, use was 82% among large businesses and 40% among sole traders. DSIT, UK Business Data Survey 2026.
Policies, skills, and implementation
Among UK businesses that said they use AI, 17% reported having a policy or guidance on AI use or development. Five percent had a formal written policy and 12% had informal guidance. The base matters: these percentages are not shares of all UK businesses. The same survey found awareness of regulatory guidance was mixed. Adoption can therefore grow faster than organizations establish consistent rules for appropriate use.
A U.S. Government Accountability Office review offers a related, but distinct, example from selected federal agencies. Officials at 10 of 12 agencies said existing federal policy, including data privacy policy, could present obstacles to adoption. Agencies also cited challenges with compliance, technical resources and budgets, and keeping appropriate-use policies current as technology changes. GAO recorded 32 generative AI use cases in 2023 and 282 in 2024 across 11 selected agencies; these are reported use cases, not a count of all federal AI activity. The findings apply to those agencies and should not be treated as a private-sector survey. GAO also identifies risks including misinformation, national security, and environmental concerns. GAO, Artificial Intelligence: Generative AI Use and Management at Federal Agencies, 2025.
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More use does not prove business value or job replacement
Adoption statistics count whether or how AI is used; they do not establish typical productivity gains, return on investment, or whether benefits exceed implementation costs. The available adoption sources do not support a universal ROI claim. Businesses need to assess results in their own workflows rather than infer value from rising uptake elsewhere.
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How a business can adopt AI with better controls
The evidence points to a practical tension: organizations may find useful applications while remaining cautious about data handling and governance. A bounded trial can help a business evaluate a specific workflow without treating adoption itself as proof of success.
Quick Recap
- Choose one contained workflow. Identify a task with a clear output and a way to check quality, such as drafting internal material for human review. Define what a successful result would look like before introducing a tool.
- Classify the data involved. Determine whether the task includes customer, employee, confidential, regulated, or otherwise sensitive information. Keep restricted data out of a system unless its use is approved.
- Review the service’s data terms and settings. Establish how prompts and outputs are stored, who can access them, whether they can be used for model training, and how retention or deletion works. Do not assume that a general-purpose tool treats business data the way the organization requires.
- Set an appropriate-use rule. Document approved and prohibited uses, review responsibilities, and escalation routes. Keep the guidance accessible to employees and update it when tools or policies change.
- Measure the outcome and revisit the decision. Compare the trial with the existing workflow on quality, time, cost, and error handling. Continue, revise, or stop based on the results and risks—not on adoption rates reported by other organizations.
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