AI strategy conversations usually follow a formulaic pattern: First of all, they’re usually too late, only showing up after someone in the org realizes “Oh, maybe we should…investigate this.”
Basically; a leadership team recognizes that AI matters, sees competitors or vendors moving, and decides it’s time to define a plan. From there, the discussion tends to move quickly toward solutions like: What to build, what to buy, where to invest, etc.
That jump is understandable, but it’s also where a lot of unnecessary commitment happens too soon. For most credit unions, that means: You don’t have a strategy problem right now, you have a sequencing problem.
The pressure to commit too early
AI has evolved quickly, and will continue to do so, and subsequently, it creates a strange kind of urgency. On one hand, there’s real momentum. New capabilities are showing up quickly, and it’s clear that some of them will matter. On the other, the landscape is still shifting. Tools change, use cases evolve, and best practices aren’t fully settled.
That combination pushes teams toward early decisions that feel strategic but are often premature.
- Large vendor commitments.
- Broad implementation plans.
- Internal mandates to “integrate AI” across functions.
These moves create the appearance of progress…they look good on the surface, but simultaneously lock institutions into paths that may not hold up six or twelve months later.
Strategy is not the same as commitment
A useful AI strategy at this stage should not look like a roadmap full of fixed initiatives — it should look more like a set of decisions about how the organization will approach uncertainty.
That includes things like:
- Where AI is most relevant right now.
- Where it is not a priority yet.
- What types of experimentation are appropriate.
- How risk will be evaluated and managed.
None of that requires heavy investment, but it does require enough clarity to proceed, because without that clarity, strategy tends to collapse into a list of disconnected efforts that are difficult to coordinate and even harder to unwind.
Most activity is happening before strategy anyway
One of the reasons overcommitment happens is that AI adoption doesn’t wait for strategy.
The way it often goes is: By the time leadership teams start formal planning, AI is already in use somewhere inside the organization. Teams are experimenting, vendors are introducing features, and employees are finding ways to apply it to their work.
That creates a mismatch, because now, activity is already happening at the edges, while strategy is being defined at the center, which is…backwards. If the response is to immediately formalize and scale everything, the organization ends up amplifying whatever inconsistencies already exist.
A more useful approach would be to step back and understand what’s actually happening before deciding what to expand.
What a more measured strategy looks like
A measured AI strategy is less about locking in decisions and more about setting direction.
At the executive level, that usually comes down to a few core elements.
- Clear focus areas: Not every part of the organization needs the same level of attention. Some areas will have more immediate relevance for AI, while others can wait. Being explicit about that reduces noise.
- Defined boundaries: Experimentation is useful, but it needs limits. What is acceptable today? What requires additional review? Where should teams avoid using AI altogether for now?
- Decision ownership: As activity increases, decisions need to be anchored somewhere. Who is responsible for evaluating use cases? Who has authority to approve or pause initiatives?
- A shared evaluation framework: Opportunities should be assessed in a consistent way. That doesn’t mean every decision looks the same, but the criteria behind them should be clear.
- A sense of pacing: Not everything needs to move at once. Some areas benefit from faster iteration, others require more caution. A strategy should reflect that.
Where things tend to go off track
The most common mistake is doing too much without a clear structure, and that often shows up as things like:
- Multiple pilots running without coordination.
- Overlapping vendor tools solving similar problems.
- Internal confusion about what is approved versus experimental.
- Leadership revisiting the same decisions repeatedly.
These are, ultimately, a failure to coordinate, and they become much harder to fix once resources, expectations, and timelines are attached.
The role of leadership is to set direction, not predict outcomes
Part of the pressure to overcommit comes from the expectation that strategy should provide certainty. In a stable environment, that makes sense, but with AI, it doesn’t really.
Shift your goal from predicting exactly which tools or use cases will matter most — to, instead: Create a structure that allows the organization to learn, adjust, and make decisions without creating unnecessary risk.
That requires a different posture from leadership: Less emphasis on locking in long-term commitments, more emphasis on setting guardrails and revisiting decisions as new information emerges.
A more practical definition of progress
A bigger AI footprint just means more ways to mess things up. If the decision-making isn’t clear, you’re just scaling confusion.
- Are teams aligned on where to focus?
- Are risks being considered consistently?
- Can the organization adjust course without significant disruption?
If the answer to those questions is yes, then the strategy is doing its job, but if not, adding more initiatives won’t fix it.
Start with alignment, not expansion
Most teams are already doing more than they realize, but the issue is nobody has really stopped to line that up. You end up with activity that looks fine in isolation, but doesn’t quite add up when you zoom out. Same tools used differently, different expectations across teams, no shared sense of what’s actually “okay” versus just happening.
And that’s where this usually starts to wobble. Once that’s cleaned up, everything else gets easier. Until then, adding more on top just makes it harder to see what’s going on.
If this feels familiar, this is exactly the kind of work we’ve been doing with leadership teams through AI BASECAMP—helping them get oriented before decisions start compounding.
