Google Says Product Data Is Infrastructure, Not Just a Feed

Google just said the quiet part out loud. In a 40-minute Ads Decoded briefing, Google VP Courtney Rose revealed that Merchant Center product data now feeds AI Mode, Gemini shopping, virtual try-on, Business Agent, brand profiles, and free listings. One product feed. Six-plus surfaces. And Google is adding "conversational attributes," a new category of product data designed specifically for AI retrieval. If your feed is messy, you're not just missing search results anymore. You're invisible across every AI-powered surface Google operates.

TL;DR: Google confirmed that Merchant Center data now powers AI Mode, Gemini, virtual try-on, and more. New "conversational attributes" are coming, designed specifically for AI shopping. Only 18% of product pages have complete schema markup, meaning most products are invisible across all of these surfaces simultaneously.

What Are "Conversational Attributes" and Why Do They Matter?

Google's Ads Decoded presentation introduced conversational attributes as a new field category in Merchant Center. These aren't your standard product attributes like color, size, or material. They're built for how AI models retrieve and discuss products in conversation.

Think about the difference between a traditional product listing and how someone actually asks about a product. A consumer doesn't search "running shoes size 10 black." They ask AI Mode: "What's a good trail running shoe for someone with wide feet who hikes on rocky terrain?" Standard attributes can answer the first query. Conversational attributes answer the second one.

Google revealed that 15% of daily searches are entirely new queries the platform has never seen before. These novel queries are conversational by nature, and they're growing. If your product data only covers traditional structured fields, you're leaving those 15% on the table entirely.

Key Stat: 15% of Google's daily searches are entirely new, meaning no keyword history exists. Products need conversational context, not just structured attributes, to surface in these queries.

How Does One Feed Power Six Google Surfaces?

The shift is subtle but massive. Merchant Center used to be "the place you upload your Google Shopping feed." Now it's the single data source for everything Google does with product information. Here's where your Merchant Center data shows up as of April 2026:

AI Mode (Search): Google's conversational search experience, where 93% of queries end without a click to external sites
Gemini Shopping: Product recommendations within Google's AI assistant
Virtual Try-On: Google Lens integration letting shoppers visualize products
Business Agent: Google's AI that interacts with shoppers on behalf of merchants
Brand Profiles: AI-curated brand pages in search results
Free Listings: Traditional Shopping tab results, still powered by the same data

The implication is straightforward. A gap in your Merchant Center data doesn't just affect one channel. It cascades across all six. Missing a product description? You're invisible in AI Mode, won't appear in Gemini recommendations, and your Business Agent has nothing useful to say about that product.

Who's Already Moving on This?

Gap became the first major fashion retailer to launch checkout within Google Gemini in March 2026, powered by Google's Universal Commerce Protocol (UCP). Shoppers can browse Gap's catalog across Old Navy, Gap, Banana Republic, and Athleta, get AI styling recommendations, and buy without switching to a separate site.

In India, Google rolled out its most advanced AI shopping features including a Gemini Shopping Assistant that handles complex queries like "Find me a laptop under ₹60,000 for video editing available for same-day delivery in Bhopal." Google is targeting 10 million new merchants in India by end of 2026, using auto-generated product descriptions from smartphone photos to solve the data quality problem for smaller merchants.

This India rollout is a preview of what's coming globally. Google is building tools to generate conversational product data at scale. Brands that already have rich product data will stay ahead. Those relying on auto-generation will get baseline coverage but no competitive edge.

"2025 will likely be the last year consumers shop as they do now."
- SAP Emarsys

Meanwhile, Walmart captures 20% of ChatGPT referral traffic and has dual AI platform presence on both ChatGPT and Gemini. They got there by treating product data as a strategic asset years before their competitors did. If you're a mid-market brand competing for the same categories, Walmart is getting the AI recommendation your brand should be earning.

Why Does the 18% Schema Stat Matter Here?

A recent analysis found that only 18% of e-commerce product pages have complete schema markup. For context, schema markup is the structured data that tells AI systems what your product is, what it costs, whether it's in stock, and what its specifications are. Without it, AI agents are essentially guessing.

This 18% figure matters for Google specifically because Merchant Center data, schema markup, and on-page structured data all feed the same AI surfaces. Google's Shopping Graph contains over 50 billion product listings with 2 billion listings updated per hour. If your data in that graph is incomplete compared to a competitor's, the AI will always surface the richer listing.

Brands that audited and rebuilt their product feed schema saw a 23% revenue lift by becoming discoverable across ChatGPT Shopping, Google Shopping, and Perplexity Shopping simultaneously. The fix isn't complicated. It's just work most brands haven't done yet.

Key Stat: Google's Shopping Graph contains 50+ billion product listings updated 2 billion times per hour. Incomplete data in this graph means your products are deprioritized across every Google AI surface.

What Should Retailers Do About This?

Stop treating your Merchant Center feed as a compliance checkbox. Google has made it clear: this is the foundation for every AI shopping experience they're building. Here's what actually moves the needle:

Audit your current state. Run a free AI readiness report at paz.ai to see how your products score across Product Mapping, Structured Attributes, Attribute Context, and Product Context. Most brands discover they have 5 to 8 structured attributes per product when AI surfaces work best with 30 or more.

Fill the conversational gaps. When Google rolls out conversational attributes in Merchant Center, the brands that already have rich product descriptions with context (use cases, comparisons, suitability) will have a head start. Start adding this context now.

Distribute to every AI surface. Your product data doesn't just need to live in Merchant Center. ChatGPT, Perplexity, and Microsoft Copilot are all pulling product data through their own protocols. Paz.ai optimizes and distributes your catalog to Google AI, ChatGPT, and other AI shopping channels through a single integration.

Google told you the rules. Product data is infrastructure. The brands that treat it that way will own every AI surface. The brands that don't will keep wondering why their traffic is declining while competitors capture the AI-referred visitors who convert 38% better than traditional search traffic.

Frequently Asked Questions

What are conversational attributes in Google Merchant Center?

Conversational attributes are a new category of product data fields announced by Google, designed specifically for AI retrieval in conversational interfaces like AI Mode and Gemini. Unlike traditional attributes (color, size, price), conversational attributes capture product context, use cases, and suitability information that AI models need to answer natural language shopping queries.

How many Google surfaces use Merchant Center data?

As of April 2026, Merchant Center product data powers at least six Google surfaces: AI Mode (Search), Gemini Shopping, virtual try-on (Google Lens), Business Agent, brand profiles, and free listings. A single data gap cascades across all six surfaces.

What percentage of product pages have complete schema markup?

Only 18% of e-commerce product pages have complete schema markup as of April 2026, according to an upGrowth analysis. This means 82% of products are missing structured data that AI agents need to discover and recommend them.

How did Gap use Google's Universal Commerce Protocol?

Gap became the first major fashion retailer to offer checkout within Google Gemini in March 2026, using the Universal Commerce Protocol (UCP). Shoppers browse products from Gap, Old Navy, Banana Republic, and Athleta, receive AI styling recommendations, and purchase directly.

Does product data quality affect AI Mode rankings?

Yes. Google's Shopping Graph contains over 50 billion listings, and AI Mode surfaces the products with the richest, most complete data. Brands with comprehensive structured attributes, detailed descriptions, and conversational context consistently outperform those with thin product feeds.

How can I optimize my product data for Google AI surfaces?

Start with a free AI readiness audit at paz.ai to identify gaps in your structured data. Focus on expanding from basic attributes (5 to 8 per product) to comprehensive data (30+ attributes). Add conversational context, including use cases, suitability information, and comparison points that help AI answer natural language queries.