AI Overviews dominate prompts as brands track more AI search surfaces
AI Overviews now surface in most commercial prompts, but brands are splitting measurement across Google, ChatGPT, Gemini, Perplexity, shopping, and agents.
Google AI Overviews has more than 2.5 billion monthly active users, Sundar Pichai said at Google I/O 2026, and AI Mode has passed 1 billion. Will Scott’s latest analysis of Peec AI’s 500,000-prompt dataset found Google AI Overviews appearing in 86% of prompts overall and 88.5% of decision-stage prompts, pushing the conversation from experimental feature to core search behavior.
Google AI Overviews set the baseline
Peec AI’s sample ran from April 13 to April 20, 2026 and leaned toward commercial, buying-intent prompts, with navigational searches such as standalone brand names excluded. The dataset was designed to reflect the part of search where buyers are comparing products, evaluating options, and moving toward a decision.
In the same analysis, AI Overviews appeared in 56.9% of Google searches in April 2025 and 86.7% in April 2026, a jump of more than 50% year over year in that sample. The numbers describe a commercial prompt mix, not Google overall, and rates move sharply with query type and geography.
Why one visibility score no longer works
The simplest mistake in AI search visibility is treating every surface as if it behaves like classic blue-link search. Google AI Overviews compress answers inside the search results page. AI Mode extends that behavior into a more conversational search flow. ChatGPT, Perplexity, Gemini, and Copilot sit outside Google’s results entirely and can surface brands through different retrieval paths, citation rules, and product discovery patterns.
Peec AI tracks brand performance across ChatGPT, Perplexity, and Gemini, and it has now expanded into AI shopping analytics and agent analytics. For marketers, visibility is no longer only about whether a page ranks, but whether a brand appears in the answer, is cited in the response, or is selected by a shopping or agent system during the path to purchase.
| Surface | What it changes | What to measure | Main constraint |
|---|---|---|---|
| Google AI Overviews | Inline answer replaces part of the results page | Mention rate, citation rate, prompt coverage | Availability and output vary by query type and market |
| AI Mode | Conversational search inside Google | Follow-up behavior, answer sourcing, conversion path | Different from classic ranking checks |
| ChatGPT, Perplexity, Gemini, Copilot | Discovery happens outside Google | Brand mentions, citations, source selection | Each model exposes different retrieval behavior |
| AI Shopping Analytics | Product-level discovery inside shopping experiences | SKU visibility, product references, merchant presence | Focused on shopping intent, not broad search |
| Agent analytics | Autonomous systems act on behalf of users | Task completion, product choice, action paths | Discovery is tied to execution, not just search |
Geography and intent can move the numbers dramatically
Peec AI’s commercial sample also shows why market-by-market tracking is essential. France was an outlier at 0% in the dataset because Google had not launched AI Overviews or AI Mode there, while the European Union overall showed a lower rate than markets outside the EU. It means a brand can look highly visible in one market and nearly absent in another, even before language, category, or intent are factored in.
The same logic applies to query stage. Commercial intent is especially likely to trigger generative answers in Peec AI’s analysis. If a brand only checks broad keyword visibility, it will miss the part of the funnel where buyers are asking comparison, recommendation, and shortlist questions.
The vendor layer is expanding around the problem
In November 2025, Peec AI raised a $21 million Series A led by Singular and had onboarded more than 1,300 brands and agencies since February 2025. Its pricing page now lists it as trusted by more than 2,500 marketing teams.
Peec AI’s June 2026 launch of AI Shopping Analytics pushes the category further toward product-level visibility. Instead of only asking whether a brand appears in a search answer, the tool is built to surface how products show up across AI shopping experiences in ChatGPT, Perplexity, and Gemini. It ties visibility to catalog data, product references, and shopping behavior, which are the signals buying teams now have to inspect.
A practical framework for tracking AI search visibility
A useful program starts by separating surfaces before it tries to aggregate them. The second step is to split prompts by intent, especially informational, comparison, and decision-stage queries, because Peec AI’s own data shows a clear jump once buying intent increases. The third step is to segment by market, since the EU and France can produce very different outcomes from the same brand and category. The final step is to track citations, mentions, and product references separately, because those signals do not behave the same way across Google AI Overviews, AI Mode, ChatGPT, Perplexity, and Gemini.
That framework also needs to include shopping and agent surfaces now that discovery is moving beyond search results. Peec AI’s move into AI shopping analytics and agent analytics reflects that the next visibility problem is not just whether a brand gets mentioned, but whether an autonomous system can find, compare, and act on it.
This article was produced by Prism’s automated news system from verified source data, official records, and press releases, then run through automated quality and moderation checks before publishing. The system is built and supervised by the people who set the standards it runs under. Read our full AI policy.
Know something we missed? Have a correction or additional information?
Submit a Tip
