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Small and midsize businesses can use AI now, but the strongest case is practical rather than guaranteed: choose a recurring task, test whether AI helps, and measure the result before expanding. Adoption is growing, yet smaller firms still face barriers such as skills, connectivity, cost, and uncertainty about accuracy and legal obligations.
AI use is growing, but adoption is uneven
Across OECD member countries, the share of firms using AI rose from 5.6% in 2020 to 14% in 2024, according to the OECD’s 2025 discussion paper AI adoption by small and medium-sized enterprises. That broad firm-level measure shows momentum, not that every business is ready or that AI will produce a return for any particular company.
A separate OECD report, Generative AI and the SME Workforce (2025), draws on a representative survey conducted in late 2024 with more than 5,000 SMEs in Austria, Canada, Germany, Ireland, Japan, Korea, and the United Kingdom. In that seven-country sample, 31% reported using generative AI for work. The country-level range was 24% in Japan to 39% in Germany. These figures describe those surveyed countries, not all SMBs worldwide.
Adoption also differed by size: 23.6% of one-person businesses reported using generative AI, compared with 45.8% of the largest SMEs in the survey, according to the OECD’s size analysis. The report’s survey asked whether anyone in the company, including the respondent, ever used generative AI for work. It therefore indicates reported use, not how often tools were used or how deeply they were built into operations.
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What SMEs report gaining—and what the figures do not prove
Among SMEs in the OECD survey that used generative AI, 65% reported improved employee performance, 35% said it helped them scale up, 29% said it improved their ability to compete with larger companies, and 26% reported increased revenue. These are respondents’ assessments, not evidence that AI alone caused the outcomes or a forecast of what another business will achieve. The OECD presents them in its report on reported effects.
The OECD estimates that AI could add 0.2–1.3 percentage points to annual labour-productivity growth across G7 economies over the next decade. This is a macroeconomic estimate, not a return-on-investment prediction for an individual SMB. Workplace effects can differ by role, function, and organization, and depend on whether people adopt and use the tools effectively; Microsoft Research makes this point in its July 2024 synthesis of workplace studies.
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Match the approach to the business’s readiness
The OECD describes four SME AI profiles based on digital maturity, complexity, and scope of use. They are ways to think about current readiness—not mandatory stages every company must pass through.
| Profile | Typical pattern | What it suggests |
|---|---|---|
| AI Novices | May use AI features already embedded in familiar tools for peripheral tasks. | Start with a bounded task and learn how to review outputs. |
| AI Explorers | Develop bespoke solutions. | Assess whether the team has the skills, data, and resources to build and maintain a tailored use. |
| AI Optimisers | Integrate tools across functions. | Coordinate workflows, training, data access, and oversight across teams. |
| AI Champions | Embed AI across operations and strategy. | Manage AI as an organization-wide capability, including governance and ongoing evaluation. |
The profiles and adoption considerations come from the OECD’s 2025 discussion paper. They do not rank vendors or identify a universally best product. A small, low-consequence experiment may suit a firm with limited experience; broader integration calls for stronger processes, capability, and oversight.
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Choose a task before choosing a tool
Begin with a recurring task that consumes staff time or requires a capability the team lacks. Then judge whether AI could assist with that specific work. The following checks help keep the decision grounded:
- Workflow fit: Is the task repeatable, and can AI plausibly help with a defined part of it?
- Consequences of error: What happens if an output is wrong, misleading, or inappropriate? Keep human review in workflows where mistakes matter.
- Data: Is the information needed for the task accessible and reliable? Does it include sensitive material that requires additional care?
- People and skills: Who will use the system, check its work, and handle exceptions? What training will they need?
- Technical readiness: Is the business’s connectivity adequate, and are any compute or other AI-enabling inputs required?
- Full cost: Consider implementation and ongoing costs, not just access to a tool.
- Safeguards: Decide how to check accuracy and harmful outputs, and how the business will address relevant legal obligations.
The OECD identifies connectivity, AI-enabling inputs, skills, and finance as adoption enablers, while noting challenges that include accuracy, harmful content, and legal uncertainty. Its discussion is a framework for assessing readiness, not a legal determination for a particular company. See the OECD’s material on enablers and risks.
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Run a measured pilot, then decide whether to expand
A pilot should test a business outcome, not merely whether a tool can produce an answer. Before using AI, record a baseline for the task and choose a result to track—for example, time to completion, output quality, or the amount of work the team can handle. Keep a person responsible for checking outputs where the consequences of error warrant it. After the test, compare the outcome with the baseline and account for setup, review, and training time as well as any ongoing expense.
This is a practical way to apply the OECD’s findings on capabilities, skills, and task-level reported effects; the OECD does not prescribe one universal implementation sequence. Expand only if the pilot provides useful evidence for the next step. If results are inconsistent, review the task, data, instructions, staff training, and review process before increasing the scope.
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In the OECD survey, 83% of SMEs using generative AI reported no effect on their overall staff needs; 9% reported a decrease and 6% an increase. Respondents were not asked how large those effects were, so the report cannot establish a net employment effect. The OECD also notes that skill needs can increase as SMEs use generative AI. These findings support treating AI as a possible way to change task capacity or performance, not as proof that a business can remove roles without consequences.
The OECD report states that “Generative AI has democratised the use of AI.” For SMBs, the useful implication is access to tools and new ways to approach particular tasks—not a promise of equal capability, automatic productivity, or guaranteed revenue. Skills remain a major barrier, and the OECD says SMEs welcome training support; investing in staff capability is part of adoption, not an optional afterthought.
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