The news: Walmart is looking to use in-store data to train and improve its Sparky AI shopping assistant. That could include information on what shoppers use their phones to search for in-store, conversations they had with store associates, and things they considered buying.

“What ends up happening is we have the totality of all the interactions that customers have with us, whether it’s online, in the store, or elsewhere, and then we’re able to build together the context and the memory of the customer,” Walmart CTO Hari Vasudev said, per The Information.

What it means: Walmart is trying to optimize around all shopper interactions, including the ones that don’t lead to purchases, to improve Sparky’s understanding of each customer. This is especially important amid the risk that retailers will become interchangeable checkout engines as ChatGPT, Gemini, and other assistants become primary discovery interfaces.

But Walmart has an advantage with its thousands of physical stores and in-person, real-time shopper behavior—something Amazon, OpenAI, and Google can’t match. Other retailers could follow Walmart’s example and capitalize on their own unique, physical first-party data to boost their AI engine training for a competitive edge as agentic shopping grows.

Implications for retailers: Superior customer data, inventory visibility, and cross-channel integrations could help retailers dominate AI-routed transactions amid the competitive battle for customer context. This dual-channel information, synthesized by Sparky, can also help inform personalization offerings such as targeted loyalty deals and in-app recommendations.

In addition, if Walmart can connect online activity, store visits, employee interactions, and purchase data, it can feed that data to Walmart Connect and provide advertisers with a more comprehensive view of the shopping journey than purchase history alone.

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