Credit Union Leadership

Before your next board meeting, take the time to ask yourself something that is often absent from the agenda: how do I feel right now? Energized by this period of change, or worn down by it? Most credit union leaders I talk to land between the two, and several say they are surprised by that, having come through digital banking without feeling this way.

Part of what is different is speed. Digital banking transformation occurred over roughly a decade, which gave leadership teams time to watch their peers and spread the investment across several planning cycles. But this latest period of transformation is entirely different, with expectations spanning months to a few years.

If You’re Feeling Uncertain, You Are Not Alone

Wipfli’s survey of 100 credit union executives found that 82% are implementing AI in some form, while only 16% have an enterprise-wide roadmap that includes governance and measurable business impact. In the same survey, 56% said managing and implementing change would have a large effect on hitting their strategic priorities this year.

Carlos Vega, a director at Wipfli, described the gap directly: “Many credit unions are using AI because it’s available, not because it’s part of a deliberate strategy.” So, if your peers seem to have this figured out, most of them are moving without a map too.

Reframing Uncertainty as a Leadership Opportunity

Most management thinking treats uncertainty as something to be minimized. Research by Mark Griffin and Gudela Grote in the Academy of Management Review makes the case that this is only half of it: organizations also open uncertainty up on purpose, and what separates effective teams is how deliberately they regulate it, closing it down where the institution needs stability and opening it up where it needs to learn.

In a credit union, risk-avoidance is critical for safety and compliance, and part of why members trust you with their money. But applied too broadly, it shuts down exploration before it can begin, along with the rewards that come with it. In the same research, Griffin and Grote tie uncertainty to learning, because people pay closer attention when an outcome is not yet settled. Framed as a threat, that attention turns into caution, skepticism, and team members who withdraw. Framed as a learning opportunity, it turns into a more engaged, proactive team.

Re-framing uncertainty is your job as a leader, and the good news is that it is more achievable than having all the answers. Nobody expects you to suddenly have every AI use case pre-mapped with a step-by-step plan for your team to execute. But your team does need permission and encouragement to learn in the open, and the most practical instrument for that is a shared framework for how decisions get made.

A framework removes the choice between protecting the institution and moving with the market, because it lets you do both at once: you say, clearly and repeatedly, which decisions are exploratory, what they are allowed to cost in terms of time and resources, and what would need to be true for you to implement the change or roll it back. This empowers your team to learn and explore new solutions with an understanding of how to make decisions based on that learning.

There are three questions that can help ground your team in this direction.

1. Does This Solve a Problem We Already Have?

I always recommend starting from first principles. Even more importantly, teach your team about first principles thinking. Before evaluating any new technology, write down the five hardest things about running your credit union today: card disputes sitting in a queue for eleven days, lien releases that take three handoffs, calls that go unanswered after 6 p.m. because you don’t have enough overnight staffing. Whatever they are, get them on the page without attaching any of them to a solution. Focus entirely on the problem.

Once you have this list, you can ask which of those five AI could genuinely take on. Typically, organizations approach this backwards, which is why so many teams now run meeting notetakers that nobody reads. These tools were adopted because they were low risk and available, then attached to a “problem” afterwards.

When you have that clarity, it can be useful to look at how peers have solved the same problems. We’ve put together a guide on where other credit unions have started.

2. Do We Know What Good Looks Like?

Run this exercise backwards before you commit. Assume it is twelve months from now and it worked, then describe that state in specifics: which number moved, by how much, who noticed, and what it cost in time, budget, and attention.

Often, your key stakeholders will broadly agree on success while holding different definitions of it internally. This isn’t because they don’t want the project to succeed, it’s because people often find their frame of reference self-evident, so it does not occur to them to voice it. Their differences only become apparent during the review, by which point it is far too late. But if they are brought to light at the beginning, they can be mitigated. It’s shockingly simple how much more alignment you can achieve by simply creating a space for it. To help, we’ve set out the measurement questions we work through with credit unions before any new AI initiative goes live.

3. Does This Align With Our Values?

You can answer this in a way a national bank cannot, so ask it three ways: a gain for one group is not automatically a gain for the others. Does the member having the worst financial week of their life get a better experience, or a faster brush-off? Does the work your team loses look like the work they hated, and have you said out loud what happens to the people doing it today? Does the capacity or saving this frees up show up somewhere your community can see?

AI deployments done well can improve service for members, help your team do more fulfilling work, and give you cost savings to reinvest locally. But this is only possible if you assess each project against your core values from the start.

What This Gives You

In periods of uncertainty, teams can either withdraw into themselves, or come together stronger, having embraced new challenges in a space that rewards curiosity and learning.

None of the questions in this framework require you to predict which technology to invest in today. Instead, they give you something more durable: a consistent way of making decisions, and a team that is empowered to use their judgment against it.

The uncertainty everyone is feeling right now will pass, much as it did with digital banking. But the lasting, positive impact you can make as a leader will endure much longer, if you are willing to embrace the uncertainty to get there.

Dimitri Masin is the CEO and Co-Founder of Gradient Labs, the conversational AI platform transforming customer operations in financial services. An early employee at Monzo, Dimitri built the bank’s AI and Data Science teams, helping it become one of the UK’s most successful fintechs. During this time, he worked on some of the most challenging problems in customer operations and saw firsthand how AI could be used to solve them.