ResearchSemrushAI Visibility Index

Semrush Expands AI Visibility Index, Analyzing 126M AI Search Prompts

Semrush's 2026 AI Visibility Index scaled to 126M U.S. AI search prompts across 22 industries and four platforms, mapping how brands get cited in AI search.

6 min readPublished 2026-06-30

What happened

On June 26, 2026, Semrush — now an Adobe company — released its expanded 2026 AI Visibility Index, a flagship study analyzing more than 126 million real U.S. AI search prompts collected from January through April 2026. The release represents a dramatic scale-up: an earlier iteration of the index examined roughly 2,500 prompts, making the new dataset one of the most comprehensive views to date of how brands are mentioned, cited, and surfaced across AI-powered discovery.

The study spans 22 industries and four AI platforms — ChatGPT, Gemini, Google AI Mode, and Google AI Overviews — and is designed to show how AI-powered discovery is reshaping brand visibility. Its central message is that being found in AI search now requires competing on two distinct fronts: earning enough authority to be mentioned in an answer, and producing credible, structured content that AI platforms will cite as the supporting source. Those are not the same thing, and the data shows they often diverge.

Semrush also surfaced a measurement gap to justify the index. According to the study, nearly half (45%) of marketing leaders admit they cannot accurately measure their brand's visibility in AI-generated answers — a blind spot the index is positioned to close. The full report, available at the AI Visibility Index site, includes platform and industry benchmarks, brand case studies, and guidance for building an AI visibility strategy.

Why it matters for practitioners

For competitive-intelligence and brand teams, the index is less interesting as a marketing artifact than as a structured read on how the "answer economy" actually distributes visibility. The findings translate directly into how teams should think about brand positioning when the discovery surface is a generated answer rather than a ranked list of links.

1. Mentions and citations are separate games. The study draws a hard line between the two: mentions show how often a company appears in an answer, while citations show which domains and pages the platform used as evidence. On Gemini, the overlap between mentioned brands and cited domains can be as low as 30% — meaning a brand can be named in answers while a third party gets credited as the source. Teams that optimize only for being mentioned may be handing the authority signal to competitors and publishers.

2. Platform behavior varies widely. Citation patterns differ sharply by engine. ChatGPT cites an average of 15 sources per response and leans on community and reference platforms like Reddit and Wikipedia, while Gemini cites roughly 3 sources, drawing on a narrower pool that includes Wikipedia, Reddit, and YouTube. A visibility strategy tuned for one platform will not automatically transfer to another, which makes competitive benchmarking across engines essential rather than optional.

3. Visibility is concentrated. The data shows steep category concentration: in News and Media, the three most visible brands accounted for 82.9% of total category visibility, and in Consumer Electronics the top three held 76.9%. For challenger brands, that concentration is a warning — AI discovery appears to reward incumbents and high-authority sources, raising the bar for breaking into the answer set.

A further data point sharpens the operational case: organizations that fully integrate their SEO and AI visibility efforts report an 81% success rate in increasing traffic or leads from AI platforms, versus 36% for those managing the two in separate silos.

Key details

  • Release date: June 26, 2026
  • Dataset: 126 million-plus real U.S. AI search prompts
  • Time period: January through April 2026
  • Scope: 22 industries across four platforms — ChatGPT, Gemini, Google AI Mode, Google AI Overviews
  • Scale-up: Expanded from an earlier ~2,500-prompt analysis
  • Measurement gap: 45% of marketing leaders say they cannot accurately measure AI-answer visibility
  • Integration effect: 81% success rate when SEO and AI visibility are integrated vs. 36% when siloed
  • Citation volume: ChatGPT ~15 sources per response; Gemini ~3 sources per response
  • Mention/citation overlap: As low as 30% on Gemini
  • Concentration: Top three brands hold 82.9% of visibility in News and Media; 76.9% in Consumer Electronics
  • Parent company: Adobe

Market implications

The index reinforces Semrush's repositioning around AI search visibility, a theme it has pressed throughout 2026 across rebrands, MCP connectors, and acquisitions of complementary capabilities. Publishing one of the largest datasets on how brands surface in AI answers is a credibility play: it frames Semrush as the authority on a measurement problem that, by its own data, most marketing leaders admit they cannot yet solve. As an Adobe company, Semrush is also building the analytical foundation that will eventually feed Adobe Experience Cloud's enterprise marketing stack.

For CI teams, the strategic takeaway is that AI visibility is becoming its own competitive surface — one with different mechanics from traditional search rankings and different winners. Tracking how a brand and its rivals appear across AI engines is now a legitimate input to competitive analysis, and the platform divergence in the data means single-engine monitoring will miss most of the picture. Teams evaluating where to source that monitoring can weigh the Similarweb vs. Semrush comparison, since both vendors are racing to own the visibility-data category from different starting points.

The broader signal is that the answer economy is consolidating attention around fewer, higher-authority sources, and the gap between being mentioned and being cited is where competitive advantage is won or lost. Brands that treat AI visibility as an extension of SEO — integrated rather than siloed — appear materially more likely to convert that visibility into traffic and leads. For practitioners, the index is a useful map of a fast-moving surface, with the standard caveat that vendor-published research is best read alongside independent benchmarks before betting strategy on any single number.

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