Google AI shopping just dropped: what that means for your ecommerce business.
Written by
Muhammed Al-Refai
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AI isn’t assisting discovery. It’s rewriting it.
Explore how AI is reshaping shopper behavior — and what brands must do to stay visible.
Written by
Feedonomics enables you to enrich and optimize your catalog at scale.
Google unveiled the future of ecommerce search at Google I/O 2025, and it’s powered by AI. With its new AI Mode, agentic checkout, and virtual apparel try-ons, Google is rewriting the rules of online shopping, and ecommerce sellers need to adapt fast.
Let’s break down what’s new, how it works, and what it means for your ecommerce business.
AI Mode is Google’s new search experience, rolling out as a dedicated tab for U.S. users (for now). Think of it as a chat-like interface powered by Gemini 2.5, designed to deliver deep, contextual, and personalized results across shopping, search, and discovery.
Key capabilities that matter for ecommerce:
Query fan-out: AI Mode breaks down complex queries into multiple subtopics and issues parallel searches across the web to return deeply relevant product results. This powers features like Deep Search, which can generate fully cited research reports or detailed shopping guides.
Visual shopping panels: When users express visual intent (e.g., “a cute travel bag for Portland in May”), AI Mode serves browsable product panels personalized to the query’s context, location, and preferences.
Live Search and multimodality: Users can point their camera at objects or settings using Google Lens and ask questions in real time, blurring the line between search and visual discovery. This is especially powerful for retailers with physical products and in-store merchandising.
Shopping Graph scale: Google’s Shopping Graph—it’s machine learning-powered, real-time data set of the world’s products and sellers—now includes over 50 billion listings, refreshed more than 2 billion times per hour. This means AI Mode draws from an incredibly deep and fresh dataset of products, sellers, reviews, and availability.
Custom charts and personal context: AI Mode can analyze complex product-related queries and generate custom graphs (e.g., comparing specs or prices over time). And if users opt in, it can pull context from Gmail and other Google apps to personalize results further, like suggesting activities or shopping based on a travel itinerary.
Agentic capabilities: AI Mode can act on a user’s behalf, like tracking prices, filling out forms, and even completing purchases using Google Pay while keeping the user in control. These agentic actions extend beyond shopping to tasks like booking tickets and appointments.
AI Mode is Google’s clearest signal yet that AI-first search is the future. As more features graduate from Labs into the core experience, ecommerce brands that adapt to these changes will have a competitive advantage. Keep reading to learn more about Google’s new AI shopping features and how you can alter your ecommerce strategy to prepare for the future of shopping.
Google AI Mode is a fundamental reimagining of how people search, discover, and shop online. It requires a fresh look at your AI ecommerce strategy. Below, we explore the key features that ecommerce brands and retailers need to understand and optimize for to stay visible and competitive in this new AI-first landscape.
At the core of AI Mode is Google’s query fan-out technology—an advanced method of breaking down a user’s question into multiple subtopics and running parallel searches across the web. This technique enables AI Mode to understand complex shopping intents and return highly relevant, well-rounded results in seconds.
For ecommerce, this means shoppers aren’t just typing in keywords—they’re describing scenarios, preferences, and needs (e.g., “comfortable running shoes for flat feet and hot weather”). Google’s AI responds by identifying relevant attributes—like arch support, breathability, material, and temperature suitability—then issuing dozens or even hundreds of targeted queries. It synthesizes those findings into a dynamic product panel that reflects the shopper’s specific use case and personal needs.
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Source: Google
Deep Search takes fan-out even further, allowing users to request in-depth comparisons, style guides, or expert-level insights. For sellers, this underscores the importance of product data enrichment: listings with complete, structured attributes are more likely to surface in these advanced AI-generated shopping experiences.
To be competitive, retailers must ensure their product feeds can support this level of semantic understanding—something only possible with high-quality data and taxonomy management at scale.
One of the most visually compelling aspects of Google AI Mode is its ability to surface browsable, image-rich product panels that update in real time as users refine their queries. These panels aren’t static search results—they’re AI-curated shopping experiences, designed to evolve with the user’s intent.
Google uses contextual cues from the conversation to personalize results based on factors like style preferences, seasonal needs, or even the shopper’s location. For example, if a user asks for “a minimalist desk for a small home office setup,” AI Mode can interpret that as a need for space-saving dimensions, clean aesthetics, and possibly cable management. It then serves up products that match those inferred priorities—compact desks, neutral color palettes, and furniture with built-in organization features.
As users engage further—adding clarifications, adjusting preferences, or asking follow-up questions—the product panel dynamically refreshes. This real-time interactivity turns product discovery into a guided journey rather than a traditional scroll-through search.
Product listings that lack high-quality images, variant data, or complete attributes may be filtered out or never surfaced at all. To win visibility in these panels, sellers need:
Multiple product images with varied angles and contexts.
Rich, structured data for attributes like material, style, fit, and color.
Consistent updates to inventory, pricing, and availability.
Google’s virtual “try it on” feature takes the guesswork out of online fashion shopping by letting users see how clothing will look on their own bodies. This state-of-the-art tool leverages a custom image generation model for fashion that understands body proportions, poses, and the way different fabrics fold, stretch, and drape.
What sets this apart from previous try-on tools is its personalization and realism. Shoppers can upload a full-length photo and instantly visualize how billions of apparel listings from Google’s Shopping Graph will look on them, right down to fit, flow, and material behavior.
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Source: Google
While the try-on does not indicate fit or size availability, it helps shoppers better visualize style and how a garment might look on them.
