Metrics question
A pilot agent deployment has strong qualitative feedback from senior stakeholders, but end-user adoption is inconsistent and the customer’s data environment is messy. How would you structure the deployment plan, define the success metrics, diagnose whether the issue is workflow fit vs data readiness vs change management, and decide if this should graduate into a repeatable product for the broader vertical?
- Harvey
- Metrics
- Hard
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What this question tests
Tests structuring a pilot-to-scale decision when qualitative feedback is strong but adoption is inconsistent and data quality is a known risk.
How to approach it
- Structure the deployment plan in phases: a scoped pilot with clear success criteria, a data-readiness assessment, and a defined graduation gate to broader rollout.
- Define success metrics beyond stakeholder sentiment: end-user adoption rate, task completion rate, and time saved, since senior enthusiasm alone does not confirm value.
- Diagnose the inconsistency by segmenting usage by team and correlating with known data-quality issues in that team's environment.
- Separate workflow fit from data readiness by testing the same workflow against a cleaner data subset, if available, to isolate whether data quality is the actual blocker.
- If data readiness is the blocker, prioritize a data-cleanup or integration fix before broader rollout, rather than pushing adoption harder on messy data.
- Decide on graduation to a repeatable vertical product only once inconsistency is explained and addressed, not while it remains unexplained.
What a strong answer includes
- Uses end-user adoption and task completion as the real success measure, not senior stakeholder sentiment alone.
- Isolates data readiness as a distinct hypothesis by testing against a cleaner data subset, rather than assuming it is workflow fit.
- Refuses to graduate the pilot into a repeatable product while the adoption inconsistency remains unexplained.
Common mistakes
- Treating strong senior feedback as sufficient proof of readiness to scale.
- Pushing broader rollout without first isolating whether data quality is the actual blocker.
Likely follow-up questions
- How would you convince senior stakeholders that their positive feedback is not the full picture?
- What would a minimum data-readiness bar look like before you would scale?
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More questions from Harvey
Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 9: Measuring AI Product Economics
- Chapter 2: Using SQL and Logs
- Chapter 14: Interviewing for AI PM Roles