A new survey from Stax, a small-business payment company that competes with banks for merchant accounts, found that 80% of small and midsize business technology decision-makers have used artificial intelligence for work they otherwise would have paid a consultant or professional to perform. The finding comes from a June 2026 survey of 500 U.S.-based SMB technology decision-makers, and it lands as banks, card networks, and payment companies race to build AI into small-business financial products.
The number is striking, but it needs careful reading. Stax sells payment and business-management services to the same customer base it surveyed, and the published results do not disclose a margin of error, field methodology, or how respondents were distributed by business size and industry. The survey shows enthusiasm for AI-enabled cost savings; it does not independently prove that companies are making better decisions, saving verified dollars, or replacing consultants at the same rate across the broader small-business economy.
The sharper angle is human, not just operational. If small businesses increasingly turn to AI for work that used to go to accountants, lawyers, marketers, insurance advisors, bookkeepers, or niche consultants, the immediate savings may come with a quieter cost: fewer expert relationships, less local professional judgment, and more pressure on owners to trust machine-generated advice without the human context that used to come with it.
Stax Survey Shows High AI Use, but the Sample Has Limits
Stax surveyed 500 U.S.-based small and midsize business technology decision-makers in June 2026, asking about AI adoption, business applications, perceived benefits, trust considerations, and future investment plans. The company said 80% of respondents expect their organization’s AI use to increase over the next six months, while 96% said AI helps them make business decisions more quickly, 95% said it increased their confidence in decision-making, and 94% said it helped them better serve customers.
Those figures describe self-reported perceptions, not measured outcomes such as revenue growth, error rates, avoided consulting spend, or customer retention. That distinction matters because confidence can rise even when accuracy does not. A business owner may feel more decisive after using an AI tool without knowing whether the recommendation is legally sound, financially complete, or appropriate for the company’s specific circumstances.
The survey also aggregates businesses that may have very different risk profiles. Stax CEO John Cimba told American Banker that larger businesses generally have more tolerance for AI experimentation because they have technology staff to manage rollout, while smaller businesses cannot afford a mishap. That caveat undercuts any simple reading of the topline result: the smallest operators may have the most to gain from lower-cost advice, but also the least capacity to detect a wrong answer before it becomes expensive.
Small Businesses Are Using AI First Where Mistakes Are Easier to Fix
The survey shows a clear risk gradient in how SMBs use AI. Marketing and content creation was the most common use case at 61%, followed by graphic design at 49% and business planning at 45%. These are areas where mistakes are often visible, reversible, and relatively low-stakes.
Adoption drops sharply in functions where professional expertise carries legal, financial, or compliance consequences. Human resources work and accounting and tax preparation each came in at 25%, legal services at 22%, and insurance at 16%. That pattern suggests small businesses are not rejecting AI broadly. They are sequencing adoption by risk, starting with tasks where a bad draft can be revised and moving more slowly in areas where a bad answer can trigger penalties, litigation, or denied claims.
That caution is the article’s most important signal. The survey does not show owners blindly handing core professional functions to AI. It shows them using AI aggressively where it saves time, while still hesitating to replace human experts in areas where accountability matters most.
The Human Cost: Losing Judgment, Accountability, and Local Expert Networks
The Stax finding is framed as a productivity story, but it also points to a negative human impact that should not be ignored. Consultants and experts do more than produce a deliverable. A bookkeeper notices when cash-flow problems reflect a pricing issue. A lawyer asks whether a contract term fits the owner’s risk tolerance. A benefits advisor sees whether a policy change will affect employee retention. A marketing consultant may know the local customer base in a way a generic model does not.
When AI replaces those relationships, the business may save money in the short term but lose an external source of judgment. That matters most for small firms, where owners already make decisions with limited staff and limited time. A large company can ask an in-house lawyer, controller, or HR specialist to check an AI output. A five-person retailer or contractor may treat the output as the only available answer because the human alternative is too expensive.
There is also a community-level effect. Local accountants, tax preparers, designers, HR consultants, and independent advisors often serve the same small-business ecosystem that banks and payment companies want to automate. If routine advisory work shifts to AI tools bundled into payment or banking platforms, some of that local professional capacity may weaken. The survey does not measure consultant job losses or revenue declines, so that conclusion should not be overstated. But the displacement risk is logically connected to the survey’s central claim: if 80% of respondents have used AI for work they otherwise would have paid a professional to do, some paid human work is being deferred, reduced, or replaced.
Banks and Card Networks Are Building Around the Same Trend
Banks, payment companies, and card networks are already moving toward AI-assisted financial tools for SMBs. Visa and Mastercard have introduced products aimed at card issuers and business clients, including agentic AI tools and AI-assisted dispute workflows. Mastercard has described its Virtual C-Suite as a way to bring executive-level intelligence into the accounting systems, business software, and banking applications that small businesses already use.
Visa has separately emphasized integrated small-business banking experiences that combine payments, lending, deposits, and cash management. American Banker’s reporting cited Visa data showing that 44% of small-business bank revenue globally comes from cards and that average small-business revenue in North America is more than five times average consumer revenue. Those numbers help explain why financial providers are investing in SMB-specific AI rather than simply repackaging consumer chatbots.
