Turn ambition into a test
AI ideas become easier to evaluate when they are narrowed into one user group, one workflow, one success measure, and one defined operating constraint.
The checklist helps teams separate strategic intent from implementation detail, so leaders can decide whether a proof of concept is worth building.
Collect decision-grade evidence
A validation cycle should produce evidence about workflow fit, data access, review effort, user adoption, and expected ROI. The goal is a practical decision, not a polished demonstration.
When the evidence is weak, the next step may be improving data readiness or redefining the workflow before development continues.
Separate feasibility from priority
A use case can be technically feasible and still be the wrong priority. The checklist therefore treats feasibility, business value, operational risk, and adoption burden as separate dimensions instead of collapsing them into a single score.
This prevents teams from overinvesting in attractive demos that do not remove enough work, reduce enough risk, or create enough decision quality to justify implementation.
Make the output useful for a leadership decision
The end of validation should produce a recommendation that leaders can act on: proceed to a PoC, redesign the workflow, prepare data, train users, or pause the theme. Each outcome should include the reason and the next evidence needed.
A concise decision record is often more valuable than a long slide deck. It keeps the team aligned on what was tested, what was learned, what remains uncertain, and who owns the next step.