How to Prioritize AI Use Cases
A structured scorecard for choosing which AI initiatives justify investment when you have more ideas than capacity.
The real problem is selection
Most organizations do not lack AI ideas — they lack a defensible way to choose between them. Prioritization is the discipline of comparing initiatives on consistent dimensions instead of enthusiasm.
Dimensions that matter
Business value, strategic relevance, feasibility, data readiness, implementation complexity, adoption complexity, and risk. Score each on a consistent scale and weight them to reflect your organization's constraints.
Four decision bands
Prioritize (high value, feasible, ready), Pilot (promising but unproven), Investigate (valuable but unclear feasibility or readiness), and Defer (low value or high risk relative to return).
Avoid false precision
A scorecard is a structured conversation, not an oracle. Its value is forcing explicit trade-offs and making the reasoning visible to everyone who has to fund the work.
Put this into practice.
Run the numbers for your own initiative in the free AI ROI calculator.