By Corey Gross
Every wave of technology within the financial services industry, whether it be core processing, online banking, or mobile deposit capabilities, offers smaller financial institutions increasing opportunities to compete with larger ones.
AI promises to be among the most meaningful advances in equalizing the playing field to date, and the timing has been critical, too, specifically for credit unions. While megabanks scale their engineering functions and fintechs continue to move at an unmatched pace, credit unions continue to operate with constrained margins and headcount. AI is one of the few tools that can meaningfully close the resource gap without requiring credit unions to compromise on their mission.
Getting the best results form AI requires knowing where to start, where to invest, and how AI and technology more broadly can help support and scale member relationships, an advantage that requires a deep, personal understanding of the credit union’s member base and the communities it serves.
Start With the Problem, Not the Solution
I’ve found credit unions are moving quickly around AI, but it’s important not to conflate reactionary investments for progress. When a financial institution finds a competitor announcing a new chatbot or hears a board member question AI adoption and enablement, there’s a pressure to chase the next opportunity without stopping to consider the problem it’s supposed to solve.
To ensure a credit union’s AI strategy is effective, measurable, and ultimately successful, leaders need to ask: Does this make something faster, cheaper, or objectively better? If the answer isn’t a resounding yes, the cost for deploying AI may not be defensible.
For example, I watched a credit union apply AI to a member interaction that was already streamlined and effective, resulting in an unnecessarily expensive outcome with no process improvement. AI refinement where it’s not needed doesn’t add value and can make things more complicated for those involved. Start by having team members identify operational and workflow pain points like escalation queues, request backlogs, and incremental call volume spikes.
Many credit unions already run lean, and high-friction workflows are where teams can see real improvement. If AI can mitigate time spent on resolution, taking a dispute that typically takes 20 minutes to resolve to 30 seconds, that’s a structural change that redefines how a team can operate and the level of service it can provide to members.
Consider Where to Build, Buy, and Partner Before Investing
Once it’s clear what a credit union needs to solve for, they need to decide how to address it, and it’s important to explore internal resources, technology partners, or third-party off-the-shelf tools. The quickest way to fail is defaulting to whichever path feels comfortable or mainstream over the one that best fits the identified workflow.
Building in-house works when the application is especially unique and the credit union has the resources to both launch it and sustain and monitor it long term. This approach requires engineering and data scientist support most credit unions don’t staff for and otherwise don’t need to. A point solution is right when the issue is standard within the broader market and, therefore, infrastructure is already built to support it. If the perfect solution already exists, the path forward is simple.
Working with an established vendor or partner can work well when the workflow is integrated into controls or systems specific to that partnership, like audit trails, security components, or role-based access. AI systems that don’t have context around terminology, data, or operational structures can be challenging, and specifically so within a highly regulated industry.
Three questions can help credit unions land the right approach around the build-buy-partner decision. Is what we’re solving for differentiated or common? Do we have the budget and headcount to own it once we implement it? And does it need to exist within our security and compliance window?
Leveraging Technology to Scale the Member Relationship
The real risk in going the AI route isn’t that the AI fails, it’s that the use case isn’t right and member trust and local knowledge are depleted as a result. Because relationships are the competitive edge of any credit union, they must be protected at all costs. Competitors don’t have employees who notice shifts in a longtime member’s transaction patterns or loan officers who understand why a local member’s income differs compared to last year.
AI enables lean teams to accomplish more, not by replacing people, but by moving them out of the backlog of administrative work to focus their time on judgment calls and relationship building that only a human can do. That’s the standard credit unions should consider for their AI investments. Does this provide my team with time for genuine conversation and decision making, or does it simply move the human element farther from the member relationship?
Ensuring this standard is met successfully requires integrating a human handoff or touchpoint into AI-supported workflows, particularly in areas such as fraud support and lending, where judgment is critical in decision making. It also requires a success measurement framework contingent upon what the time back enabled team members to do rather than simply the number of interactions AI deflected.
Credit union teams also need to be trained to identify blind spots and gaps so those who are closest to the member relationship continue as the last line of judgment and aren’t subjected to the structure changes due to updated workflows.
Credit unions can’t out-resource large banks, and they don’t need to. AI provides them with the ability to scale what has always been their differentiator: personalized member relationships, local knowledge, and a deep understanding of the communities they serve. AI should integrate strategically and deliberately, solving the right problems and providing team members more capacity for the work only they can do.
Done right, AI doesn’t make a credit union operate more like a megabank. It gives it the ability to be an even better credit union.
Corey Gross is VP and Head of Data & AI at Q2.
