AI Transformations in Retail Practice

This unit examines historical AI breakthroughs and applies a structured, bake-off framework to evaluate and operationalize AI services (e.g., speech-to-text, translation, recommendation, computer vision) for retail use cases. Students synthesize results into actionable plans and assess performance, costs, and ethical implications to prepare for capstone implementation.

Learning Objectives

• Analyze major AI technology milestones (speech-to-text, machine translation, game-playing AI, transportation and creative AI advances) and infer their practical implications for retail operations and customer experience
• Apply a structured bake-off evaluation framework to compare AI models and services for specific retail problems, documenting quantitative metrics (accuracy, latency, cost) and qualitative trade-offs
• Demonstrate the conversion of AI outputs into actionable retail workflows by designing prototypes or implementation plans (e.g., automated note capture, multilingual support, personalization, inventory or logistics recommendations)
• Evaluate performance, ethical, and business outcomes of proposed AI deployments in retail contexts and recommend optimizations or mitigation strategies (bias, privacy, cost-efficiency, reliability)