AI Use Case Qualification Framework
An attractive use case is not automatically a deliverable one. Assess value, feasibility, readiness, and risk independently before committing investment.
A measurable baseline, a named beneficiary, and a credible benefit hypothesis.
How the model works
Business value
Name the beneficiary and intended outcome. Separate efficiency from efficacy, establish the baseline, and state how the benefit will be realized.
Feasibility
Inspect the data, system access, integration constraints, and expected quality. Identify the smallest experiment that can disprove the idea.
Organizational readiness
Look for an accountable owner, available users, capacity for process change, and operational support after launch.
Risk & control
Consider the cost of error, sensitive data, permissions, and oversight. Decide which actions require approval and how the system can be stopped.
Decision & next evidence
Choose to investigate, deliver, defer, or stop. Record uncertainty and the evidence needed for the next decision rather than hiding blockers in a total score.