Terry Clear returns to 3-Minute Insights discuss the groundbreaking partnership between Walmart and Google’s Gemini AI platform on Three Minute Insights.
Lainie: How might this new partnership change product discovery for suppliers?
Terry: It’s a shift from a traditional traffic-generation approach, directing website visitors to a particular product or category, to an in-stream conversation. Think about how people interact differently with a website or search engine versus an AI like Gemini or ChatGPT.
When I use a search engine, I’m very specific but also a bit stilted in my wording. But when I talk to an AI model, it’s far more conversational. Traditional product search might look like typing “blue shirt,” hoping to be directed to a section where I can browse blue shirts. A conversational search, on the other hand, might sound more like, “What batteries do I need for this toy?”
Lainie: How should suppliers prepare for this new agentic e-commerce environment?
Terry: It all goes back to their item file and how they describe their products. Part of this Walmart–Google partnership involves developing the Universal Commerce Protocol, which will define how items are discovered. Suppliers will ultimately create or complete a machine-readable JSON file that includes additional details beyond the standard Walmart item file.
This extra information will make the data more robust and give AI agents more to work with when evaluating products—helping them better direct search results to a supplier’s items. This is all brand new, and we’ll see more clarity emerge in the coming weeks and months.
Lainie: Finally, how can AI-powered personalization reshape the relationship between suppliers and retailers?
Terry: We should actually include the consumer in that relationship too, because AI will make all three connections—supplier, retailer, and consumer—much more dynamic. Reliability will center around product listings and availability. We can’t rely on overnight inventory updates anymore; it needs to be nearly real-time.
If someone buys an item, that change in inventory must be reflected immediately so consumers and AI systems alike have high confidence in product availability. On top of that, suppliers now have the opportunity to highlight value rather than just price. When people interact with AI models like Gemini, they often ask, “What’s the best option?” rather than “What’s the cheapest?” Suppliers can position their products in terms of quality and features—not just cost.
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Lainie: You mentioned two things that stood out to me. One is the dynamic nature of this shift—conversations are by nature back-and-forth interactions. In this new model, the consumer becomes an active participant, expanding and energizing the conversation.
The other point is the importance of timely product availability. Could it be that if someone is conversing with Gemini and a product is recommended but turns out to be unavailable, that could decrease their trust in the model?
Terry: Yes—and it could also cause the model to deprioritize that supplier’s items in future recommendations.
Think about how we interact with AI today: you rarely get the best answer from your first prompt. It often takes two, three, or even four iterations before you refine your question enough to get what you’re really after. The same will happen with item search—results will evolve as the prompts evolve. Users will continue refining their queries, and the model’s responses will adapt in real time, driving better and more precise product matches.
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