AI & Technical question
Suppose Glean wants to launch a proactive enterprise insight feature that flags an emerging organizational issue using signals from tickets, docs, chat, and other connected systems, then recommends next steps. Scope the MVP: who the user is, the end-to-end flow, what must be reliable in v1, which technical constraints you would resolve with engineering and data teams up front, and what you would defer.
- Glean
- AI & Technical
- Hard
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What this question tests
Whether you can scope a proactive AI insight feature responsibly, naming the specific technical constraints and reliability bar needed before a v1 that surfaces organizational issues unprompted.
How to approach it
- User: a functional leader, like a VP of Support or Head of Engineering, who wants early warning on emerging issues without manually digging through tickets, docs, and chat.
- End-to-end flow: the system continuously monitors connected signals for the leader's team, detects a pattern crossing a defined threshold, like a spike in a specific complaint type, then surfaces a flagged insight with evidence and a recommended next step.
- V1 reliability bar: precision matters more than recall here, since a proactive alert that is usually wrong destroys trust fast; it is safer to under-flag confidently than to over-flag noisily.
- Technical constraints to resolve with engineering and data teams: cross-system entity resolution, so a ticket, a doc, and a chat message about the same underlying issue are correctly linked, and a defined latency bar for how fresh the signals need to be.
- Defer to later releases: fully automated recommended actions, expanding beyond the leader's own team signals, and any workflow beyond surfacing and providing evidence, since acting on organizational issues should stay human-led initially.
What a strong answer includes
- Prioritizes precision over recall explicitly for a proactive, unprompted feature, which is the correct call since false alarms are more damaging than a missed one here.
- Names a concrete hard technical problem, cross-system entity resolution, that must be solved for v1 to work at all, showing real technical scoping.
- Explicitly defers full automation of actions to later, keeping v1 focused on detection and evidence rather than overreaching.
Common mistakes
- Optimizes for catching every possible issue (recall) without weighing the trust cost of frequent false alarms.
- Skips naming the hard technical constraint (entity resolution across systems) that actually determines whether this feature is even buildable in v1.
Likely follow-up questions
- How would you tune the threshold that decides an issue is worth flagging.
- What would you do if entity resolution across systems proves too unreliable for v1.
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 1: Foundations: the model and the decisions it forces on you
- Chapter 8: Evals: define good and make the number defensible
- Chapter 6: Agents and agentic architecture