Brand Visibility in AI Search: New Research, A New Era, and Why the Old Playbook Is Not Enough

Published: March 19, 2026 | 5 min read

Key Takeaway: Valorie Luther, M.S., Founder and Chief Strategist of Creative Concepts, and Ofri Touboul-Cohen, Co-Founder and CEO of Whitebox, have produced the first longitudinal, cross-platform study of competing brand AI visibility within a single consumer category. The research is currently under academic review and addresses a gap that has been building in the literature since AI-generated responses began reshaping how consumers find and evaluate brands.

If you have been in marketing long enough, you remember when SEO felt like cracking a code. Figure out what Google wanted, give it that, and your brand showed up. It was not effortless, but it was logical. You could see the levers, pull them, and measure what happened.

That clarity is harder to come by now. The introduction of ChatGPT in November 2022 and the rollout of Google AI Overviews in May 2024 did not just add new platforms to the mix. They changed the fundamental structure of how consumers find information. Instead of a ranked list of links, people increasingly receive a synthesized answer. And whether your brand appears in that answer, and how it is represented when it does, is governed by a different set of rules than the ones most marketing teams have spent years mastering.

Co-authors Valorie Luther, M.S. of Creative Concepts and Ofri Touboul-Cohen of Whitebox.io set out to study exactly that. Their research, now under academic review, is the first longitudinal study to track how competing brands actually appear in AI-generated responses within a single consumer category across multiple platforms over time. It matters because that specific question had not been formally studied before.

How Search Worked and Why It Made Sense

Search engine optimization developed its logic around how Google ranked pages: the quality and quantity of inbound links, the relevance of content to the query, and a growing list of technical signals. A brand that invested in understanding that system could predict, with reasonable confidence, where it would appear for a given search term.

The click-through rate data made the stakes concrete. Research from Backlinko found that the top organic result on a Google search captured roughly 28 to 36 percent of all clicks, with each position below capturing significantly less. Rank higher and you got traffic. Get traffic and you could convert it into customers. For 2 decades, that loop held.

SEO also had the advantage of being what researchers call platform-convergent. The signals that worked on Google translated reasonably well to other search engines. One coherent program could reach most of the search landscape. A single strategy, thoughtfully executed, was enough. That is no longer the case.

What Changed and How Quickly

The introduction of ChatGPT in November 2022 and Google AI Overviews in May 2024 replaced ranked lists with synthesized natural-language responses. Research from seoClarity and Seer Interactive found that organic click-through rates decline by as much as 61 percent for queries where AI Overviews are present. Bain and Company reported that roughly 80 percent of consumers now rely on zero-click results in at least 40 percent of their searches.

Pew Research put behavioral data behind those numbers. In a 2025 study tracking the browsing activity of 900 U.S. adults, users who encountered an AI-generated summary in Google search results clicked through to an external website only 8 percent of the time, compared to 15 percent when no summary appeared. 26 percent ended their session entirely after seeing the AI summary, without clicking anything. The search results page had become the destination.

For brands, the shift is not abstract. A consumer asking an AI platform for a product recommendation in any category is going to receive an answer, not a list of options to explore at their own pace. The question is whether your brand is in that answer, and what it says when it is.

Why AI Visibility Is Not Just SEO by Another Name

The instinct in most marketing organizations has been to treat AI visibility as an extension of SEO. If you rank well on Google, the assumption goes, you are probably showing up in AI-generated answers too. That assumption deserves more scrutiny than it has received.

A 2026 analysis by Punturo, Torres Llorca, and Fishkin examined 27 million citations across ChatGPT, Gemini, and Google AI Overviews and found substantial differences in how each platform constructs its answers. The sources AI platforms draw on are not simply the pages that rank highest in traditional search. The signals that govern whether a brand is included in an AI-generated response, and where it appears within that response, are not identical to the signals that determine search ranking.

The field of Generative Engine Optimization has emerged to address this. Foundational research established that optimization strategies developed for traditional search do not transfer cleanly to generative AI environments. Studies have shown that how brand content is framed influences whether large language models include a brand in their recommendations. Platform architecture, training data, and retrieval mechanisms differ enough between AI systems that the same brand can appear very differently depending on which platform a consumer happens to use.

What that body of work had not yet produced was a study of how actual competing brands show up in AI-generated results over time. The existing GEO research has largely relied on fictitious product catalogs and single-platform experiments. Valuable for establishing principles, but unable to answer the question a CMO is actually asking: where does my brand stand today, is it gaining or losing ground, and how does it compare to the competition?

The Research That Was Missing

That is the question Valorie Luther and Ofri Touboul-Cohen built their study around. Not a theoretical exercise about how AI systems work in principle, but an empirical one: within a real consumer category, across the platforms where real consumers are searching, how do competing brands actually appear, and does that change over time?

Their study examined a U.S. consumer category with established national brands competing across distinct market positions. It tracked AI visibility across multiple platforms and multiple measurement intervals. It is the first study of its kind in the published literature and is currently under academic review.

Luther brings expertise in AI visibility strategy, brand reputation, and academic research, including published work on neuromarketing ethics. Touboul-Cohen, as Co-Founder and CEO of Whitebox, brings the generative engine intelligence infrastructure that made systematic data collection at this scale possible. The collaboration is what allowed the study to meet academic standards while staying grounded in the practical measurement challenges brand teams actually face.

Frequently Asked Questions

What is AI visibility and why does it matter for brands?

AI visibility refers to whether and how a brand appears in the responses generated by AI platforms when consumers ask questions relevant to that brand's category. When a consumer asks ChatGPT or Google AI Overviews for a product recommendation and a brand does not appear in the answer, that brand is not ranked lower. It simply is not there. As consumer behavior shifts toward AI-generated answers, the practical consequences of that absence are growing.

Is AI visibility the same as SEO?

No, and the difference is more than technical. Traditional SEO is built around a ranked list of results where visibility is determined by position and click-through behavior is relatively predictable. AI-generated responses work differently. The platform constructs a natural language answer, selecting which brands to include, in what order, and with what framing. The signals that govern that process are not the same signals that govern search rankings, and a strategy built for one environment does not automatically serve the other.

What is Generative Engine Optimization?

Generative Engine Optimization, or GEO, is the emerging discipline focused on how brands can improve their visibility within AI-generated responses. Unlike traditional SEO, which targets ranked lists of links, GEO is concerned with whether a brand appears in the answer an AI platform constructs and how it is represented within that answer. The field is relatively young, and the peer-reviewed research needed to support real strategic decisions is still catching up to industry interest.

What happens next with this research?

The research is under academic review. Valorie Luther and Ofri Touboul-Cohen will share findings as they are cleared for public discussion. Updates will be published as they come.

The gap this research addresses is not just an academic one. Organizations making AI visibility decisions right now are largely doing so without a formal empirical foundation. They are borrowing frameworks from traditional search, making assumptions that have not been tested against real competitive data, and measuring performance with tools designed for a different information environment.

The brands that build real measurement capability now, before it becomes standard practice, will have an advantage that is difficult to close later. The question is not whether AI visibility matters. The behavioral data on click-through rates and zero-click search has already answered that. The more useful question is whether your organization understands where it stands in AI-generated results today, and whether that understanding is based on evidence or assumption.

Creative Concepts publishes ongoing analysis on AI visibility, brand strategy, and digital marketing through its Intelligence Briefings at creative-conceptsllc.com/intelligence-briefings.