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
Write a clear role and a fair, structured way to evaluate candidates.
Identify, rate and plan responses to project or business risks.
Turn a vague ambition into measurable objectives and key results.
Turn a process you do by hand into a checklist anyone can follow.
A candid, metrics-first update that builds trust.
Turn rambling notes into decisions, owners, deadlines and open questions.
A comparison that ends in decisions, not a table of facts.
Set prices using value, competitors and customer segments.
A tight plan you can write in an afternoon and defend in five minutes.
A side-by-side comparison to spot gaps and positioning opportunities.
A monthly/quarterly update that respects investors' time.
A structured strengths/weaknesses/opportunities/threats breakdown.
Turn a raw transcript into decisions, action items, and owners.