Use Case Prioritisation
The process of evaluating and ranking potential AI or product initiatives by their expected business value relative to effort and risk, to determine which to invest in first.
Full Definition
Use case prioritisation prevents organisations from chasing every AI opportunity at once — a common failure mode that spreads resources too thin and delivers nothing. A structured prioritisation framework applies consistent scoring criteria across all candidate use cases, surfaces the highest-return opportunities, and builds stakeholder alignment around the investment decision before resources are committed.
Prioritisation Frameworks
RICE Scoring
Reach × Impact × Confidence ÷ Effort. The most widely used framework for AI use case prioritisation. Scores each use case on four dimensions and produces a normalised priority score.
Value vs Effort Matrix
A 2×2 plot of business value against implementation effort. Quick visual for workshop settings. Less precise than RICE but faster to apply.
MoSCoW
Must-have / Should-have / Could-have / Won't-have. Useful for stakeholder alignment workshops when scoring feels premature.
KANO Model
Categorises features by their effect on customer satisfaction. Particularly useful for product-led AI features rather than internal process automation.
Frequently Asked Questions
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