Across 123 credit union executives, one department stood out—and not in a good way.
Finance & Accounting reported an average AI readiness score of 2.40 out of 5, the lowest of any function in the organization. Not a single respondent rated their team as “fully prepared.”
That’s not a rounding error or a minor lag. It’s a consistent signal across the dataset.
Most departments are in the same range. Finance isn’t.
If you look across the rest of the organization, the numbers cluster pretty tightly.
Marketing & Member Experience comes in at 3.41, Risk & Compliance at 3.29, Technology at 3.13, Lending at 3.07, and HR at 3.00.
None of those are especially high, but they’re all operating in the same general range. You’d describe them as partially prepared, with room to improve.
Finance sits a full step below that group.
It’s the only function that consistently falls closer to “not ready” than “getting there.”
This wouldn’t matter as much if it were any other team
If HR or even marketing lag a bit, you can still move. Projects might be uneven, but they move.
Finance is different.
This is where budgets get approved.
Where investment cases get challenged.
Where risk is evaluated, formally or informally.
Even when finance isn’t directly involved in an AI initiative, it tends to influence whether that initiative gets funded, expanded, or quietly deprioritized.
A low readiness score here doesn’t just reflect capability—it affects momentum everywhere else.
The hesitation is visible in the responses
If you break down the finance responses more closely, the distribution tells the same story.
- 20% rated their team as “not prepared”
- 26.67% rated it a 2
- Nearly half landed at a 3
- Only 6.67% rated it a 4
- 0% selected “fully prepared”
That’s not a polarized set of opinions. It’s a broad, consistent middle with a noticeable lean toward caution.
You don’t see the same pattern as you do in other departments, where at least a few respondents feel confident pushing forward.
At the same time, finance sees the upside
This isn’t a case of resistance for the sake of it.
When asked where AI could help, finance leaders pointed to areas like:
- automated reporting
- fraud detection
- financial forecasting
- cost reduction
These are not fringe use cases. They go directly to efficiency, accuracy, and performance.
The interest is there. The use cases are clear. The readiness just hasn’t caught up.
This is where AI strategies tend to stall
Most AI strategies assume a relatively even starting point across the organization.
In practice, that’s rarely the case. And when one of the most influential functions is the least prepared, it changes how quickly anything can move.
You see it in small ways at first. Longer approval cycles. More questions around ROI. A higher bar for experimentation.
Over time, that adds up to fewer initiatives making it out of the early stages.
Not because they’re bad ideas, but because the organization isn’t fully comfortable backing them yet.
The implication is pretty straightforward
If finance isn’t part of the conversation early—and if its level of readiness doesn’t improve alongside other departments—AI efforts tend to stay fragmented.
Teams experiment in pockets. A few pilots run. Some tools get adopted at the edge of the organization.
But scaling anything meaningful becomes harder than it needs to be.
What this means for leadership teams
Most leadership teams are already thinking about AI in terms of tools, vendors, and use cases.
That’s the visible part of the strategy.
The less visible part is making sure the functions that control investment and risk are equipped to engage with those decisions in a more informed way.
That doesn’t mean turning finance into an AI lab. It means building enough shared understanding that discussions around cost, value, and risk don’t default to hesitation.
Right now, the data suggests that gap is still there.
Closing it is less about adopting another tool and more about bringing one of the most important functions in the organization up to the same level of readiness as the rest.
Until that happens, most AI strategies are going to move slower than expected—regardless of how strong the intent is elsewhere.
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