Foundations of AI Product Management

This unit establishes the conceptual and practical foundations for turning AI demonstrations into business-ready products by introducing the MDLC, experimentation and research mindsets, and core machine learning concepts including generative AI, explainability, and model drift. Learners will connect AI capabilities to business problem-solving and prepare product-scoped plans that align technical trade-offs with organizational goals, setting up subsequent units focused on detailed design, development, and launch.

Learning Objectives

• Analyze real-world business problems and distinguish which are appropriate for AI solutions by mapping problem characteristics to value, feasibility, and risk
• Demonstrate understanding of core machine learning and generative AI concepts, including the MDLC, explainability, and model drift, by accurately describing their roles and implications in product contexts
• Apply the nine-step framework and an experimentation/research mindset to draft an AI product concept and preliminary development plan that aligns proposed features with measurable business goals
• Evaluate ethical, operational, and performance risks (e.g., explainability gaps, model drift, and unrealistic timelines) and recommend mitigation strategies that preserve business alignment and user trust