Google is exposing new AI shopping and search data. The signal is incomplete, but it changes how marketers should prioritize product data this week.
Google has started opening up AI performance insights in Merchant Center, a pilot reporting surface that shows how products are discovered across AI Mode, AI Overviews, and the Gemini app. The report is currently in a limited pilot for some Merchant Center accounts in the U.S., with rollout planned to more countries in the coming months.
This matters because it is the first time Google has given retailers any direct view into query-like demand on these AI surfaces, even if the data is grouped rather than exposed as raw searches. Google also launched separate Search Console reporting for visibility in generative AI features on Search and Discover, which means the company is now building a reporting layer around AI-generated search experiences on both the retail and publisher sides.
For marketers, the shift is not that Google has finally made AI search measurable end to end. It has not. The Merchant Center report shows visibility and query themes, but it does not show whether AI Mode or AI Overviews actually sent traffic or revenue to your site. The report is therefore a directional signal, not a full attribution system.
That limitation is still useful. Google is surfacing how shoppers phrase needs inside AI experiences, which gives teams a new way to identify the vocabulary customers use when they are exploring categories, comparing specs, or evaluating options. In practical terms, that means marketers can stop guessing which product attributes need emphasis and start seeing which terms and specs show up often enough to justify listing changes.
The bigger strategic point is that Google is moving from static search reporting toward journey-based visibility. Instead of only ranking pages or keywords, the new reporting breaks activity into discovery, evaluation, and purchase stages, as well as query types and product terms. That is a strong signal that AI SEO and product feed optimization are converging.
The Merchant Center report includes four useful areas: share of voice, shopping funnel performance, product term insights, and product attribute insights.
Google says this shows how visible your brand is across AI-driven experiences compared with similar brands. That is useful for benchmarking, but it is still a visibility metric rather than a traffic metric.
The report groups queries into discovery, evaluation, and purchase stages. This helps teams see whether a product is being surfaced early in the journey or only when shoppers are closer to buying.
Google says this surfaces popular product terms used in conversational shopping queries and your share of voice for those terms. Brodie Clark noted that Google groups these questions rather than listing them individually, so the output is closer to category vocabulary than a raw query log.
Google also identifies attributes shoppers ask about, such as color, style, and material, and points out missing structured data through an attribute completeness score. That is the most actionable part of the report for many teams because it translates conversational demand into feed work.
The immediate implication is that product data quality now affects discoverability in AI surfaces more directly. If your feed is missing important attributes, Google is explicitly telling you that those gaps may limit how well AI shopping systems can match your products to conversational requests.
For local businesses, the same directional shift is happening in Google Business Profile. Businesses that treat GBP as a live engagement channel are outperforming competitors who still manage it like a one-time directory listing.The new Google Business Profile playbook for AI local search argues that Google now rewards ongoing activity, not static setup. In other words, Google’s surface area for discovery is becoming more dynamic across both local and retail intent.
There is also a legal and operational backdrop here: Google’s ongoing fight with SerpApi shows that scraping public search results remains contested, but the court dismissed Google’s DMCA claims where no copyrighted content was involved.Court dismisses Google’s DMCA claims against SerpApi For marketers, that reinforces a simple reality: if you want reliable AI search visibility data, platform-native reporting is safer than trying to reconstruct it externally.
Start with your product feed and your structured attributes. Google’s new reporting is most actionable when you can connect conversational demand to missing or underdeveloped product fields.
- Audit which products already have complete attributes for color, material, style, size, and other category-specific specs. - Review whether your top categories are represented with the language customers actually use in AI shopping conversations. - Identify which products sit in discovery versus evaluation versus purchase, then align content and feed enrichment accordingly. - If you have Merchant Center access, check Analytics > Products > AI performance and use the report as a prioritization layer, not a final performance measure. - Treat the data as a guide for listing optimization, not as proof of downstream traffic or revenue.
This is not a revolution in measurement. It is a narrow but important opening from Google that makes AI shopping behavior more legible inside Merchant Center. The signal is incomplete, but it is still enough to change how marketers prioritize feed hygiene, product schema, and category messaging this quarter.
The teams that win here will be the ones that use Google’s new visibility data to make faster decisions about what shoppers are asking for, what attributes are missing, and which products need stronger conversational coverage.
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