Real-world Foundations of AI Product Management

AI product success depends on navigating constraints that don’t exist in traditional software: data readiness, probabilistic outputs, and ongoing monitoring after launch. This course provides practical frameworks for taking AI ideas to production — scoping the problem, writing AI-ready requirements, prototyping and validating early, and defining success metrics beyond accuracy. It emphasizes collaboration with engineering and data teams and making the tradeoffs explicit: quality vs speed, automation vs human judgment, and risk vs reward. Best for beginners moving into AI product management who want a real-world operating model for building, launching, and iterating AI-enabled features.