The competitive logic is clear: whoever controls the payment account, the banking dashboard, or the card relationship can become the place where owners receive advice. That creates convenience, but it also raises a human-support question. If a bank or processor markets AI as a substitute for expert guidance, small-business owners need to know where human escalation begins when the recommendation is wrong, incomplete, or conflicted with the provider’s commercial interest.
AI Can Lower Costs, but It Can Also Shift Responsibility Onto Owners
The clearest opportunity in the Stax findings is cost avoidance. A business that cannot afford a marketing agency, design consultant, or business-planning advisor may still be able to produce usable first drafts with AI. For owners who have historically gone without help, that can expand access rather than replace a human relationship that already existed.
The concern is what happens when the tool moves from drafting support into expert substitution. If an AI-generated cash-flow forecast misses a tax obligation, if an HR answer misstates a state-law requirement, or if a legal summary omits a contract risk, the business owner remains responsible. The provider may present the tool as assistance, not professional advice, leaving the owner to absorb the consequences of trusting it too far.
This is where replacing experts with AI can have a double human cost. The consultant loses work, and the owner loses a person who could be questioned, challenged, or held accountable. AI may make advice cheaper, but it can also make responsibility more diffuse.
Data Quality and Human Review Remain the Practical Barriers
Andrew Jamison, chief executive of small-business payment company Extend, told American Banker that businesses want AI tools that connect directly to accounting software and pull in more data, giving owners a view beyond the profit-and-loss statement. That vision assumes the business already has clean, current, connected financial records. Many smaller operators still rely on spreadsheets, paper receipts, or fragmented point-of-sale exports, which means the AI’s output can only be as reliable as the data feeding it.
That makes human review essential, especially in the higher-risk categories where adoption remains low. A bank or payment provider offering AI for payroll, tax, credit, dispute resolution, or insurance should be able to explain what human review exists, whether an owner can reverse an AI-generated recommendation, and whether the tool’s output is advice, automation, or a decision that affects the customer’s account.
The best use case is not replacing experts everywhere. It is using AI to prepare, organize, and summarize information so that a human advisor, banker, accountant, or owner can make a better decision faster.
What Operators Should Ask Before Replacing a Professional With AI
Small-business owners comparing AI-enabled banking or payment tools should treat the Stax survey as a prompt for due diligence, not as a mandate to automate professional advice.
- Separate low-risk from high-risk functions. Use AI freely for drafts, brainstorming, basic marketing, and internal summaries, but apply more scrutiny to legal, tax, insurance, payroll, credit, and compliance work.
- Keep a human escalation path. Before relying on an AI feature, confirm whether a human banker, support specialist, accountant, or advisor can review the output before it affects a loan, payment, dispute, or filing.
- Ask whether the tool is replacing advice or preparing it. A summary that helps an accountant work faster is different from a tool that tells the owner to skip the accountant entirely.
- Confirm data-use terms in writing. If a banking or payment tool connects to accounting software, ask what data it accesses, whether the provider uses that data for model training, and how long it is retained.
- Check fees and bundling. AI tools may be included, sold as add-ons, or embedded in higher account tiers. Owners should know the pricing model before building workflows around the feature.
- Preserve expert relationships for consequential decisions. Even if AI reduces routine consulting spend, businesses should maintain access to qualified professionals for decisions that create legal, tax, employment, insurance, or financing exposure.
- Track actual outcomes. Measure whether AI reduces time, errors, outside spending, or customer response delays. A tool that increases confidence but not accuracy should not be treated as a professional substitute.
Indicators to Watch
- Independent adoption benchmarks. Watch whether the Federal Reserve, NFIB, SBA, or other neutral sources publish AI-adoption figures that can be compared with Stax’s 80% consultant-replacement claim.
- Movement in high-risk categories. Follow whether accounting, tax, legal, insurance, and HR adoption rises above the 16% to 25% range Stax reported.
- Human support disclosures. Banks and payment providers should specify when AI outputs receive human review and when they do not.
- Professional displacement evidence. Look for data from local consultants, bookkeepers, tax preparers, and marketing advisors showing whether AI-bundled banking tools are reducing paid advisory work.
- Verified outcome data. The key question is whether AI improves decision quality, not whether respondents feel more confident using it.
AI Should Support Expertise, Not Quietly Replace Accountability
Stax’s research points to a real shift: small businesses are already using AI for work they once would have assigned to consultants or experts, and financial providers are building products around that behavior. The efficiency opportunity is real, especially for owners who could not previously afford outside help.
The risk is that the same efficiency story can obscure the human value being displaced. Consultants and experts provide judgment, context, accountability, and relationships that a bundled AI tool may not replace. For small businesses, the strongest path is not rejecting AI or treating it as a complete substitute for professional advice. It is using AI to reduce routine work while keeping humans responsible for decisions where a mistake can harm the business, its employees, or its customers.
