How Walmart uses AI

Walmart Global Tech describes how Walmart's supply chain systems use AI and forecasting models, drawing on signals such as historical sales, seasonality, local demand and weather, to decide which products are needed and where to position them across stores and fulfillment centers before customers order. Walmart says it combines AI foresight with human expertise to refine its demand forecasting, and once an order is placed the Walmart Fulfillment Engine takes over. Walmart gives no forecasting accuracy or inventory figures.

No outcome disclosed.

Walmart launched Sparky in June 2025 as an "Ask Sparky" button in its app across all categories. Sparky answers product questions, compares options, synthesizes reviews and recommends products for an occasion, and Walmart describes a roadmap toward reordering, service booking and multimodal input. It joins Walmart's earlier generative AI features for search, review summaries, product descriptions and comparisons. No outcome figures were published on the launch page.

No outcome disclosed.

Walmart uses several large language models to extract product attributes such as color, size and material from item descriptions and images and to create or improve catalog data. One model extracts the values and a second model, tuned on human validated labels, checks them; values for attributes above an accuracy threshold go into the catalog, and the rest are checked by the quality model, with specialists validating samples. Walmart's chief executive told investors in August 2024 that the work covered more than 850 million pieces of catalog data, and estimated that without generative AI the same work would have needed nearly 100 times the current headcount to finish in the same time.

• Interactions handled: at least 850 million, catalog data points created or improved, reported August 2024

  "We've used multiple large language models to accurately create or improve over 850 million pieces of data in the catalog."

  Claimed by: organization

Walmart uses a chatbot from Pactum to negotiate payment terms and price discounts with tail end suppliers, where buyers lack time to negotiate and around 20% of suppliers had signed standard terms that are often not negotiated. The HBR article describing it is co written by two sourcing leaders at Walmart International and two University of Arkansas professors. Walmart decides the acceptable negotiation trade offs and the bot negotiates and closes agreements within them; the article advises starting in indirect spend categories with pre approved suppliers and scaling by geography, category and use case. The authors report that, so far, the chatbot has closed agreements with 68% of suppliers approached. Pactum, the vendor, reports a 3% average gain across negotiations while extending payment terms by an average of 35 days. The first is a supplier agreement rate and the second a commercial gain on negotiated terms; neither measures how much of the process runs without human touch or what procurement costs to run.

No outcome disclosed.