AI Innovations and Retail Implementation

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This unit examines landmark AI advancements (speech-to-text, translation, game-playing, transportation, creative AI) and uses a structured bake-off framework to evaluate and select solutions for retail applications. Students synthesize case studies into actionable integration plans, pilot evaluation metrics, and scale-up recommendations that address business impact, technical requirements, and ethical considerations.

Learning Objectives

Analyze six historical AI advancements (speech-to-text 2013, translation 2014, Go 2016, air travel 2019, cars 2020, art/game development 2022) and summarize their implications for retail operations, customer experience, and business models.
Apply a bake-off framework to compare at least two candidate AI solutions for a defined retail problem using measurable criteria (accuracy, latency, cost, scalability, data needs, and compliance).
Demonstrate development of an actionable integration plan for a selected AI capability in retail that includes data requirements, deployment tasks, stakeholder roles, timeline, pilot KPIs, and change-management steps.
Evaluate pilot results by measuring performance against KPIs, estimating ROI, identifying ethical or regulatory risks, and proposing prioritized improvements for scaling and continuous monitoring.