AI Product Strategy and Operations

This unit bridges research and production by teaching students how to plan, experiment, and operationalize AI-driven products. Learners will practice synchronizing development and model lifecycles, designing ethical and interpretable models, and creating deployment and monitoring plans that align with product and business goals.

Learning Objectives

• Analyze the AI landscape, stages of the software and model development lifecycles (SDLC and MDLC), and experimentation mindsets to select appropriate development pathways for a given product scenario
• Apply strategic planning techniques to produce a deployment-ready roadmap that integrates research-to-production transitions, interpretability requirements, ethical considerations, and defined success metrics
• Demonstrate the ability to design and execute experimentation protocols for generative and deep learning components, including hypothesis formulation, metric selection, iteration cadence, and documentation of results
• Evaluate model operations and monitoring strategies—such as drift detection, model explainability, and AI-UX product management trade-offs—to recommend actionable steps for scaling, governance, and mitigation of innovator’s dilemma risks