Artificial intelligence is spreading rapidly through the backbone of the American economy — its small businesses — but the tools are outpacing the people wielding them. According to Thryv's 2026 AI and Small Business Adoption Survey, AI adoption among U.S. small businesses has climbed to 66%, up from 55% just twelve months prior. That eleven-percentage-point surge in a single year is remarkable by any measure. Yet the same survey surfaces a troubling counterweight: seven in ten small business owners acknowledge they need more training to use artificial intelligence effectively. The combination of accelerating adoption and persistent under-preparedness is fast becoming one of the defining tensions of the current technology cycle.
A Surge Driven by Tangible Results
The rise from 55% to 66% adoption in a year is not incidental. Small business owners are pragmatic operators who invest in tools only when those tools demonstrably move the needle. Thryv's survey confirms that this is precisely what is happening: despite the acknowledged skills gap, small businesses report experiencing concrete benefits from their AI usage. That finding matters enormously for how policymakers, lenders, and technology vendors should interpret the data. This is not adoption driven by hype or peer pressure alone — real operational gains are compelling owners to integrate AI into their workflows even before they feel fully equipped to do so.
The categories where those benefits manifest are consistent with what larger enterprises have reported for several years: time savings on administrative tasks, improved customer communication, faster content generation, and more responsive scheduling and billing workflows. For a sole proprietor or a team of five, reclaiming even two hours per week through automation can translate directly into revenue or personal capacity — an outcome that justifies the learning curve, however steep it may feel in the moment.
The Skills Gap Is Not a Reason to Pause — But It Is a Warning
The 70% figure demanding attention here is not merely a measure of discomfort with new software. It signals a structural readiness problem that, left unaddressed, could widen economic inequality between small businesses that receive adequate support and those that do not. When the majority of adopters self-report insufficient training, the risk profile of AI integration rises. Poor prompting, misinterpreted outputs, and over-reliance on automated recommendations without human verification are not hypothetical failure modes — they are the predictable consequences of deploying powerful tools without appropriate literacy.
In the financial services context specifically, where small business owners increasingly use AI to generate invoices, manage cash flow forecasting, or interact with banking platforms equipped with AI-powered advisors, the stakes of that literacy gap are amplified. A misunderstood AI output in a customer service context is correctable. A misunderstood AI output in a lending application or tax filing workflow can carry material financial consequences. The survey's findings should prompt banks, fintech platforms, and small business software providers alike to treat embedded training — not as a feature footnote — but as a core product obligation.
What the Industry Owes Small Business Operators
The fintech and banking sector has been among the most aggressive in rolling out AI-powered tools aimed at small and medium-sized enterprises, from automated bookkeeping to intelligent cash flow dashboards and AI-assisted loan underwriting. Platforms serving this segment bear a particular responsibility to close the gap that Thryv's survey quantifies. Adoption rates climbing to 66% represent a market that has already accepted the premise of AI — the remaining work is ensuring that acceptance translates into competent, confident, and safe usage.
Training programs bundled directly into software interfaces, contextual help systems that explain AI-generated recommendations in plain language, and partnerships with community organizations to deliver financial AI literacy at scale are all levers available to the industry. Regulatory bodies have an emerging role here as well: as AI becomes embedded in the financial tools that small businesses use daily, guidance on transparency, explainability, and user education will need to evolve beyond enterprise-focused frameworks to reach Main Street operators.
What This Means
Thryv's 2026 data delivers a clear verdict on the state of AI adoption among U.S. small businesses: momentum is real, benefits are materializing, and the market has decisively crossed the majority threshold. The jump from 55% to 66% in twelve months is not a plateau — it is an acceleration. But that acceleration is carrying a significant portion of operators into territory they do not yet feel equipped to navigate. The 70% who acknowledge a training deficit are not failures of ambition; they are early adopters operating under conditions that the broader ecosystem — software vendors, financial institutions, and policymakers — has not yet fully supported. Closing that gap is not merely a matter of user experience improvement. It is a prerequisite for ensuring that the productivity gains AI promises for small business actually materialize equitably and sustainably across the economy.
Written by the editorial team — independent journalism powered by Codego Press.