Strategy question
Glean is defining a new Enterprise Intelligence category. In your first 90 days, how would you narrow the opportunity space and choose the first 2-3 product bets to build? Walk through the framework you’d use to weigh customer pain, willingness to act on the insight, repeatability across enterprise accounts, competitive whitespace, and Glean’s current assets like connectors, permissions, and the Enterprise Graph.
- Glean
- Strategy
- Hard
Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.
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What this question tests
Whether you can narrow an open-ended new category into a small, evidence-based set of first bets using multiple weighted criteria, rather than chasing every plausible opportunity at once.
How to approach it
- In the first 90 days, run structured discovery across a sample of existing enterprise customers to surface where they most want proactive insight versus where Glean already serves them well as pure search or Q&A.
- Score candidate opportunities on customer pain (how costly the problem is today), willingness to act (would leaders actually change behavior based on the insight, not just find it interesting), repeatability (does the pattern hold across many accounts or is it one customer's quirk), competitive whitespace (is this defensible versus adjacent BI or analytics tools), and asset leverage (does it use connectors, permissions, and the Enterprise Graph Glean already has).
- Weight asset leverage and repeatability heavily for the first bets specifically, since Enterprise Intelligence needs to prove it is a natural extension of Glean's existing platform, not a bolt-on analytics product competing from scratch.
- Pick 2-3 bets that score well across most criteria rather than the single highest-scoring one, to hedge against the category being new enough that any one bet could underperform.
- Set an explicit checkpoint at the end of the 90 days to kill or double down on each bet based on real customer pull, not just internal conviction.
What a strong answer includes
- Uses named, weighted criteria including asset leverage tied specifically to Glean's own platform strengths, connectors, permissions, Enterprise Graph, rather than generic opportunity scoring.
- Deliberately picks a small portfolio of 2-3 bets instead of one, appropriately hedging for a genuinely new and uncertain category.
- Builds in an explicit 90-day checkpoint to kill or scale bets based on real customer pull, avoiding sunk-cost commitment to an early bet.
Common mistakes
- Chases too many opportunities at once without a scoring framework to narrow them.
- Ignores Glean's existing assets (connectors, permissions, graph) as a criterion, treating this as a from-scratch product decision.
Likely follow-up questions
- How would you handle two bets that both score well but require the same scarce engineering resource.
- What would make you kill a bet at the 90-day checkpoint despite early enthusiasm.
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More questions from Glean
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