Finding high-value use cases
The hardest skill: deciding what to build at all.
Most AI effort is wasted on the wrong problems. A lead's product sense — spotting use cases with real value, feasibility and acceptable risk, and saying no to the rest — is what separates impact from theatre.
Key ideas
- 1
Score use cases on three axes: value (business impact) × feasibility (can AI do it reliably?) × risk (regulatory/safety/reputational). You want high value, proven feasibility, manageable risk.
- 2
Start where errors are cheap and volume is high (drafting, triage, search, summarization) before high-stakes automated decisions.
- 3
Prefer 'assist the human' over 'replace the human' early — it captures value at far lower risk, crucial in insurance.
- 4
Say no well: a clear, criteria-based no protects credibility and capacity. Maintain a visible prioritized backlog.
- 5
Build vs buy: buy commodity capabilities, build where you have proprietary data or real differentiation.
A simple prioritization
- Value: revenue, cost, risk-reduction, or experience — quantify roughly.
- Feasibility: is the task within reliable LLM ability? Can you eval it?
- Risk: regulatory tier, data sensitivity, blast radius of errors.
- Sequence: quick credible wins first, then bigger bets.
Patterns that tend to pay off
- Drafting & summarization (tickets, docs, comms).
- Search & Q&A over internal knowledge (grounded RAG).
- Triage/classification & routing with a human check.
- Developer productivity (your own proven area).
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What three axes should you score AI use cases on?
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