AI Use-Case Prioritization Playbook

The short version

The most expensive AI mistake isn't a failed project — it's building the wrong thing competently. This playbook is how you pick the right first bet. Score every candidate on three axes: **value** (impact if it works), **feasibility** (how ready your data, tooling, and team are), and **risk** (regulatory, reputational, safety). The use case to start with is rarely the highest-value one — it's the one with the best value-to-feasibility ratio and acceptable risk. That's your proof point. The framework also forces the harder discipline: explicitly parking the use cases you're *not* doing yet, so the org stops relitigating them. The full playbook includes the scoring rubric, a weighting worksheet, a filled-in example for a 60-person company, and the "start here" decision rule.