60% of search journeys now involve AI. Here is what that means for brand visibility.

60% of search journeys now involve AI. Here is what that means for brand visibility.

Thirty percent of consumers now use AI for product research. A year ago, that figure was 12%. At the same time, roughly 40% of Google results include an AI Overview, according to a Brainlabs analysis of internal SEOClarity data, meaning people are reading AI-generated answers without actively choosing to. Add those two things together and the share of search journeys that involve AI in some form is already closer to 60%, according to Brainlabs research. The same research projects that figure will reach 80% within twelve months, based on current growth rates. As Brainlabs reports below, that changes how to think about almost everything in organic search.

How many people are actually using AI search?

Brainlabs' survey data splits consumers into three groups. Traditionalists, around 69% of consumers, use established search engines exclusively. Augmenters, around 30%, use both Google and AI platforms at different points in a single research journey. Dissenters, fewer than 1%, use only AI platforms. The augmenters are where the growth is. They have not abandoned Google. They have added AI as a research layer for complex queries, and that behavioral shift is what makes the aggregate 60% figure significant.

Why AI SEO is not just a rebrand of traditional SEO

The two disciplines are similar enough right now that teams do not need to tear up their roadmaps, but different enough that the gap is widening faster than most have adjusted for. There is not one AI search. There are at least four major platforms with different models, different guidelines, and a different relationship with Google's index.

The figure worth pausing on is Gemini's 15% overlap with Google's top 10 results, according to a Brainlabs analysis of internal SEOClarity data. Gemini is a Google product, yet it only cites pages from Google's top 10 results 15% of the time. An SEO strategy built entirely around top-10 rankings does very little for Gemini or ChatGPT visibility. And the overlap keeps shifting. AI Overviews used to have a 76% overlap with Google's top 10. In 2026, that figure has roughly halved.

How LLMs actually decide what to cite

Understanding why an obscure page ends up cited in a Gemini answer while a major brand's content is ignored requires understanding how LLMs generate responses, which works differently from how Google ranks pages.

When Gemini receives a prompt, it first determines whether it needs external data or can rely on training data alone. For most product research queries, it needs external data. From there, the LLM generates dozens of related micro-questions from the original prompt, sometimes tens of them running simultaneously for complex queries. For each micro-query, it searches Google's index across the top 100 results and beyond, not just the top 10. That is the first major divergence from traditional SEO.

The LLM then looks for direct answer blocks, headings matching the micro-question, hard statistical data, and freshness signals such as a recent last-updated date. Sources with low consensus compared to higher-authority references get filtered out. A page that answers a specific micro-question very directly can outcompete higher-authority pages that bury their answer in editorial prose.

In practical terms, three optimization approaches have produced the most consistent results across Brainlabs client work. First, studying query fan-out: The micro-questions LLMs generate are knowable, and FAQ sections built around them drive measurable citation increases. Second, running embedding similarity analyses to measure how closely content resembles what is already being cited, an approach that has produced an average 140% increase in AI citations across Brainlabs client tests. Third, treating content freshness as a technical requirement rather than a nice-to-have, with monthly refreshes of high-demand pages as a minimum for time-sensitive topics.

Measuring AI search performance is hard. Here is why.

No first-party data from any major AI platform is available at scale. Access to Google and Bing data on AI performance has begun to open up, but it remains limited. Google Search Console's Generative AI report provides impressions but not the data most useful for optimization decisions: queries and clicks. Impressions and page performance are useful for establishing baselines and tracking direction, but they leave significant questions unanswered: What are customers actually searching for, what are their follow-up questions, and what is happening in standalone apps like Gemini?

The current standard approach is to convert keyword data into likely prompts, track them across one of the thirty-plus third-party AI tracking tools available, and measure brand visibility over time as a proxy for real-world performance. The data is noisier than it looks. Across models, only 23% of citations remain active after 14 days. There is only around 45% agreement between platforms on which brand to recommend first. And fewer than 5% of query sets show perfect consensus across all major platforms.

Practical measurement approaches that work within these constraints include running 100 related prompts per category four times each, which produces equivalent reliability to running one prompt 400 times with richer category insight. Tracking AI referral traffic in GA4 and running correlation analyses against existing Google search demand data provides directional signal. Published AI demand studies, including high-quality data from SEOClarity, can be used alongside sensible adjustments to existing Google volume estimates. Any AI traffic estimate will be questioned by leadership, so documenting the methodology and making the reasoning defensible is as important as the number itself.

Agentic search: the next shift

Everything so far sits within the current paradigm, where AI is a layer on top of search. The next shift is larger. Google CEO Sundar Pichai has described plans to transform search into an "agent manager," where AI agents do the research and decision-making on a consumer's behalf. In this emerging agentic model, an AI agent researches in seconds, filters to a recommendation, and facilitates the transaction, all without the consumer leaving the AI platform. ChatGPT already has Instant Checkout. Google has its Universal Commerce Protocol.

The agents still need to do research, and they will still need information that has to come from somewhere. The question is what content and data sources they will trust enough to cite and act on. Three approaches are worth building toward now.

Restructuring content for agent consumption is the starting point. Agents look for explicit conclusions, unambiguous recommendations, precise structured data, and clean HTML. Burying the recommendation in the third paragraph does not work at machine speed.

Thinking about data as an asset agents will want to connect to is the second. B2B API access, consumer-facing authentication, and tiered licensing with AI platforms are all plausible commercial models that do not require waiting for the market to fully mature.

The third is considering building rather than just feeding. Brands with genuinely valuable expert content have the ingredients to become agentic platforms in their own right, with commerce rails so users can act on recommendations immediately.

Where to start

Start with measurement. Set up AI referral tracking in GA4, pick two or three priority categories, and begin tracking 50 to 100 related prompts at low frequency. The data will not be perfect, but it will be directional.

Next, audit priority content against the signals LLMs look for: freshness dates, direct-answer formatting, structured data, and heading-level relevance to likely micro-questions. A targeted refresh of high-demand pages applying these principles will indicate whether the changes move citation rates before scaling further.

Then build the roadmap for divergence. The overlap between traditional SEO and AI SEO is decreasing. In order to be best positioned in two years, teams should be building those capabilities now. The window to get ahead of it rather than chase it is shorter than it looks.

Methodology

The original survey data around consumer AI search behavior was taken from this Brainlabs study. The survey data showed that ~80% of all search journeys use established search engines (Google, Bing, Amazon) at some point when researching a product. If at least 40% of results now have an AI answer e.g. an AI overview or equivalent then Brainlabs maintains that the data saying 30% of consumers are using AI search is under-stating the scale of usage.

This story was produced by Brainlabs and reviewed and distributed by Stacker.