The Use Case Selection Trap
Most organizations choose their first AI use case based on what sounds ambitious or what a vendor demo made look easy. Both are unreliable selection criteria.
The right first use case has two qualities:
- The problem is specific enough that you can define “good output” in advance.
- The data needed to solve it already exists in a usable format.
That second point gets skipped more often than the first.
A concrete use case:
“We want an AI agent to handle first-contact customer inquiries about order status.”
That’s testable. You can define what a correct response looks like. You probably have historical inquiry data. You have a system with order information you can connect to.
A vague use case:
“We want AI to improve the customer experience.”
That’s not a use case – it’s a goal. Before an implementation plan can be written, it needs to become specific.
Filtering question:
Can you write, in two sentences, exactly what success looks like at 90 days?
If not, the use case isn’t ready.
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Build, Configure, or Integrate
There’s a spectrum from off‑the‑shelf AI tools through configurable platforms to fully custom‑built models. Where you land on that spectrum for a given project should be decided early – it determines timelines, costs, and your ongoing operational commitment.