Use case teardown

Why AI use-case lists fail executive review

Most lists fail because they describe possible automation but cannot prove why one bet should be funded before another.

1

Lists over-index on idea quantity rather than decision quality.

2

Missing evidence and sequencing assumptions surface too late.

3

Without shared scoring criteria, prioritization becomes opinion-led.

The recurring failure pattern

Teams collect ideas quickly, then stall when executives ask: why this, why now, and what evidence supports it?

What to fix

  • Tie every candidate to a specific workflow, pain, owner, and evidence source.
  • Separate quick wins, strategic bets, prerequisites, and evidence gaps.
  • Use transparent scoring so stakeholders can challenge assumptions directly.

Practical next step

Run one high-pain workflow through an evidence model first. Then decide whether the method scales across your portfolio.

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