How to Choose AI Use Cases That Actually Return ROI

The most expensive AI projects are the ones that look impressive in a board deck and die in weekly active usage. Strategy starts with where AI removes real work or unlocks new product value.

Score candidates on four axes: how often the task happens, how costly a wrong answer is, whether you have clean enough data, and whether the feature differentiates your product versus a commodity chat wrapper.

High frequency + low cost of failure is the sweet spot for first launches: drafting, triage, search, and structured extraction. High-stakes decisions need humans in the loop and stronger evals.

Data readiness kills more roadmaps than model choice. If you cannot retrieve the right documents or events, no frontier model will save you.

As an AI consultant, I push teams to ship one measured use case in weeks, then expand. Momentum beats a twelve-month platform bet that never touches a customer.