Product discovery is getting harder to see.
Not because shoppers have stopped searching, but because more of that search behaviour is moving into AI-driven answers, comparison prompts, and conversational shopping paths where traditional reports were never built to look. Google is now preparing to give merchants a clearer view into that shift through new AI performance insights in Merchant Center.
The reporting feature, announced for Merchant Center and expected to roll out in the coming months, is designed to show how brands and products are being discovered across AI Mode, AI Overviews in Search, and the Gemini app. For retailers, the bigger change is not another dashboard. It is the arrival of measurement for shopping journeys that no longer start with a clean keyword, a standard results page, or a familiar Shopping tab interaction.
Merchant Center Is Moving Closer to AI Search Measurement
Merchant Center has long been the operational hub for product feeds, pricing, availability, shipping details, images, and other structured commerce data. That role is expanding.
Google’s new AI performance insights are intended to help brands understand their visibility on AI-powered surfaces, including shopping journeys that begin inside AI Mode, AI Overviews, or Gemini. Instead of only looking at product approvals, feed errors, clicks, or campaign performance, merchants will be able to examine how their brand appears when users ask broader, more conversational shopping questions.
That distinction matters.
A shopper might not search “men’s waterproof trail shoes size 10” anymore. They may ask which shoes are best for wet weekend hikes near Toronto, compare brands in a conversational result, refine by material or colour, then move toward a purchase path only after an AI system has narrowed the field. In that environment, product visibility is shaped by far more than bids and titles.
For advertisers already following Google’s broader AI commerce push from Google Marketing Live 2026, the Merchant Center update fills in a missing layer: reporting for how products and brands perform inside AI-mediated shopping experiences.
Google has been moving steadily toward a commerce model where product feeds, AI recommendations, automated campaigns, and conversational interfaces work together. AI performance insights bring that strategy into measurement.
Share of Voice Comes to AI Shopping Results
One of the most important pieces of the new report is share of voice.
Google says merchants will be able to see brand visibility across AI-driven experiences in Search and Gemini, benchmarked against similar brands. The shopping journeys covered include those starting from AI Mode, AI Overviews, and the Gemini app.
That creates a new kind of competitive signal for retailers. Instead of asking only whether a product received impressions or clicks, merchants can begin asking whether their brand is being included when AI systems assemble answers, recommendations, comparisons, and product options.
For retailers, share of voice in AI shopping may become a practical proxy for discoverability. If a brand is absent from conversational results where similar competitors appear, the issue may not be campaign spend alone. It could point to product data quality, missing attributes, weak feed coverage, unclear descriptions, or gaps in how products map to user intent.
This is where AI shopping agents change the measurement conversation. Once AI systems begin filtering choices before a shopper reaches a conventional product grid, retailers need to know whether they are present in the recommendation layer at all.
The report will not replace campaign-level reporting. It sits earlier in the decision path, closer to discovery and evaluation. That is the layer many retailers have struggled to measure as AI search features moved into mainstream consumer behaviour.
Product Attributes Are Becoming a Visibility Problem
The update also puts product attributes under sharper scrutiny.
Google says the report will include product attribute insights that identify popular specifications users search for, such as colour, style, or material. Merchants will also be able to use an attribute completeness score to see which products are missing structured attributes.
That is a quiet but important shift.
In traditional search and shopping campaigns, incomplete product data could still perform if titles, images, pricing, and bids were strong enough. In conversational shopping, missing attributes can become a larger liability because the user’s query often includes context rather than exact product language.
A person might ask for “a lightweight linen shirt for humid weather,” “a modern black faucet that works with a small vanity,” or “a backpack that fits a 16-inch laptop and does not look bulky.” Those are not just keywords. They are attribute-heavy requests.
If the feed does not clearly describe material, size, compatibility, style, fit, or use case, Google’s systems may have less structured evidence to connect the product to that request. The product may still exist. It may even be a good match. But the AI layer has to understand that match before the shopper ever sees it.
That places product feed management closer to ecommerce marketing strategy. Feed completeness, structured data hygiene, product naming, and descriptive accuracy are becoming part of how brands compete for AI visibility.
Funnel Reporting Will Track Discovery, Evaluation and Purchase
Google’s AI performance insights will also include shopping funnel performance, with reporting across discovery, evaluation, and purchase stages.
That matters because AI-assisted shopping rarely behaves like a single-session product search. A user may begin with a broad need, evaluate several brands through follow-up prompts, ask for pros and cons, compare prices, look for local or delivery options, and only then click or buy.
Conventional reporting often compresses that behaviour into late-stage metrics. AI shopping reporting appears designed to expose more of the earlier path.
Discovery tells retailers whether products are being surfaced when the shopper is still exploring. Evaluation shows whether products remain visible as users compare brands or attributes. Purchase-stage reporting helps connect those AI interactions closer to commercial intent.
For marketers, the value is not only knowing whether AI surfaces sent traffic. It is knowing where the brand fell out of consideration.
A retailer might have strong visibility during discovery but lose ground when users compare specifications. Another may appear during evaluation but fail to convert because product data, price, availability, shipping, or offer information is weaker than competitors. Those are different problems, and they require different fixes.
The report’s product term insights add another layer. Google says merchants will be able to identify popular product terms searched by users across Search conversations, along with share of voice for those terms. That may help retailers see how conversational demand differs from legacy keyword reporting.
Practical Implications for Retail Marketers
For retail marketers and SEOs, the practical takeaway is straightforward: product data quality is becoming measurable in AI search environments. Merchant Center feeds should be reviewed for missing attributes, vague descriptions, inconsistent taxonomy, weak product titles, and incomplete specifications. Campaign teams should also compare AI visibility signals against existing Shopping, Performance Max, and organic product performance to understand whether discoverability gaps are appearing before the click. This is not a replacement for standard reporting, but it gives marketers another view into how AI-driven product discovery is forming across Google surfaces.
The New Report Gives Retailers a Starting Point, Not a Full Map
Google’s rollout is still limited by geography and timing. AI performance insights are coming first to the U.S., Canada, Australia, India, and New Zealand, and Google has not said that every merchant will receive the same depth of reporting at launch.
There are also open measurement questions. Share of voice inside AI shopping is not the same as impression share in a classic auction. AI responses can vary by query phrasing, user context, location, product availability, and the surface where the conversation begins. Retailers will need time to understand how stable the reporting is and how directly changes in product data affect visibility.
Still, the direction is clear.
Google is treating AI shopping as a measurable commerce environment, not just an interface experiment. Merchant Center is becoming the place where retailers manage the data AI systems use and, increasingly, where they see whether that data is enough to keep products visible.
For brands selling through Google, the next visibility problem may not begin in the ad auction. It may begin inside the product feed, long before the shopper reaches a product page.
