Small businesses are having their AI moment — and it’s exposing a services gap

I have spent more than 30 years in enterprise IT and managed services. For most of that time, small businesses adopted new technology on a predictable lag. Large enterprises moved first because they had the budget, staff and risk tolerance to experiment. Everyone else waited for the tools to mature and prices to fall.

That pattern held during the first wave of workplace AI. The conversation among IT leaders centered on enterprise platforms, large service operations and multi-quarter programs connected to systems such as Workday. ServiceNow’s acquisition of Moveworks reinforced the enterprise direction of travel. Small businesses were mostly spectators.

They are not spectators anymore. Over the past six months, the requests coming into my practice have changed from, “Should we look at AI?” to, “Our employees already opened ChatGPT or Claude, and now we need help doing this properly.” That is not a product-selection question. It is an operating-model question.

The adoption line has moved down-market

The data now reflects what I am seeing with clients. Bluevine’s 2026 Small Business AI Trends Report found that 74% of small business owners are using or actively testing AI. In the owner survey, ChatGPT was used by 57%, Gemini by 56%, Copilot by 30% and Claude by 12%. But Bluevine’s customer transaction data tells a second part of the story: the number of Claude users grew 729% year over year and, by April 2026, Claude had surpassed ChatGPT in total monthly transactions among Bluevine customers. Claude therefore has a smaller reported user share in this survey, but exceptionally strong growth and engagement momentum.

A separate Business.com study found that 57% of U.S. small businesses were investing in AI, up from 36% in 2023, while only 30% of employees used it daily. Adoption also varied sharply by company size. The important signal is not a single headline percentage. It is the gap between buying access and changing how work gets done.

AI vendors see the same shift. As TechCrunch reported, Anthropic launched a small-business offering built around Claude Cowork and integrations with tools such as QuickBooks, DocuSign and HubSpot. The company pointed to a familiar market reality: small businesses account for 44% of U.S. GDP and employ nearly half of the private-sector workforce, yet tools and training have rarely been designed around how these businesses operate.

A different measurement shows why user share should not be confused with workload share. A 2025 Menlo Ventures survey of more than 150 technical leaders estimated that Anthropic held 32% of enterprise LLM usage, ahead of OpenAI at 25% and Google at 20%. In coding workloads, Anthropic’s share was 42% compared with OpenAI’s 21%. Menlo Ventures is an Anthropic investor, but the methodology and sample are disclosed in the report.

I read these findings alongside the Bluevine data rather than as a contradiction. The first measures which chatbot an owner reports using. The second estimates which models technical teams select for enterprise workloads. Together they suggest that Claude’s broad SMB awareness remains behind the largest consumer-facing tools, while its usage is stronger when organizations make a deliberate choice for production and coding work. Closing the gap between casual experimentation and considered adoption is precisely where workflow design, training and governance become valuable.

That last point matters. A 30-person company does not need a smaller version of a Fortune 500 transformation program. It needs a practical path from unapproved experimentation to a few repeatable, governed workflows that save time without creating new data risks.

Four stages of small business AI maturity

Across my client engagements, I see four distinct stages. Knowing the stage matters more than choosing the tool because each stage has a different problem to solve.

Stage one is shadow AI. Employees use personal accounts with no company visibility into what they paste into a chat window. Leadership may believe the organization has not adopted AI, but employees already have. A 2026 analysis of BlackFog research reported that 49% of workers use AI in ways their employers have not approved. This is not primarily an employee-discipline problem. It is a demand signal combined with a visibility problem.

Stage two is licensed but unstructured. The owner buys seats, shares a getting-started guide and assumes adoption will follow. It rarely does. Some employees experiment, others avoid the tool and the business eventually concludes that AI did not deliver. The investment-to-usage gap in the Business.com findings is consistent with what I see at this stage: access exists, but repeatable work does not.

Stage three is structured enablement. The business maps AI to real daily work and builds a small number of workflows around it. In one Northern California commercial real estate lending engagement, we did not teach loan officers to chat with an assistant in the abstract. We built a specific workflow: review an unread email, compare deal terms with the current rate sheet, draft a response and prepare a calendar hold. One workflow proved the value before we expanded.

Other functions followed the same pattern. General counsel used AI for a first review of leases and third-party documents, with the tool flagging unusual provisions and explaining why they deserved attention. Final legal judgment stayed with the attorney. Marketing converted one funded-deal summary into a blog post, social copy and an email blurb, then reviewed each item before publication. These are not dramatic use cases. They save real time because the tool adapts to the job instead of forcing the employee to become a prompt specialist.

We also set boundaries before expanding usage: no payroll or HR data, human review for anything client-facing or judgment-based and approved business accounts with appropriate contractual data protections. Those rules did not slow adoption. They gave employees confidence about what was allowed.

Stage four is governed and embedded. AI connects to the systems the business already uses, with identity controls, permissions, audit trails and clear ownership. Few small businesses I work with are fully there. Larger enterprises reached this stage first because compliance teams and security budgets pushed them toward it. Smaller firms may arrive because customers, regulators or insurers demand it. I would rather see them arrive by design than after an incident.

The services opportunity is bigger than the license

Most demand I see today is the move from stage two to stage three. That transition is a services problem. A license can be purchased in minutes. Moving a 30-person team from scattered experimentation to safe, repeatable workflows requires discovery, data-access decisions, training, governance and follow-through. Most small businesses do not have those capabilities in-house.

The managed-services market is beginning to recognize this. Coverage from The MSP Summit points to revenue opportunities in AI assessments, data-loss-prevention controls, guardrails and AI-inclusive service tiers. The opportunity is real, but providers should resist turning it into another software sale. A tool deployed without workflow ownership and governance simply creates a more expensive version of stage two.

My advice to MSPs and independent trainers is straightforward. First, identify the client’s maturity stage before scoping the work. A stage-one business needs visibility and policy before workflow automation. Second, begin with one recurring task that matters to the employee, not a generic demonstration. Third, build security and governance into the engagement from day one. Finally, measure independence rather than attendance.

Login counts and workshop participation reveal little. A better test is whether an employee can run the workflow correctly twice without help and explain it well enough to hand it to a colleague. That second test predicts whether the process will survive after the consultant leaves. It also creates internal champions, which small organizations need because they rarely have a dedicated AI training function.

This extends the principle I described in “Where AI fits — and where it doesn’t”: AI amplifies the foundation already in place. For a small business, identity access, clean data and clear ownership cannot be afterthoughts. They are part of the adoption work.

Two years ago, the enterprise-first pattern looked settled. Small businesses would eventually receive simplified versions of what large organizations had already proven, with a lag measured in years. This time, the lag is measured in months and the support infrastructure is not keeping pace. The gap between how quickly small businesses want to move and how safely they are prepared to move is the real opportunity. It will belong to technology leaders who treat AI adoption as a people, process and governance challenge — not merely a software purchase.