Product design question
How can you improve Google Calendar?
- Product design
- Easy
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What this question tests
Everyday product improvement instinct, checking whether you can name a specific scheduling pain point and a targeted fix instead of generic feature requests.
How to approach it
- Define the user: someone juggling overlapping personal and work calendars, often across multiple accounts.
- Identify the core pain point: finding a mutual free time slot across several people's calendars is still manual and slow, even with existing scheduling tools.
- Prioritize this over cosmetic UI changes, since scheduling friction is the most common complaint about calendar tools generally.
- Propose a feature: an AI-assisted scheduling suggestion that scans all attendees' calendars and proposes the top 3 mutually free slots, factoring in time zone and stated preferences like no meetings before 10am.
- Address a secondary pain point: recovering focus time by letting the calendar auto-defend blocks of uninterrupted work time against new invites.
- Define success as reduced back-and-forth messages to schedule a meeting and increased use of protected focus time blocks.
What a strong answer includes
- Names mutual scheduling friction as the top pain point, the most universally cited calendar complaint, rather than a random feature idea.
- Adds a concrete mechanism, ranked suggested time slots with stated preferences, not a vague 'smarter scheduling' claim.
- Includes a focus-time protection feature, addressing a second real pain point beyond pure scheduling.
Common mistakes
- Proposing purely cosmetic changes like new calendar views with no tie to an actual pain point.
- Ignoring time zone and preference handling, which is where most scheduling tools actually fail.
Likely follow-up questions
- How would you handle a case where no mutually free slot exists?
- How would you measure if focus-time protection actually improves productivity?
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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