AI Foundations for Retail Operations

This unit builds on introductory AI concepts to examine how AI can transform retail roles, processes, and decision-making. Students learn to evaluate AI vendors and models, understand core technical concepts (generalizability, convex optimization, unsupervised learning), and design a data-driven operational and cultural transition using OODA loop principles.

Learning Objectives

• Analyze existing retail roles, workflows, and inefficiencies to quantify opportunities where AI solutions could improve key performance indicators
• Evaluate AI vendor proposals and selection criteria using a rubric aligned to retail use cases, data requirements, and implementation constraints
• Demonstrate understanding of core machine learning concepts (generalizability, convex optimization, supervised vs. unsupervised learning) by comparing model performance metrics on sample retail datasets
• Apply OODA loop and change-management principles to create a measurable plan for educating staff and transitioning the organization to a fact-based, data-driven culture