AI Strategy and Model Evaluation

This unit builds organizational and technical fluency required to move retail teams from intuition-driven to data-driven decision making. Students will learn how to identify process inefficiencies, evaluate AI vendor options, and understand core machine learning concepts (convex optimization, unsupervised learning, generalizability and model comparison) while applying OODA and change-management techniques to plan adoption and stakeholder education.

Learning Objectives

• Analyze retail roles, processes, and inefficiencies to identify at least three opportunities where AI or analytics can produce measurable improvements, citing evidence from the Baseball 2002 case study or classroom data.
• Evaluate and compare AI vendor offerings using a rubric that includes cost, data requirements, scalability, generalizability, and governance, and produce a justified vendor selection recommendation.
• Demonstrate understanding of core machine learning concepts — convex optimization, unsupervised learning, generalizability, and performance metrics — by explaining each concept and applying them to compare two model approaches in a provided scenario.
• Apply OODA loop and change-management principles to design a semester-level implementation plan that educates stakeholders, establishes fact-based metrics, sequences experiments, and defines monitoring for iterative improvement.