Overview

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GenAI for Retail Managers: Optimizing Inventory with AI is a practical, intermediate course that teaches retail professionals how to use Generative AI to improve inventory management, demand forecasting, replenishment planning, and markdown optimization. Through hands-on demonstrations with ChatGPT and Claude, you'll learn to analyze retail data, generate SKU-level demand forecasts, automate reorder recommendations, optimize omnichannel inventory, and measure inventory performance using GMROI and other key retail metrics. No coding is required—just a basic understanding of retail operations. You will build repeatable AI-powered workflows for everyday retail inventory decisions using real-world datasets, guided prompts, and practical business scenarios. The course includes in-video questions, hands-on labs, role-play activities, quizzes, and a course-end project that culminates in a personalized 90-day GenAI implementation roadmap. Designed for retail managers, inventory planners, merchandise analysts, category managers, and retail operations leaders, this course helps you confidently integrate AI into retail operations to reduce stockouts, lower inventory costs, and drive better business outcomes.

Syllabus

Foundations of GenAI for Retail Inventory Management

This foundational module equips retail managers with a clear mental model of how generative AI works and where it creates the most value in the retail inventory lifecycle. Learners explore core GenAI capabilities from demand narrative generation to stockout prediction and automated reporting mapped directly to the stages of assortment planning, purchasing, replenishment, markdowns, and liquidation. Using a real Kaggle retail dataset, learners identify the highest-impact AI opportunities in a store context and build a data-backed ROI business case ready to present to leadership.

AI-Powered Demand Forecasting, Replenishment, and Performance Measurement

This module is the practical engine of the course. Learners use ChatGPT and Claude to build real demand forecasting workflows combining POS history with external signals like weather, events, and promotional calendars. They generate store-level and SKU-level predictions with plain-language explanations, design automated replenishment workflows, model promotional lift, and optimize markdown timing. The module also covers performance measurement: defining and tracking the KPIs that matter most, building AI-generated weekly performance summaries, and creating a phased 90-day GenAI implementation roadmap. All exercises use real Kaggle retail datasets.