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Small and midsize businesses are adopting AI, but the figures depend on what counts as adoption—and smaller firms remain behind large ones in cross-country comparisons. The biggest gap is not simply whether someone has tried an AI tool; it is whether a business has embedded AI in core workflows and put safeguards around its use. That gap is creating an advisory opportunity for managed service providers (MSPs), although provider surveys show stronger evidence of customer demand than of substantial AI-service revenue today.
How far has AI adoption reached in smaller businesses?
There is no single adoption rate that answers this question. Surveys, transaction records, and measures of AI in core business functions capture different activities, populations, and geographies. They should be read as distinct indicators, not combined into one global percentage.
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| Evidence | What it measures | Finding |
|---|---|---|
| OECD countries, 2024 or most recent available year | Reported firm use of AI by employee band | 11.9% of firms with 10–49 employees and 20.4% of firms with 50–249 employees, compared with 40% of firms with 250 or more employees. OECD report |
| OECD G7 comparison, 2024 | AI use in core business functions | Country rates ranged from 1.9% in Japan to 6.1% in the United States. OECD report |
| United States, end of 2025 | Small businesses with payments to AI services in de-identified Chase Business Banking transaction data | JPMorgan Chase Institute estimated that 17.7% paid for AI services. Its estimate was close to the Census Bureau Business Trends and Outlook Survey’s 17.8% for the same period, but the measures have different definitions. Transaction data do not capture all free-tool use. JPMorgan Chase Institute |
| United States, 2025 | Further detail within the JPMorgan Chase Institute transaction-data series | 12.03% paid for generative AI services and 9.44% paid for three or more AI services. These figures indicate variety among paying businesses, not how deeply AI was integrated into their operations. JPMorgan Chase Institute |
| United Kingdom, 2025–26 | Businesses handling digitised data; reported use by employee band | AI use was reported by 51% of small businesses with 10–49 employees and 58% of medium businesses with 50–249 employees. This survey population and method differ from the OECD and US transaction measures, so the rates are not directly comparable. UK Business Data Survey 2026 |
The OECD comparison supports a clear, qualified conclusion: AI use is less common among smaller firms than among larger firms. It does not mean small businesses are standing still, nor does it establish that every country or industry follows the same pattern. OECD cautions that country coverage and definitions can vary across comparisons.
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“Using AI” can mean anything from an employee trying a standalone assistant to a system embedded in a recurring business process. A broad survey question may count experimentation; a payment record counts purchases of services; a core-function measure asks about a narrower kind of operational use. None alone describes the whole journey from trial to reliable deployment.
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The OECD’s G7 measure illustrates the distinction: in 2024, the share of firms using AI in core business functions ranged from 1.9% in Japan to 6.1% in the United States, far below the broadest reported-use figures in many settings. In the UK government survey, only 31% of AI-using small businesses said they had integrated AI into existing systems in 2025–26; the corresponding share for AI-using microbusinesses was 27%. Those figures suggest that reported use often has not yet become connected, routine operations.
Governance is another marker of maturity. Across AI-using businesses in the UK survey, 17% said they had an AI policy or guidelines, while 5% had a formal written policy. A business can therefore be using AI without having settled who may use it, what information may be entered, how outputs are checked, or who is responsible when a workflow fails. UK Business Data Survey 2026
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What is driving the gap between smaller and larger firms?
The adoption figures establish a difference in reported use by firm size; they do not prove why it exists. A reasonable practical explanation is that integrating AI takes more than access to a tool: it requires choosing a worthwhile workflow, connecting systems, assigning responsibility, and managing security and reliability. The evidence here does not quantify how much each of those factors contributes, or show that a particular intervention will close the gap.
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This distinction matters for business owners. A low adoption rate does not mean a firm should adopt AI for its own sake. It means there is room to assess whether a specific task can be improved, and whether the expected benefit justifies the effort and risk of changing the workflow.
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What role can an MSP play?
An MSP can act as a practical adviser and operator when a small business has a real use case but lacks the time or technical capacity to evaluate, connect, and maintain the necessary tools. That role can include:
- Mapping repetitive or information-heavy tasks and assessing whether AI is suitable for them.
- Choosing tools that fit the firm’s existing systems and requirements, rather than adding an isolated application without a clear purpose.
- Connecting AI tools to workflows and testing how people will review outputs and handle errors.
- Helping establish rules for access, sensitive data, human review, and escalation when a result is uncertain or wrong.
- Monitoring the systems and adapting them as business needs, products, or risks change.
Pax8 CEO Scott Chasin described the opportunity this way: “Most SMBs can’t build that alone. That’s the role of a managed intelligence provider (MIP): a technology partner who deploys AI, governs it, and keeps it running securely alongside their people.” This is a vendor executive’s perspective, not an independent finding that every SMB needs such a provider. Pax8 survey announcement
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Does the evidence show that MSPs are already earning from AI?
Not broadly, based on the available provider survey. Kaseya’s 2026 survey of more than 1,000 MSPs worldwide found that 48% ranked AI and automation as the top client need for 2026, while 13% said AI services were already a meaningful revenue source. The same survey reported that 53% of MSPs were using AI internally for ticketing, patching, and monitoring. These are provider-reported results, not a representative survey of SMB customers; client demand, internal provider use, and revenue already realized are different measures. Kaseya, 2026
The figures point to an emerging service area, not proof that MSPs have already built a major revenue stream or that advice from an MSP causes better adoption or business results. A provider considering this work should be able to explain the proposed use case, the business’s role in approving outputs, the security controls involved, and how the system will be maintained—not merely offer access to an AI tool.
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How should a small business decide whether to get help?
An MSP is one possible source of help, not a prerequisite for using AI. The right choice depends on the complexity of the use case, the sensitivity of the data, and whether the business can safely manage the work itself.
- Start with a task, not a product. Identify a recurring problem and define what improvement would count as success.
- Check the risk and the workflow. Consider whether the task involves sensitive information, consequential decisions, or outputs that require expert review.
- Decide what capability is missing. If the challenge is simply learning a standalone tool, outside implementation may be unnecessary. If it involves systems integration, access controls, security, or ongoing monitoring, a qualified adviser may be useful.
- Agree on accountability. Clarify who owns the process, approves changes, reviews results, and responds to errors or service interruptions.
For an MSP engagement, a sensible first step is a bounded pilot with a defined workflow and review criteria. The business should retain responsibility for its decisions and data; the provider’s role is to help implement and operate the technology within agreed limits.
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