Product design question
How would you improve Microsoft Teams?
- Microsoft
- Product design
- Easy
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
Product sense in an enterprise collaboration tool, where the user is often not the buyer.
How to approach it
- Pick a segment, for example knowledge workers in large meetings versus small team daily standups.
- Identify a pain point: meeting fatigue and difficulty finding decisions or action items after a call ends.
- Prioritize this over cosmetic UI changes, since post meeting follow through is a widely reported enterprise pain point.
- Propose an AI generated meeting summary with action items assigned automatically to mentioned people.
- Explain the benefit: less time spent re-watching recordings or asking 'what did we decide'.
- Define success as adoption of the summary feature and reduction in follow up meetings scheduled.
What a strong answer includes
- Targets a specific enterprise pain point, post meeting follow through, instead of a generic 'make it nicer' answer.
- Considers the IT admin and compliance angle, since enterprise tools need admin controls for AI generated content.
- Gives an illustrative number, e.g. assumes 30 percent of meeting time is spent restating prior decisions.
- Notes the tradeoff between automation usefulness and privacy concerns around recording and summarizing conversations.
Common mistakes
- Ignoring that Teams is bought by IT admins, not just used by employees, and missing the admin angle.
- Proposing a consumer style feature that ignores enterprise compliance and privacy needs.
- No metric tied to actual productivity or time saved.
Likely follow-up questions
- How would IT admins react to this feature?
- What privacy concerns would you need to address?
- How would you measure actual time saved?
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More questions from Microsoft
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