How to identify a genuine AI use case in the enterprise?

The pressure is real: every executive committee wants "its" AI project. But the gap between hype and value creation is considerable. Experience shows that a sound AI use case can be recognized by five criteria — to be assessed honestly before writing a single line of code.

1. Business value: which problem, which decision, which gain?

A good use case starts from a quantifiable business pain: hours spent qualifying requests, customers leaving without any detected signal, prices set by gut feeling. The question is not "what can AI do?" but "which decision or task do we want to improve, and what is that improvement worth?"

If nobody can estimate the gain — in time, revenue or avoided risk — the use case is not ripe.

2. Feasibility: is the problem actually predictable?

Some phenomena model well, others don't. A rich, stable churn history lends itself to scoring; a rare, unprecedented event does not. Before committing, a quick test on real data beats any promise: a few weeks of exploration are usually enough to estimate whether the achievable performance justifies the investment.

3. Data: does it exist, is it accessible, is it reliable?

This is the most commonly overestimated criterion. The questions are concrete: does the data actually cover the phenomenon to predict? Over what history? At what quality? Who can grant access, and how fast? A brilliant use case without accessible data is a data collection project in disguise — which can be legitimate, but must be owned as such.

4. Risk: what happens when the model is wrong?

Every model gets things wrong. The real question is the cost of the error and the ability to recover from it. Recommending the wrong article is benign; misrouting a sensitive complaint is not. This criterion determines the required level of human oversight, the guardrails to build — and sometimes leads to the conclusion that this decision should not be automated at all.

5. Adoption: who will use it, and do they want it to exist?

A high-performing model that teams work around produces nothing. Identify the end users early, understand their current process, and check that the solution fits into it naturally. The best use cases are often the ones teams ask for — not the ones imposed on them.

A grid, not a dogma

These five criteria don't produce a magic score: they structure an honest conversation between leadership, business teams and Data teams. A use case weak on one criterion can be strengthened; weak on three, it should be postponed. This discipline of selection is what separates organizations where AI delivers results from those where it delivers demos.