Credit Unions See AI’s Promise, but Readiness Lags Adoption

Artificial intelligence (AI) has the potential to help credit unions scale personalization, improve fraud detection and streamline operations, but only if the foundations beneath those tools are solid.

The opportunity is clear. AI has moved rapidly from novelty to necessity across financial services, and credit unions are not immune to those shifts. More than half of consumers already use AI for financial planning or budgeting, and four in 10 say they would feel comfortable using AI to complete financial transactions, according to the January 2026 Credit Union Tracker Series from PYMNTS Intelligence and Velera. Adoption is even higher among younger members, placing pressure on credit unions to modernize while preserving trust.

Yet harnessing AI at scale remains difficult. While interest is widespread, most credit unions remain in early or selective stages of adoption. Only a small minority have deployed AI broadly across their organizations, creating a widening gap between what members expect and what institutions can reliably deliver.

Why AI Momentum Breaks Down

The report finds that this gap is not driven by resistance to AI itself. Instead, AI stalls when the underlying conditions are not in place. Data quality, governance and system connectivity consistently emerge as the main constraints preventing pilots from becoming enterprise capabilities.

Across the industry, credit unions acknowledge the long road ahead. Roughly half of credit union leaders say they are only somewhat familiar with AI applications, and just 17% describe themselves as very familiar. About 42% report implementing AI in specific areas of operations, but only 8% use it across multiple facets of the organization. That imbalance highlights how experimentation has outpaced readiness.

The challenges are well defined:

Data isn’t ready: Many credit unions lack a unified data strategy, limiting AI’s usefulness regardless of model sophistication.

Decisions aren’t explainable: Black-box outcomes undermine confidence with regulators, staff and members alike.

Systems can’t connect: Legacy cores and fragmented platforms keep AI initiatives stuck in pilot mode.

As one industry executive cited in the report observed, “A lot of credit unions do not have a data strategy; a lot of them don’t have their data in a place where it can be accessed readily. And so, no matter how much AI we apply to that, it won’t do much good if we don’t have our fundamentals in place around data.”

These structural barriers matter because trust sits at the center of the credit union model. Unlike large banks that can absorb reputational shocks, credit unions operate with thinner buffers between frontline experience and member confidence. As AI expands into lending, fraud prevention and member service, the ability to explain and defend automated decisions becomes non-negotiable.

From Pilots to Practice

Integration presents another hurdle. More than eight in 10 credit unions cite integration with existing systems as a major obstacle to AI adoption, according to the report. Without clean connections between data sources, analytics platforms and core systems, AI tools struggle to move beyond isolated use cases.

The path forward, however, is not abstract. The report points to practical steps credit unions can take to address these constraints. Strengthening data readiness and governance early allows AI-assisted decisions to be explainable, auditable and aligned with cooperative values. Prioritizing high-trust, high-impact use cases such as fraud prevention and member service helps deliver value without forcing abrupt change.

Partnerships also play a central role. Shared intelligence models and credit union service organizations can reduce integration complexity while improving accuracy and transparency. By pooling data across institutions, credit unions can gain the scale needed to make AI both effective and defensible.

Education and transparency complete the equation. Members increasingly expect guidance on how AI works and how their data is used. Credit unions are uniquely positioned to provide that context, reinforcing trust while supporting adoption.

AI, in other words, is no longer a question of if for credit unions. The challenge is whether data, explainability and integration can advance fast enough to meet rising expectations.

At PYMNTS Intelligence, we work with businesses to uncover insights that fuel intelligent, data-driven discussions on changing customer expectations, a more connected economy and the strategic shifts necessary to achieve outcomes. With rigorous research methodologies and unwavering commitment to objective quality, we offer trusted data to grow your business. As our partner, you’ll have access to our diverse team of PhDs, researchers, data analysts, number crunchers, subject matter veterans and editorial experts.