Demonstrating Hands-On AI Fluency in Your Everyday Product Management Work

The interviewer opens with a broad question: how do you use AI? Answer from your own experience as a product manager. ```hint Show a few workflows in depth Instead of listing every tool you have tried, choose two or three tasks you now do differently because of AI. Be ready to say what the tool produced, how you checked it, and what changed. ``` ### Clarifying Questions - Are you asking about my own day-to-day use of AI at work, or also about AI features I have built into products? - Should I include how I use AI outside work, for example to learn a new domain or build side projects? - How much detail helps: the tasks and tools, or also how I set up context, write prompts and check the output? ### What a Strong Answer Covers - Specific, recent workflows tied to real product work (discovery, analysis, specs, prototyping, communication) rather than a list of tool names - How outputs are checked, and where the candidate deliberately does not rely on AI, such as confidential data or decisions they must own - Honest evidence of impact on speed or quality, including at least one case where the tool got something wrong - A working grasp of how models behave and fail (context, hallucination, prompting) deep enough to inform building AI products - What hands-on use has taught the candidate about how people experience AI, and how that would shape product decisions at a company whose product is AI ### Follow-up Questions - Tell me about a time an AI tool gave you a confident but wrong answer. How did you catch it, and what did you change afterward? - If you could add one AI capability to your own workflow that no tool offers today, what would it be, and how would you judge whether it works? - How do you decide which customer or company data is acceptable to share with an AI tool?