AI Product Manager
Decide what AI product to build, for whom, and how to measure it.
Problem framing and research, judging feasibility, requirements with evaluation plans, designing AI experiences, quality-cost-speed trade-offs, safe launches, metrics and feedback loops, and working with engineers, legal and leaders. Ends with interview practice.
10 lessons · about 3.1 hours
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What stays the same, what changes with AI and what the role covers.
Find the user's job, write the problem down and test ideas cheaply before building.
Is AI the right tool, a one-day feasibility test and the levels of automation.
Write requirements with examples, a rubric, a test set, failure behavior and budgets.
Set expectations, show your work, handle doubt, ease correction and design the handoff.
Move the triangle with the right levers, and do the unit economics before you scale.
Staged launches, gates, experiments, a readiness checklist and a kill switch.
Measure use, quality, trust and value together, and turn feedback into the roadmap.
How to partner with each group, write decisions down and plan under uncertainty.
Common interview types, a design method, practice cases with strong answers and a portfolio.