Small Business AI Needs Less Hype and More Strategy

Artificial intelligence has moved from an emerging technology to a routine part of small-business operations in remarkably little time. The U.S. Chamber of Commerce reported in 2025 that 58% of small businesses were using generative AI, up from 40% in 2024 and 23% in 2023. The acceleration has made AI adoption itself a less pressing question. The harder question is what businesses are actually doing with it.

Much of that activity remains visible at the surface of the business. Owners are using generative AI models to improve emails, create marketing material, build website copy, or test ideas, while AI-enabled features are increasingly appearing inside everyday software. The U.S. Chamber's 2026 report points to a growing divide between experimentation and implementation.

The stakes are operational. A missed customer call can mean a missed sales opportunity. A repetitive administrative task can consume hours that could have been spent on revenue-generating work. A process that requires several manual handoffs can create friction every time a customer moves through it. AI may be capable of addressing such problems, yet the technology cannot identify every business-specific bottleneck on its own.

The proliferation of tools can make the problem harder to see. Nearly all small businesses surveyed by the U.S. Chamber use at least one technology platform, while 58% reported using four or more technologies in their operations. Adding another tool without understanding how those systems function can create another layer of complexity instead of solving the underlying problem.

Tanya Ferguson, founder of Clearbrook AI Solutions, approaches that implementation question through the operational structure of a business. Her perspective is shaped by two decades of experience in insurance and business leadership, where understanding how an organization functions is inseparable from understanding where it can improve. Her approach starts with the business itself, then determines where technology can contribute.

"It's incredibly important that we don't start with AI tools," Ferguson explains. "We don't teach AI, we start with the business itself and learn how it operates. From there we find where the gaps are and where technology can fill those gaps, only then do we implement AI, allowing the business owner to remain a business owner."

Her concept of an operational blind spot provides a useful way to understand the implementation gap. Ferguson compares it to driving before blind-spot indicators became standard. Before blind-spot indicators became standard, changing lanes meant taking the eyes off the road for a moment to check what was beside or behind the car. The risk was always there, but drivers had little way to see it without physically looking. A small warning light on the side mirror changed that by making the blind spot visible. Ferguson sees AI creating a similar shift for businesses. Established processes may have worked for years simply because they were the way the business had always operated. New technology can now reveal opportunities within those processes that were previously easy to overlook.

One example she points to involves a medical office. New clients previously provided information over the phone, received paperwork by email, completed it, and returned it. Ferguson's team identified an opportunity to consolidate those steps into one digital process aiming to reduce the time spent. In another case, a physical therapist was preparing to hire administrative support to manage scheduling and related tasks. After operational systems were put in place, the business owner was able to redirect that planned expenditure toward hiring another physical therapist, creating additional capacity for growth.

Operational AI does not always arrive as an obvious, sophisticated system. Ferguson points to something as simple as an unanswered business call and the decline of customers leaving voicemails. A customer who receives an immediate text after a missed call has another opportunity to continue the conversation. Without that response, the business may never know whether the caller represented a minor inquiry or a significant potential client.

Ferguson argues that businesses should first understand the path a lead takes from its initial contact through payment and fulfillment. Repetitive tasks can then be examined for opportunities to automate, while more complex processes require greater scrutiny and, in some cases, specialist implementation.

Her warning about technology accumulation is equally direct. She explains, "Slow down before buying more tools. You can end up with a tech stack so large that you don't know what each tool does, how they work together, or whether they are actually solving a problem in your business."

The issue becomes particularly important as companies grow. Ferguson identifies periods of expansion, hiring, or increasing owner dependence on the business as moments when operational blind spots can become more costly. A process that consumes manageable time for one employee can become an expensive constraint once the same process is repeated across a larger operation.

AI adoption, in that sense, is entering a more consequential phase. The U.S. Chamber found that 77% of small businesses using AI believe limits on the technology would negatively affect their growth, operations, or bottom line, illustrating how closely the technology is becoming tied to business performance.

In Ferguson's view, the next step is not mastering every new AI application, but understanding the business well enough to identify where technology can remove friction, recover lost opportunities, or create capacity. She remarks, "You're not behind. Understand your business first before you think about implementing AI into it at all."