Artificial intelligence is showing up everywhere in the credit union space—vendor demos, conference sessions, boardroom discussions, and examiner conversations. With all that attention, some practical considerations can seem unclear.
I recently participated in a panel discussion for credit unions at the University of Maine Augusta, focused on stepping back and asking a simpler question: What actually matters for credit unions right now when it comes to AI?
Below are some of the main themes that were discussed:
Start With the Problem You’re Trying to Solve
The strongest AI efforts don’t start with technology—they start with a real need. Improving member experience. Reducing fraud losses. Giving staff better tools so they can spend less time on manual work.
When the “why” is clear, AI becomes a practical solution instead of an experiment. When it isn’t, projects tend to drift, stall, or quietly fade away.
Governance Can Be Practical (Not Painful)
AI governance sounds intimidating, but it doesn’t have to be. In most cases, it fits naturally into processes credit unions already have—risk management, vendor oversight, compliance, and internal controls.
Rather than creating something entirely new, many organizations are simply adding AI-specific considerations to existing frameworks. That approach keeps governance effective without slowing the business down.
Most AI Risk Lives with Vendors
For many credit unions, AI isn’t something being built internally—it’s embedded in third-party tools. That makes vendor conversations especially important.
Questions around data use, explainability, and accountability aren’t just “nice to have.” They help set expectations early and prevent surprises later, especially when regulators or auditors get involved.
Clear answers build trust. Unclear answers deserve a closer look.
Measure Success in Ways Everyone Understands
AI delivers the most value when success is measured using familiar metrics. Cost savings, efficiency gains, fraud reduction, faster turnaround times—these are outcomes leaders already care about.
When results are visible and easy to explain, AI becomes part of normal operations rather than a special initiative that needs constant justification.
Transparency Goes a Long Way
Regulators aren’t expecting perfection or cutting-edge AI labs. What they want is understanding and control—clear documentation, defined ownership, and the ability to explain how systems work and how risks are managed.
If an AI tool can be comfortably explained to leadership and examiners, it’s usually on solid ground.
The Big Picture
AI brings real opportunities for credit unions, but the most successful approaches are thoughtful and deliberate. By focusing on clear business goals, practical governance, and strong vendor oversight, credit unions can move forward with confidence—without chasing trends or taking on unnecessary risk.
Progress matters more than speed. And clarity matters more than buzzwords.
About the Author
Steve Torino, CISM, is Chief Information Security Officer at Synergent. With over 20 years of experience in information technology and cyber risk management, Torino’s areas of expertise include identifying security threats, leading complex IT projects, developing and implementing robust security policies and procedures, creating comprehensive risk management strategies, and ensuring compliance with industry and government standards.
