AI Strategy and Model Evaluation

This unit equips students to bridge retail business needs and AI solutions by examining current roles and processes, vendor selection, and the practical challenges of building and evaluating models. Students will learn foundational technical concepts (generalizability, convex optimization, unsupervised learning) and apply decision-cycle frameworks (OODA) and change-management practices to prepare for implementation and advanced model deployment in subsequent units.

Learning Objectives

• Analyze current retail roles, workflows, and inefficiencies to identify specific AI use cases and required changes in processes and responsibilities
• Evaluate AI vendor offerings and selection criteria by comparing cost, scalability, data requirements, performance claims, and alignment with organizational goals
• Apply foundational AI concepts—generalizability, convex optimization, and unsupervised learning—to design experiments and compare model performance using appropriate metrics
• Demonstrate strategies for transitioning to a fact-based, data-driven culture and implement OODA loop practices in retail operations to shorten decision cycles and improve adoption