Ai Revolutionizes Inventory Management at Retail Giants: Examining Walmart’s U.S. Operations

Abstract

AI has gone from small pilot projects to widespread use in U.S. retail. Walmart is often mentioned as an early adopter of predictive analytics, computer vision, automation, and generative tools to help with inventory management. This article brings together peer-reviewed research on AI-enabled forecasting and inventory management with information from practitioners about Walmart's recent efforts, such as AI assistants for store associates and merchants, RFID-AR item location, digital twins, quality inspection automation, and route/pack optimization. We put Walmart's program in an evidence-based framework that connects data, models, and workflows to key performance indicators (KPIs) that can be measured. We talk about how stockouts and forecast errors have gone down, how excess inventory has gone down, how maintenance has been found sooner, and how task productivity has gone up. Next, we look at ethical and organizational issues like data quality, bias, explainability, human–AI teaming, and change management in a critical way. There are two main parts to the contribution: (i) a practice-oriented synthesis that links academic evidence to a large-scale retail deployment, and (ii) an analytic template for figuring out how valuable AI is in inventory operations. We finish with a research agenda that includes causal evaluation of AI interventions, multi-objective optimization that includes sustainability, and the creation of explainable tools for use by people on the front lines.