Every leadership team has a list of AI ideas, and the usual way to rank them is value against feasibility. That ranking keeps producing pilots that impress in the demo and stall afterwards.
This matrix adds the criteria that decide whether AI pays off: how cheap the output is to check, what a mistake costs, whether the work is hard or merely messy, whether a smaller model would do, who owns the outcome, and what it costs at production volume.
What is inside
- How to run the exercise in a 90-minute leadership session
- An intake form for each candidate use case
- The scoring criteria, with what low, medium and high look like
- A scoring matrix for up to eight use cases
- How to read the result: pilot, buy, or drop
- A one-page business case template for the winner
- A pilot design checklist, so the pilot measures the tool and not the expert running it
