Foundations of AI Product Management

This unit introduces the role, mindset, and core processes needed to move from AI demos to impact-driven products. Students learn foundational concepts—MDLC, experimentation, explainability, model drift, and how AI aligns with business goals—preparing them to plan experiments and define early-stage product requirements in subsequent units.

Learning Objectives

• Analyze distinctions between prototype/demo capabilities and production-ready AI products and map AI use cases to specific business problems and stakeholder needs
• Demonstrate understanding of the Machine Development Life Cycle (MDLC) and the nine-step framework by outlining phases, key activities, and deliverables for an AI product concept
• Apply an experimentation and research mindset to design a basic ML experiment, specifying hypotheses, success metrics, data requirements, and considerations for explainability and drift monitoring
• Evaluate proposed AI solutions for alignment with business goals by assessing value proposition, risks (including explainability and model drift), timelines, and required cross-functional roles