Google Unveils AI Shopping Tools
Google has announced new AI-powered shopping features designed to personalize product discovery and streamline e-commerce. As these tools are adopted more widely, they could accelerate markdown cycles and inventory rebalancing, potentially increasing the flow of surplus goods to off-price channels.
These new features are powered by Google's Gemini AI model and leverage its Shopping Graph, a massive database with over 50 billion product listings that is refreshed 2 billion times per hour. The tools introduce a conversational "AI Mode" for search, allowing users to ask for products in natural language, such as "durable, waterproof boots under $150 that are good for small feet." Key functionalities include a virtual try-on for apparel and beauty products, which allows shoppers to see how items look on different body types or even upload their own photo for a more personalized view. Another feature, aimed at tackling price uncertainty, is an agentic tool that can monitor an item's price and automatically complete the purchase when it drops to a user-specified level. At the National Retail Federation (NRF) 2026 conference, Google announced a "Universal Commerce Protocol," an open standard developed with partners including Walmart, Target, Shopify, and Etsy. This protocol creates a shared language for AI agents to communicate across the entire shopping journey, from product discovery to checkout and customer support. The collaboration with Walmart allows customers to link their store accounts to Google for personalized recommendations based on past purchases and to buy items directly through the Gemini chatbot. This move is part of a larger race, with competitors like OpenAI also partnering with Walmart to enable shopping directly within ChatGPT. For retailers, Google is launching "Business Agent," a virtual sales associate that can answer customer questions in a brand's specific voice directly within search results. Additionally, a new "Direct Offers" pilot enables merchants to serve personalized discounts to high-intent shoppers within the AI-powered search results. The underlying technology aims to improve demand forecasting for retailers by analyzing historical sales, real-time demand signals, and market trends. AI-driven inventory management can lead to more accurate predictions, helping to reduce overstocking, stockouts, and the necessity for deep markdowns. Some retailers using AI for forecasting have reported reductions in excess inventory of up to 30%.