For apparel retailers and DTC brands, this means optimizing your product listings for compatibility with the try-on experience is a competitive differentiator.
To take full advantage of Google’s virtual try-on feature, apparel sellers should ensure their product listings meet Google’s eligibility and image quality requirements. Any merchant with an active product feed showing free listings in eligible categories—tops, bottoms, and dresses—is automatically opted in. While the feature is currently only available in the U.S. and not supported for Shopping Ads, it plays a growing role in organic product discovery across Google Search and the Shopping tab.
To support this experience, follow these best practices laid out by Google:
Upload high-resolution images—at least 1024 x 1024 pixels is ideal.
Ensure images show a single garment only.
Use a front-facing model in a neutral pose (arms down, hood down if applicable).
Avoid covering details with hands, bags, or accessories.
Display the entire garment with minimal wrinkles or visual distractions.
As more customers try on clothes virtually, brands with clean, structured, and visually rich apparel data will be best positioned to turn search interest into conversions, especially for mobile-first, fashion-forward shoppers.
One of the most transformative additions to Google AI Mode is its agentic capabilities, where AI can take action on the shopper’s behalf.
With features like price tracking and agentic checkout, shoppers can specify product preferences—such as size, color, and budget—and allow Google’s AI to monitor listings across the web. When the product matches their criteria and the price drops, AI Mode can:
Notify the user via price drop alerts.
Add the item to the cart on the retailer’s website.
Auto-fill purchase details.
Complete the transaction securely using Google Pay.
Crucially, while AI handles the legwork, the user remains in control, reviewing and confirming purchase details before anything is finalized. These capabilities extend beyond shopping, too. Google has already demoed AI Mode booking event tickets, restaurant reservations, and even local appointments by integrating with partners like Ticketmaster, Resy, and Vagaro.
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Source: Google
Ecommerce sellers should ensure their product listings and checkout flows are compatible with agentic interactions:
Submit structured product data that clearly defines variants such as GTIN, size, color, and material. Google’s AI needs this detail to allow users to track and buy specific configurations—not just the parent product.
Keep pricing and inventory data accurate and up to date. Google’s Shopping Graph refreshes listings billions of times per hour, and outdated availability or pricing can cause friction in the checkout process or prevent your products from surfacing in AI Mode altogether.
Integrate with Google Pay and Google Merchant Center. Agentic checkout flows rely on these systems to add items to the cart and complete the purchase securely. Ensure your account settings, feed attributes, and policies are in full compliance.
Sellers who fail to align with these expectations risk losing conversions, not because their products aren’t appealing, but because their infrastructure isn’t AI-ready.
Behind every AI-powered product experience in Google AI Mode is the Shopping Graph—Google’s massive, real-time dataset of the world’s products, sellers, prices, and availability. According to Google, the Shopping Graph now includes over 50 billion product listings and is refreshed more than 2 billion times per hour.
This scale is what enables AI Mode to deliver fast, accurate, and deeply personalized results. But the graph’s effectiveness hinges on one thing: product data quality.
For ecommerce sellers, this means your ability to surface in AI Mode—and rank well within visual panels, agentic flows, or deep search results—is directly tied to the quality, structure, and completeness of your product data. Listings that are missing key attributes (like material, color, fit, or stock availability) may be excluded or ranked lower in AI-driven shopping results.
To thrive in an AI-powered search ecosystem, brands must:
Maintain accurate, real-time inventory and pricing across all SKUs.
Ensure rich product attributes are standardized and mapped to Google’s requirements.
Deliver high-resolution images and variant-level data to support visual discovery and try-on features.
Leverage feed management platforms to syndicate clean, optimized data across all relevant channels.
In short, the Shopping Graph, just like any AI-driven shopping experience, is only as good as the data it receives. Ecommerce success depends not just on having the right products, but on presenting them in the right way, with the right data, at the right time. That’s where strategic feed optimization becomes mission-critical.
Google’s AI Mode is a whole new way to shop. As the experience rolls out across more product categories and merchant listings, the brands that win will be the ones who embrace structured data, rich product listings, and automation.
Feedonomics’ feed management platform can help you identify and fix data quality issues.
Google AI Mode is a new AI-powered search experience in Google Search that reimagines how users discover, compare, and interact with content, including the shopping experience. While it spans many categories beyond ecommerce, its impact on product discovery is significant by using state-of-the-art AI models to provide visual inspiration, product comparisons, and personalized recommendations.
It’s part of Google’s broader push into artificial intelligence, with capabilities like dynamic product panels, agentic checkout, and virtual try-on features that help users make smarter buying decisions.
The virtual try-on feature uses advanced image generation powered by AI models to realistically simulate how clothes look on different body types. Unlike earlier tools, shoppers can upload their own photos and see how garments drape or stretch based on their real shape. Google said it will not be using user-uploaded images to train its models.
Yes, with Google’s agentic checkout, AI agents can initiate a purchase when a track price notification is triggered. Once the buyer has set their preferences (like size, color, and price), the AI tool can automatically add the product to a cart on the retailer’s site and complete checkout using Google Pay, streamlining the functionality of buying online while keeping the shopper in control.
Retailers will need to adapt to these new features by improving their product data quality and optimizing listings for AI visibility. Structured product feeds, high-quality images, and accurate inventory are critical to ranking in AI product listings.
While Amazon remains a dominant ecommerce destination, its shopping experience is largely confined to its own marketplace. In contrast, Google is leveraging AI to power open discovery across the web through AI Mode. This allows shoppers to find products from a wider variety of retailers—including small and midsize brands—not just those selling on a single platform.
Muhammed Al-Refai