Where AI pays back first: a triage framework for leadership teams
Not every AI idea deserves a pilot. Score use cases on value, feasibility and risk, and start where all three line up.
After a strategy offsite, most leadership teams have a list of 30 AI ideas and budget for three. Picking by enthusiasm leads to impressive demos with no business impact. Picking by score leads to quieter projects that pay for the next ones.
Score on three axes
- Value: measurable cost, revenue or risk impact, in currency, not adjectives.
- Feasibility: is the data available, structured enough and accessible? Is the task within today's model capability?
- Risk: what happens when it's wrong? Who sees the error, and how expensive is it?
The sweet spot
The best first projects are high-volume, internal-facing, text-heavy workflows with a human already reviewing the output: document processing, case triage, internal knowledge search, first-draft generation. The error cost is contained, the data exists, and the value scales with volume.
The takeaway
Your first AI project's most important output isn't savings. It's organisational confidence: proof that AI can be shipped safely here. Choose the project most likely to create that proof.
- AI strategy
- Prioritisation
- Leadership
