Qualify before you score
Require each opportunity to name a workflow, an owner, a beneficiary, and a measurable outcome. Ideas without these foundations belong in discovery, not in the committed delivery portfolio.
Check whether an existing product or a non-AI intervention can address the problem. Build-versus-buy decisions should include integration, evaluation, support, data handling, and switching costs—not just license or development cost.
Look through four lenses
Assess business value, technical feasibility, organizational readiness, and risk separately. A high-value task may be impossible with available data. A technically easy task may lack a team willing to change its workflow.
Record the evidence and confidence behind each assessment. Avoid adding arbitrary scores into a precise-looking total that hides a critical blocker.
- Value: what improves, for whom, and against which baseline?
- Feasibility: can the required information and systems be accessed?
- Readiness: can an accountable owner mobilize users and change?
- Risk: what can go wrong, and can the impact be contained?
Manage a mix of commitments
Balance small improvements, reusable capability investments, and uncertain but potentially valuable bets. Shared dependencies—such as one missing data integration—may be worth addressing before several individual use cases.
Review the portfolio as evidence changes. Decide explicitly whether to investigate, deliver, expand, or stop each opportunity. A roadmap is a sequence of learning and investment decisions, not a promise that every submitted idea will be built.
Put the idea to work.
AI Use Case Qualification FrameworkA measurable baseline, a named beneficiary, and a credible benefit hypothesis.
Related concepts
References & further reading
These sources provide supporting context. The operating frameworks and recommendations are editorial interpretations, not claims of endorsement.