JUDI.AI CEO Gord Baizley was recently featured in York PR’s The State of Fintech 2026 report.

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“Start with the problem, not the technology.”
-Every AI pundit, everywhere

Go to any fintech conference or read any fintech publication and you’re in for a steady stream of discourse about AI. Some of it is instructive, while some might be aptly described as navel gazing.

For credit unions, AI is front and center in executive boardrooms and budget conversations across the country. Adoption efforts are ramping up, focusing heavily on areas like customer service (chatbots), fraud detection, risk assessment and credit decisioning.

While there is plenty of opportunity for AI to streamline processes and improve the member experience, the data suggests that we are still early in the adoption journey.

• 65% of credit union leaders are exploring agentic AI, but only 18% report mature, enterprise-wide deployments. (1)
• 40% of small FIs under $10B in assets are piloting generative AI, compared to 75%+ among larger banks. (1)
• Current AI deployments with credit unions include chatbots (45%), machine learning (33%) and generative AI (36%). (2)

In speaking with credit union leaders, I see several common threads emerging around AI adoption at the enterprise level. Resources are finite. Access to data is complex. Change management and reskilling remain a challenge. The market is moving quickly. I can certainly empathize, as we are grappling with many of the same dynamics in our organization.

Rather than sit on the sidelines spinning your wheels, consider a more grassroots approach with the resources you do have. Pick a small use case and map the existing process to identify potential bottlenecks where AI can help. Take credit decisioning, for example. According to Filene (3), 66% of credit unions are planning to apply AI to credit decisioning, making it the most active investment category across the ecosystem.

Our clients are finding traction by starting small. For example, with a strategic mandate to adopt AI within the organization, a $1B credit union in Florida wanted to streamline small business loans up to $50K and enable borrowers to apply online.

With our AI cash flow underwriting platform, the team replaced the manual collection of a business borrower’s financial statements with the automated collection and categorization of real-time bank transaction data to evaluate creditworthiness. This approach has trimmed the loan application and decisioning process from 7+ days to less than 10 minutes.

The team of five went live in 8 weeks with a total time commitment of about 8 hours, with plans to extend the program to the branch network. With predictive AI working behind the scenes to support underwriting, the team now has a quick win they can showcase internally and build on moving forward.

As Cornerstone Advisors notes, “A streamlined loan approval process that cuts decisioning time from days to hours may be more transformative than a flashy chatbot that handles 2% of inquiries. The question is not ‘Is it innovative?’, but ‘Does this remove a meaningful obstacle to growth?’” (4)

Agentic AI and generative AI may be dominating the headlines, but don’t overlook the potential of predictive AI models to tackle one small problem at a time.

1. Multimodal, The State of Agentic AI in Credit Unions: 2025 Insights, December 8, 2025
2. Cornerstone Advisors, What’s Going On in Banking 2025
3. Filene, The AI Adoption Journey: A Survey of Credit Union Leaders, January 30, 2025
4. Cornerstone Advisors, Next-Level Growth: Credit Union Opportunities in a Changing Market