Decision guide · AI opportunity · 6 min guide
Choose an AI use case by the decision it improves—not the demo it enables.
A high-value AI use case sits where a meaningful business outcome, a real user problem, feasible technology, usable data and acceptable risk overlap. Start with workflow evidence, score opportunities consistently, and test the assumption most likely to make the idea fail. Do not begin with a model or a feature list.
Start with friction you can observe
Interview the people doing the work and trace real cases. Look for repeated handling, waiting, inconsistent judgment, hard-to-find knowledge and decisions that arrive too late. Record frequency, time, error, consequence and ownership.
- A named workflow and owner
- A baseline you can measure
- A user who would adopt a better path
Compare every idea with the same lens
Score business impact, user desirability, technical feasibility, data readiness and risk. A popular idea with no owner or representative data should rank below a smaller problem with clear evidence and a credible adoption path.
- Value: what outcome changes?
- Feasibility: what capability is uncertain?
- Readiness: can permitted, representative data be evaluated?
- Risk: what is the cost of a wrong output?
Select the smallest decision-producing test
The first pilot should prove or disprove the hardest assumption. It may be a shadow workflow, a human-reviewed assistant or an offline evaluation. Its result must support a documented proceed, change or stop decision.
A good first use case has five things
- 01A costly or valuable problem
- 02A specific user and workflow
- 03A measurable baseline
- 04Representative, permitted evidence
- 05A safe and reversible pilot boundary