Quick Decision Framework
- Who This Is For: Shopify merchants and DTC operators actively running Google Shopping ads or free listings who manage their own product feeds or work with a feed management tool and want to understand how Google’s AI-powered shopping surfaces are changing what product data actually needs to say.
- Skip If: You are not yet running Google Shopping or have fewer than 50 SKUs in your catalog. Get your foundational feed structure right first, then return to this when you are ready to optimize for AI retrieval.
- Key Benefit: A clear understanding of what Google’s new conversational attributes mean for your feed architecture, which specific data gaps make your products invisible in AI Mode and Gemini Shopping, and the four concrete steps to start enriching your feed before the formal spec drops.
- What You’ll Need: Access to your Google Merchant Center account, your current product feed (Shopify feed, supplemental feed, or third-party feed management tool), and the ability to add custom attributes or supplemental feed data to your existing product data.
- Time to Complete: 9 minutes to read. Feed audit and initial enrichment: 2 to 5 hours depending on catalog size and your current feed management setup.
Google just told merchants that product data quality determines whether they exist in AI commerce at all. That is a different sentence than anything they have said about Shopping before.
What You’ll Learn
- Why Google’s conversational attributes represent a fundamental shift in how product feeds work, moving from keyword matching to structured AI retrieval across seven distinct shopping surfaces.
- What the seven surfaces now powered by a single Merchant Center feed are, and which ones require conversational attribute depth to surface your products at all.
- How to audit your current feed for attribute depth and identify the specific gaps that make your products invisible to AI Mode and Gemini Shopping queries.
- Which attribute types map most directly to natural language shopping queries, including use-case fields, compatibility data, and specification-level detail that AI agents can actually parse.
- What steps to take right now before Google publishes the formal conversational attributes spec, so you are building toward compliance rather than scrambling to catch up.
Google is adding a new category of product data fields to Merchant Center called “conversational attributes.” These are structured fields designed specifically for how AI retrieves products in natural language conversations, not keyword searches.
Google VP Courtney Rose confirmed during the 2026 Retail Ads Decoded session that Merchant Center product data now feeds seven surfaces: AI Mode, Gemini shopping, virtual try-on via Google Lens, Business Agent, brand profiles, free listings, and Shopping ads. One feed, seven surfaces. Conversational attributes are built for the AI-powered ones.
If you manage product feeds, this changes how you think about data architecture.
What Are Conversational Attributes?
Conversational attributes are structured product data fields optimized for conversational AI retrieval. They go beyond standard Merchant Center fields like title, price, GTIN, and brand to include the kind of detail an AI needs to answer natural language shopping queries.
Traditional Shopping ads match keywords to product titles. AI parses structured attributes to answer queries like “What are the best affordable trail running shoes for wet conditions?”
To answer that, the AI needs to evaluate waterproofing specs, weight, terrain compatibility, and price as separate, queryable fields.
How Should I Optimize My Product Feed for Conversational AI?
Google hasn’t published the full conversational attributes spec yet. But based on how AI Mode currently retrieves products, there are clear steps to prepare.
Audit your attribute depth. Count structured attributes per product in your feed. Under 15 means you’re likely invisible in AI Mode for anything beyond basic queries.
Add use-case and context fields. “Intended use,” “best for,” “season,” “compatible with” map directly to conversational queries. Include them in supplemental feeds or custom attributes.
Think in questions, not keywords. For each product, write down the five most likely natural language questions a shopper would ask. Check if your feed data could answer them. Gaps point to missing attributes.
Use specifics, not descriptors. “Waterproof” is a descriptor. “IPX4 rated, seam-sealed, tested to 10,000mm water column” is a specification. AI agents parse specifications.
What Should I Do First?
Google has been telling merchants for years that product data quality matters for Shopping ads. Now Google is telling merchants that product data quality determines whether they exist in AI commerce at all.
The conversational attributes rollout is coming. The merchants who start enriching now will be ready. The ones who wait for the formal spec will be catching up.
Check where your products stand with a free AI readiness score at paz.ai. Enter any product URL, get a full breakdown of what AI agents see and what’s missing in 30 seconds.
Author
Dor Shany – CEO of Paz.ai
Frequently Asked Questions
What are Google’s conversational attributes and how are they different from standard Merchant Center fields?
Conversational attributes are structured product data fields designed for AI retrieval rather than keyword matching. Standard Merchant Center fields like title, price, GTIN, and brand were built for a system that matches search terms to product titles. Conversational attributes go deeper, covering use-case context, compatibility data, specification-level technical detail, and lifestyle fit fields that allow an AI agent to evaluate whether a product answers a shopper’s natural language query. The key difference is that AI Mode and Gemini Shopping do not retrieve products by matching keywords. They evaluate structured attributes to determine which products best answer a specific question, which means products without sufficient attribute depth simply do not appear in AI-powered results.
How many attributes does my product feed need to be visible in Google AI Mode?
Based on how AI Mode currently retrieves products, fewer than 15 structured attributes per SKU is a strong signal that your products are likely invisible in AI Mode for anything beyond the most basic queries. This is not an official threshold from Google, but it reflects the practical reality that conversational queries require enough attribute data to evaluate product fit across multiple dimensions simultaneously. A shopper asking about trail running shoes for wet conditions needs the AI to evaluate waterproofing rating, terrain compatibility, weight, and price as separate fields. If those fields are not in your feed as discrete structured values, your products cannot be confidently included in the AI’s answer. Audit your current attribute count per product and prioritize enrichment for your highest-revenue SKUs first.
What are the seven surfaces that Google Merchant Center now powers?
Google VP Courtney Rose confirmed at the 2026 Retail Ads Decoded session that one Merchant Center feed now powers seven surfaces: AI Mode, Gemini Shopping, virtual try-on via Google Lens, Business Agent, brand profiles, free listings, and Shopping ads. The AI-powered surfaces, specifically AI Mode, Gemini Shopping, and Business Agent, are the ones that require conversational attribute depth to surface your products effectively. Shopping ads and free listings continue to operate on keyword-to-title matching logic, though richer attribute data improves performance there as well. Virtual try-on and brand profiles have their own requirements around image quality and brand identity fields.
How do I add conversational attributes to my Shopify product feed?
The most practical path for Shopify merchants is supplemental feeds submitted through Google Merchant Center. Supplemental feeds allow you to add custom attributes and additional structured fields to your existing product data without rebuilding your primary Shopify feed from scratch. Use-case fields like “intended use,” “best for,” “activity type,” and “compatible with” can be added this way, as can specification-level technical detail that is not captured in your standard Shopify product data. Feed management tools like Feedonomics, DataFeedWatch, and GoDataFeed can help you structure and manage supplemental feed submissions at scale. For a quick read on where your current product data stands with AI agents, paz.ai offers a free product URL audit that shows what AI agents see and what is missing.
What is the difference between a product descriptor and a product specification for AI feed optimization?
A descriptor is a qualitative label: “waterproof,” “durable,” “comfortable,” “fast.” A specification is a measurable, verifiable value: “IPX4 rated, seam-sealed, tested to 10,000mm water column,” “1,000-denier Cordura nylon,” “memory foam with 3-inch profile and CertiPUR-US certification.” AI agents parse specifications because they can evaluate them against a shopper’s stated requirements. They cannot reliably evaluate descriptors because descriptors are subjective and do not map to specific shopper needs. When you are enriching your feed for conversational AI retrieval, the goal is to replace or supplement descriptors with specifications wherever your product has measurable attributes that could answer a specific question a shopper might ask.
