AI use-case prioritisation scorecard score it before you build it
A scoring sheet that ranks candidate AI use cases on value, data readiness, risk and effort before anyone builds.
What this covers.
A working spreadsheet and scoring rubric we use in discovery. Each candidate use case is scored on measurable value, data availability and quality, regulatory and reputational risk, and delivery effort — producing a ranked shortlist you can defend to a board.
Four weighted axes
Value, data readiness, risk and effort, each with a defined 1–5 rubric so scores stay comparable.
Board-ready output
A ranked shortlist with the reasoning attached, not a list of ideas.
Reusable each quarter
Re-score as data and appetite change; the sheet keeps the history.
Section by section.
Scoring rubric
The 1–5 definitions for each axis, with worked examples.
Use-case sheet
One row per candidate, with owner, sponsor and dependency fields.
Weighting model
Adjustable weights so the sheet reflects your risk appetite.
Facilitation notes
How to run the scoring session, and who needs to be in the room.
Three things you can act on.
- A ranked, defensible AI shortlist in one workshop
- A shared language for value, risk and effort
- A record of why something was deferred
The detail before you ask for it.
Format
Template · XLSX + PDF rubric
Category
AI engineering
Extent
1 workbook, 4 sheets
Written for
Transformation leads and technology executives
Level
Mixed
Language
English
Published
2026-02-25
Author
Lucidspire IT Services practice
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AI use-case prioritisation scorecard
A scoring sheet that ranks candidate AI use cases on value, data readiness, risk and effort before anyone builds.
XLSX + PDF rubric
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