What it means
Use case prioritization is the moment after discovery when you have a list of 8 to 12 places AI could help, and you have to pick the one or two that ship first. The right shortlist is not always the most exciting use case. It is the one that combines high business value, clean data, low integration risk, and a short path to measurable proof.
A simple framework: score each candidate from 1 to 5 on (a) annual hours saved or revenue uplift, (b) data readiness, (c) integration complexity, (d) measurability. Multiply the first by the rest. The top score ships first.
Why it matters
Trying to deploy four AI workflows simultaneously is the single most reliable way to ship none of them on time. Each workflow has its own data prep, integration work, and stakeholder alignment. Concurrent delivery multiplies risk; sequential delivery compounds confidence.
Picking the right first use case also funds the next one. A pilot that returns measurable hours back to the team in the first 30 days becomes the internal case study that gets you headcount and budget for use case two.
Example
A specialty clinic has six possible AI workflows on the table. Scoring puts appointment confirmation and no-show recovery at the top: highest annual hours, cleanest data (already in the booking system), simplest integration. Clinical notes summarisation scores high on value but flunks on data readiness and regulation risk. Confirmation ships first, clinical notes parks for quarter two.