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AI Use-Case Prioritisation Framework

The AI Use-Case Prioritisation Framework ranks candidate AI projects by scoring their impact (business impact, time saved, strategic value, scalability) against their effort (implementation effort, data readiness, risk, cost), and classifies each as a quick win, a strategic project, an experiment or a lower-priority initiative.

The model

ImpactEffort

Quick wins

Strategic projects

Experiments

Lower priority

Use the framework PDF / Word

Developed by Markaigen · Updated 2026-10-03

What each part means

Business impact: How much the use case changes revenue, cost, speed or customer experience if it works.
Time saved: Hours per month the team gets back, and whether those hours are the scarce ones.
Strategic value: Whether it builds a capability you will need again, or solves a one-off problem.
Scalability: Whether the same solution works for more channels, markets or teams without rebuilding.
Implementation effort: Integration, change management and the number of people and systems involved.
Data readiness: Whether the data exists, is clean and may be used; missing data is the most common hidden effort.
Risk: Cost of an error, legal and brand exposure, and how reversible the output is.
Cost: Licences, build time and ongoing operation, compared with what you spend today.

How to use it

Score every candidate with the same people in the same session, so the scale stays consistent.
Start with quick wins to build confidence, and run one strategic project in parallel.
Revisit experiments after a pilot: they often turn into quick wins once the data is ready.

Classify a use case

Name a use case, score it on eight factors and see where it lands. Add several to build your map.

Your map