Credit Union Chatbot Adoption Jumps From 3% to 46%

Artificial intelligence (AI) may be climbing the strategic agenda at credit unions, but the most consequential gap is no longer one of awareness. It is the widening distance between what business members are asking for and what their financial institutions are prepared to deliver.

Findings in the June/July 2026 edition of the Credit Union Tracker® Series, a PYMNTS Intelligence collaboration with Velera, show that 75% of small- to medium-sized businesses (SMBs) say they would use at least one AI feature offered by their financial institution within the next two years. Among businesses generating more than $1 million in annual revenue, that share rises to 83%.

But, per the report, the SMB demand for AI is not primarily for autonomous agents that move money, select financial products or make consequential decisions without human involvement. Business owners are looking for something more immediate: tools that help them understand their financial position, reduce administrative complexity and make better decisions.

That distinction gives credit unions a more practical AI strategy. Rather than trying to leap directly into autonomous banking, they can begin with advisory tools that make members more capable before attempting to make banking more automated.

The Demand for SMB Business Finance AI Is Practical, Not Futuristic

The small-business AI market is often framed around automation: software that reconciles accounts, pays invoices, forecasts demand or performs work previously handled by employees. But when SMBs are asked what they want from their financial institutions, their priorities are more grounded.

Thirty-one percent are interested in AI-powered expense tracking. Another 22% want assistance with budgeting, cash-flow management, supplier discovery and financial-product comparisons. These are not moonshot applications. They are digital extensions of the financial guidance business owners already expect from a trusted banking relationship.

The pattern also reveals something important about how SMBs assign value to AI. Business owners appear more willing to use the technology when it improves the quality and speed of a decision than when it removes them from that decision entirely.

Although 76% of small businesses reportedly use AI in some capacity, only 14% say it is fully integrated into their operations. The gap suggests that adoption is broad but still largely incremental. Starting with expense insights, cash-flow alerts or conversational financial guidance allows an institution to demonstrate value without immediately confronting the operational, compliance and trust challenges associated with autonomous execution. It also mirrors the way many SMBs are adopting AI internally: through targeted applications rather than companywide transformation.

Read the report: The Practical AI Opportunity: Why SMBs Want Guidance Before Automation

That creates a hierarchy of adoption. At the bottom are low-risk applications that organize information, identify patterns or answer questions. Higher up are systems that recommend actions. At the top are autonomous tools that execute transactions or make decisions on a company’s behalf. Credit unions do not need to begin at the top.

Chatbot adoption among credit unions rose from 3% in 2019 to 46% in 2026, indicating that deployment is accelerating. PYMNTS Intelligence estimated that by 2029, nearly half of top-tier credit unions will offer AI-powered chat and financial-advice capabilities, with middle-tier institutions narrowing the gap.

The strongest near-term opportunity is conversational AI that works less like a generic chatbot and more like a financial interface. A conventional banking chatbot typically helps users locate information: a routing number, branch location or transaction record. An advisory system can interpret information. It might explain why cash balances declined, identify an unusual expense pattern or show how an upcoming payment could affect short-term liquidity.

The difference is not merely better customer service. It is a shift from making financial data available to making that data understandable. It is also a more credible starting point than presenting AI as an all-purpose transformation.

For SMB members, the progression is likely to be sequential: first help me see; then help me decide; and only later, help me act. Credit unions that follow that sequence can turn their current AI deployment gap into a practical road map.