Why 57% of Small Businesses Are Investing in AI — And What the Other 43% Are Waiting For

What the Investors Are Actually Doing

The small businesses leading the charge aren’t building proprietary AI models. They’re doing something far more practical: identifying the specific places in their operations where AI removes friction, then implementing it deliberately.

The most common use cases, according to Gusto’s 2025 small business survey, cluster around three categories. First, customer communication — using AI to draft responses, personalize outreach, and handle first-contact inquiries. Second, internal operations — automating the reporting, data entry, and document work that consumes hours every week. Third, business intelligence — using AI to surface patterns in sales data, customer behavior, and operational performance that would take days to find manually.

What these use cases share is that they’re all about reclaiming time. Most small business owners aren’t investing in AI because it’s exciting technology. They’re investing because their team is stretched, their margins are tight, and any tool that gives them two hours back per employee per week compounds into something meaningful.

Why the 43% Are Hesitating — and What They’re Getting Wrong

The reasons the other 43% haven’t moved tend to fall into three categories. The first is uncertainty about where to start. When every vendor is pitching AI and every headline is about disruption, it’s genuinely hard to know which problem to solve first. The hesitant businesses aren’t opposed to AI — they’re overwhelmed by the options.

The second reason is fear of getting it wrong. Small businesses don’t have the capital to absorb expensive experiments. If an AI tool costs $500 a month and doesn’t deliver, that’s a real budget impact. The concern isn’t irrational — it’s a rational response to a market full of oversold software.

The third reason is a belief that AI requires technical infrastructure they don’t have. This one is mostly a myth. The most impactful AI implementations for small businesses right now don’t require custom models, data science teams, or expensive enterprise platforms. They require clear thinking about which problem to solve and someone who knows how to configure the right tools for that specific problem.

The Cost of Waiting

Here’s what often gets overlooked in the AI conversation: inaction has a cost too. Research from Harvard Business School and Stanford shows that knowledge workers using AI tools can complete tasks 25–40% faster — and in some cases produce measurably higher quality output. For a business where every team member wears multiple hats, a 25% efficiency gain isn’t a nice-to-have. It’s the difference between a growth quarter and a treading-water quarter.

More concretely: every week your competitors are using AI to move faster, serve clients better, and generate insights you’re generating manually, the gap widens. The window for competitive advantage through early adoption is closing. It hasn’t closed — but it will.

The Right Starting Point Isn’t the Coolest Tool

The businesses seeing the best returns from AI investment share one thing: they started with a problem, not a product. They didn’t ask “what can ChatGPT do for us?” They asked “where are we losing the most time to manual work, and what would it be worth to fix that?”

That’s the question worth sitting with. Because the answer almost always points to a workflow that’s measurable, improvable, and ready for automation. Start there. Get one win. Then build.

The 57% figured this out. The other 43% can too — they just need to take the first step with intention instead of waiting for certainty that isn’t coming.