How do you prioritise AI use cases?

Score each candidate from one to five on four axes: value, feasibility, data readiness, and risk posture. Multiply the scores rather than averaging them, so that a fatal weakness on any single axis removes the case instead of being offset by strength elsewhere. Score the specific use case rather than a general ambition.

Why multiply rather than add use case scores?

Because averaging hides fatal weaknesses. A use case with compelling value and no usable data scores respectably on a mean and then consumes a year proving it cannot be built. Multiplying makes a zero on any axis decisive, which is the correct behaviour when one missing precondition makes delivery impossible.

AI Use Case Scoring Sheet

Last updated: 2026-08-14

The four-axis scoring sheet rates an AI use case from one to five on value, feasibility, data readiness, and risk posture. The scores are multiplied rather than added, so a zero on any single axis removes the case entirely instead of being averaged away by strength elsewhere.

Most use case prioritisation fails by averaging. A case with compelling value and no usable data scores respectably on a mean and then consumes a year proving it cannot be built.

The template

Score each axis 1 to 5. Multiply rather than add, so a zero on any axis removes the case rather than being averaged away.

Value 1. Marginal convenience, no measurable effect 2. Efficiency gain in a minor process 3. Measurable improvement in a process that matters 4. Material improvement in a priority outcome 5. Removes a constraint on the organisation's mandate

Data readiness 1. Data does not exist or is not accessible 2. Data exists in fragmented form, quality unverified 3. Data accessible, quality acceptable, labels partially available 4. Data accessible, quality verified by sample, sufficient history 5. Data already in production use for a comparable purpose

Decision ownership 1. No identified owner 2. Owner identified, not consulted 3. Owner engaged, has not committed to act on the output 4. Owner committed, has capacity to review outputs 5. Owner committed and has funded implementation beyond the pilot

Reversibility 1. Irreversible effect on individuals with no appeal route 2. Irreversible operationally, correctable at high cost 3. Correctable within the process at moderate cost 4. Human reviews before effect, correction routine 5. Fully reversible, output advisory only

Interpretation. Scores above 200 are strong candidates. Between 100 and 200, address the weakest axis before proceeding. Below 100, decline and record the reason. Any axis at 1 removes the case regardless of the total.

How to use it

Score each axis independently from one to five using the descriptors, before comparing cases against each other.
Multiply the four scores rather than adding them. The multiplication is the mechanism: it lets a single fatal axis remove a case.
Score the specific use case rather than the general ambition. "AI for HR" cannot be scored; "screening applications against mandatory criteria" can.
Where data readiness is the weak axis, treat the data work as the project rather than treating the model as the project.

This template comes from the AI Use Case Discovery, Readiness and Maturity: A Practical Playbook, which sets out the reasoning behind it, the rules of thumb that govern its use, and the signals that tell you the approach is failing. The full playbook is also available as Markdown. See all AI governance templates.