What is use case prioritization?
Use case prioritization is the process of deciding which AI projects a company funds first. Ideas arrive from every department, and most companies can staff only a few at a time. Prioritization describes each candidate in the same format, scores it against shared criteria, and ranks the list. The output is a short, ordered list of projects with named owners and target metrics, plus a record of why the other ideas wait.
How use case prioritization works
Each candidate is first written up as a one-paragraph use case. The write-up names the task or decision, the people who perform it, the data it needs, and the measure that would show success. Each use case is then scored on three groups of criteria.
- Value. High volume, a measurable cost of delay or error, and a link to a metric leadership already watches all raise the score.
- Feasibility. The data must exist and be reachable, and the integration with the system of record must be manageable. The task must also suit a model, a question known as problem tractability.
- Risk. Sensitive data, regulatory exposure, and the harm a wrong answer could cause all lower the score or add required safeguards.
Teams often plot value against feasibility on a two-by-two grid, and the first projects come from the high-value, high-feasibility corner.
Use case prioritization in practice
A county government collects a dozen ideas from its departments. They include summaries of non-emergency 311 calls, completeness checks on building permit applications, and draft responses to public records requests. Permit checks rank first. The volume is steady, the requirements are written in the county code, and a staff reviewer still makes every decision. Public records responses score high on value but carry more risk, since a missed redaction can release protected information. The county schedules a proof of concept on requests that are already closed. A resident-facing chatbot for zoning questions is set aside because a wrong answer could mislead applicants.
What to watch for
- Score ideas from senior leaders on the same sheet as every other idea, or the ranking loses credibility.
- Feasibility scores are guesses until someone examines the actual data, so check the top candidates before committing.
- Favor use cases where a person can verify the output quickly, such as a draft, a ranked list, or a flagged exception.
- Re-score the list every few months, because model capability and cost change quickly.