Product design question
Design the first version of a Command Center dashboard for enterprise admins at a large law firm. What core views would you include to show adoption, risky or non-compliant usage, and governance coverage, and how would you make the dashboard actionable for Harvey’s Customer Success and Sales teams without turning it into a generic BI report?
- Harvey
- Product design
- Hard
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What this question tests
Product design judgment for an internal-facing admin tool: can you design views that drive real action for two different internal teams instead of building a generic reporting dashboard.
How to approach it
- Core view one: adoption, showing active users, usage frequency by practice group, and stalled or never-activated accounts, since low adoption is the first thing Customer Success needs to act on.
- Core view two: risky or non-compliant usage, flagging things like access to matters outside a user's assigned practice group, or usage patterns that deviate from firm policy, surfaced with enough context to investigate, not just a raw alert count.
- Core view three: governance coverage, showing what percentage of the firm's users, matters, or practice groups are actually under active policy controls, since partial coverage is itself a risk the admin needs visible.
- Make it actionable for Customer Success and Sales by attaching a suggested next step to each flagged item, like reach out about stalled onboarding, or expand to this under-covered practice group, rather than just displaying numbers.
- Avoid the generic-BI trap by limiting each view to a small number of decisions it is meant to drive, and pushing exploratory drill-down data behind a secondary layer instead of the main dashboard.
What a strong answer includes
- Names three specific, differentiated views (adoption, risk, coverage) rather than one generic usage dashboard.
- Attaches a suggested action to each flagged item, which is what actually makes a dashboard useful to Customer Success and Sales instead of just informative.
- Explicitly limits main-view scope and pushes exploration to drill-downs, directly addressing the stated risk of becoming a generic BI report.
Common mistakes
- Builds a broad, exploratory BI-style dashboard with no specific decisions it is meant to drive.
- Shows risk and coverage as raw numbers without enough context for someone to act on them.
Likely follow-up questions
- How would you prioritize which flagged risky-usage items surface first when there are many.
- What would you do if Sales and Customer Success want different things from the same view.
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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 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop