Strategy question
Some large enterprise customers are reluctant to upgrade to newer LLMs because of trust, governance, and workflow disruption concerns. How would you shape the product roadmap and go-to-market plan to increase adoption of newer models while preserving enterprise trust and minimizing rollout risk?
- Glean
- Strategy
- Hard
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What this question tests
Tests shaping a roadmap and go-to-market plan that increases adoption of newer models among trust-sensitive enterprise customers without forcing disruptive change.
How to approach it
- Identify the specific sources of reluctance: unfamiliarity with new model behavior, concern about workflow disruption from changed outputs, and lack of governance visibility into what changed.
- Address governance concerns first with product changes: transparent model-version labeling, and the comparison and canary rollout tooling from the model-hub work, so admins can validate before committing.
- Reduce workflow disruption risk by defaulting to opt-in upgrades rather than forced migrations, letting cautious customers stay on a stable model until they're ready.
- Build go-to-market trust assets: case studies or internal benchmark results showing the newer model's improvement on metrics this segment cares about, like accuracy or latency, not just capability breadth.
- Offer a low-risk migration path, for example a time-boxed parallel run where both old and new models are available, so customers can compare before fully committing.
- Set an explicit goal, for example a defined percent of eligible enterprise accounts opted into the newer model within two quarters, and track opt-in rate and post-migration satisfaction as the success metrics.
What a strong answer includes
- Separates governance, workflow disruption, and unfamiliarity as distinct reluctance drivers, prescribing a different tactic for each rather than one blanket adoption push.
- Defaults to opt-in migration with a parallel-run comparison period, respecting the real workflow disruption risk this segment cites.
- Uses go-to-market assets tied to metrics this specific segment cares about, not a generic newer-is-better pitch.
- Sets a concrete adoption goal and tracks post-migration satisfaction, not just opt-in count, to confirm the migration is actually going well.
Common mistakes
- Forcing migration to build adoption numbers quickly, undermining the trust this segment specifically needs protected.
- Pitching newer models generically on capability without addressing the specific governance and disruption concerns raised.
- Measuring only opt-in rate without tracking whether migrated customers are actually satisfied afterward.
Likely follow-up questions
- How would you handle a customer who opts in and then wants to roll back?
- What would you do if the parallel-run comparison period reveals real regressions for some use cases?
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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