Small Business AI Adoption: Why Most SMBs Are Still at Level 1

Many small and midsize businesses are experimenting with AI. Far fewer are using it to improve how the business actually runs. That gap is where the real opportunity sits.

Why Small Business AI Adoption Still Stalls at Level 1

David Schonthal’s recent Inc. article on the four stages of AI adoption makes a useful observation: many SMBs are still stuck at the earliest stage. They are trying tools, testing prmpts, generating content, and getting familiar with the technology. That counts as adoption. But it is still only the beginning. The businesses that stop there will get some convenience. The businesses that move beyond that stage will build advantage.

That matters because small business is not a side story in the U.S. economy. The U.S. Small Business Administration reports there are 36.2 million small businesses in the country, and they account for 45.9% of private-sector employment. At the same time, the U.S. Chamber of Commerce reported in 2025 that 58% of small businesses say they are using generative AI, up from 40% in 2024 and 23% in 2023. AI awareness is here. Tool usage is here. But meaningful operational adoption is still uneven. [ https://advocacy.sba.gov/2025/06/30/new-advocacy-report-shows-the-number-of-small-businesses-in-the-u-s-exceeds-36-million/ and https://www.uschamber.com/technology/artificial-intelligence/u-s-chambers-latest-empowering-small-business-report-shows-majority-of-businesses-in-all-50-states-are-embracing-ai ]

That is the point many leaders miss.

The real divide is no longer between businesses that have heard of AI and businesses that have not. It is between businesses using AI casually and businesses using AI deliberately.

A lot of SMBs today are using AI like an assistant around the edges. They use it to draft a post, rewrite an email, summarize notes, brainstorm ideas, or clean up language. There is nothing wrong with that. Those are useful applications. But they do not usually create real separation in the market. They save bits of time. They do not meaningfully redesign the business.

That redesign is where the value starts.

For most SMBs, the important question is not, “Are we using AI?” The important question is, “Where does AI improve a recurring workflow that affects revenue, responsiveness, consistency, or operating capacity?”

That is a much harder question. It is also the one that matters.

A business does not gain much long-term advantage from using AI once in a while to make marketing copy sound better. It gains advantage when leads get handled faster, follow-up becomes more consistent, repetitive customer questions are answered more efficiently, proposals move faster, internal knowledge is easier to access, and administrative drag starts coming out of the system.

That is the shift from experimentation to application.

And most SMBs are still early in that shift - or not even there.

This pattern shows up in broader small-business technology adoption as well. NFIB’s 2025 Small Business and Technology survey found that while many owners have adopted newer technologies, the most common benefit was simply staying competitive. Far fewer said the technology gave them a clear edge. That is exactly the risk with AI. A business can adopt it just enough to keep up, but not well enough to create advantage.

That is why so many SMBs feel busy with AI but do not yet feel transformed by it.

The issue usually is not effort. The issue is approach.

Too many businesses start with the tool.

They ask which model to use, which app to subscribe to, which assistant to try, or which automation platform is getting the most attention. Those are not useless questions, but they are not the best starting point. The right place to begin is not the tool stack. It is the bottleneck.

Where is the business slow?

Where is it inconsistent?

Where is labor being spent on repetitive handling?

Where are leads cooling off while someone means to follow up later?

Where are employees recreating the same answer, the same email, the same explanation, or the same internal lookup over and over again?

That is where AI belongs.

For a clinic, that might be inbound inquiry handling.

For a professional services firm, it might be proposal drafting, follow-up sequencing, or meeting-summary-to-action conversion.

For a local service business, it might be lead response, quoting support, appointment-related communications, or internal SOP lookup.

For an owner-operator, it might simply be reducing the number of routine decisions and repetitive drafting tasks that consume attention every week.

These are not glamorous use cases. They are better than glamorous use cases. They are useful.

That is the lens more SMBs need.

The businesses that get the most from AI over the next few years will not be the ones chasing every new model release. They will be the ones that identify one or two high-friction workflows and improve them in a disciplined way.

That discipline matters.

A smart SMB AI initiative usually starts with one workflow that is repetitive, important, and measurable. It should connect clearly to a business outcome. Faster lead response. Higher follow-up completion. Shorter turnaround time. Lower admin burden. More consistent customer communication. Better throughput without adding headcount.

Then it needs a metric.

Without a metric, most AI efforts drift into vague optimism. People feel productive. Leadership feels enthusiasm. Nothing is actually being measured. That is how teams end up saying AI is “promising” for a year without being able to point to any operational result.

For an SMB, the goal should be simpler than that.

Pick one meaningful workflow. Define one concrete success measure. Improve it. Then expand.

Just as important, keep human review where judgment matters.

For most SMBs, the right answer is not fully autonomous AI. It is structured AI support with human oversight. AI can draft, classify, summarize, suggest, extract, route, and organize. Humans should still review where trust, accuracy, tone, compliance, or nuance matters. That model is usually faster to implement, easier to manage, and much safer for the business.

Done well, that is enough to create leverage.

And once one workflow works, adjacent workflows become easier to improve. Better lead response can extend into better follow-up. Better follow-up can extend into better scheduling communication. Better internal knowledge access can extend into faster onboarding and more consistent customer handling. One improvement often unlocks another.

That is where compounding begins.

This is why the current moment matters.

AI adoption among small businesses is rising quickly, but the market is still early enough that disciplined operators can gain ground. Many SMBs are using AI. Far fewer are systematically applying it to the operational areas where it can produce measurable business value. That gap will not stay open forever.

For leaders of small and midsize businesses, the takeaway is not that they need a massive AI transformation program. In most cases, they do not.

They need a clearer operating lens.

They need to know where AI belongs, where it does not, where human review still matters, and which workflows are worth improving first.

That is the real work.

At JKS Advisory, that is exactly where we focus: helping SMB leaders move past generic AI enthusiasm and identify practical, workflow-level opportunities that can improve responsiveness, consistency, and operating leverage without overengineering the business.

Because most SMBs do not need more AI noise.

They need better decisions.

And the businesses that make those decisions well will not just “use AI.”

They will quietly build an edge with it.