Strategy question
A large enterprise customer says Duet’s suggested workflow updates look useful, but they do not trust the AI enough to approve changes at scale. How would you prioritize the roadmap to increase trust without turning Duet into a slow, manual review tool? Walk through the customer discovery you’d run, the tradeoffs you’d evaluate, and the first capabilities you’d ship.
- Decagon
- Strategy
- Hard
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What this question tests
Whether you can build trust in an AI recommendation product without defaulting to slow manual review, which would undermine the product's core value proposition.
How to approach it
- Run discovery to find exactly where trust breaks: is it the suggestion's accuracy, the lack of visibility into why it was suggested, or the blast radius if it is wrong.
- Separate low-risk suggestions (formatting, minor copy) from high-risk ones (policy changes, pricing logic) since they need different trust mechanisms.
- For high-risk suggestions, ship explainability and evidence citations first, so a reviewer can verify quickly rather than re-deriving the answer from scratch.
- Add a staged autonomy model: start with suggest-and-approve for everything, then let low-risk categories graduate to auto-apply once accuracy is proven per category, not company-wide.
- Track approval rate and time-to-approve per risk category as the metric that trust is actually increasing, not just suggestion volume.
What a strong answer includes
- Segments suggestions by risk instead of treating trust as one uniform bar to clear.
- Proposes evidence and explainability as the trust lever before proposing auto-apply, which matches how enterprise buyers actually build confidence in AI.
- Uses category-level graduation to auto-apply, avoiding an all-or-nothing trust decision.
Common mistakes
- Proposes making the AI faster or smarter as the only fix, ignoring the explainability gap.
- Treats all suggestions as equally risky, missing the chance to earn trust on low-risk changes first.
Likely follow-up questions
- How would you measure whether trust actually increased versus users just accepting defaults.
- What would you do if a high-value customer wants auto-apply before you trust the accuracy.
More strategy questions
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More questions from Decagon
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop