What Determines Your Brand’s AI Search Visibility

A shopper opens ChatGPT and asks a simple question: “What’s a good waterproof jacket for hiking in Scotland?”

Within seconds, three or four brands appear in the answer, each with a short explanation of why it might suit. Some of those brands have never run a paid ad in that category. Others invest heavily in advertising and still don’t appear.

This kind of interaction is becoming more common. Shoppers are increasingly starting their research inside AI tools rather than traditional search engines, and in some cases, that research may lead to a shortlist forming before the customer has visited a single website. According to 2026 research from L.E.K. Consulting, 46% of AI users now start their purchase research directly on a stand-alone AI platform such as ChatGPT, Gemini or Perplexity, up from 25% in 2024, while the share starting on traditional search fell from 43% to 24% over the same period.

That shift raises a genuine question for brands: what actually determines your AI search visibility, and how much of it is still in your control?

Why some brands get recommended and others don’t

Traditional SEO was built around ranking pages, so that a search engine could index and display them in a list for the shopper to work through themselves. AI-led discovery works differently. Rather than returning a list of links and leaving the comparison to the customer, AI tools synthesise an answer directly, drawing on product content, reviews and other brand information available across the web to decide which brands to name and which to leave out.

This emerging discipline has a name: GEO (generative engine optimisation), the practice of improving a brand’s AI search visibility. Where SEO is about being findable, GEO is about being understandable and trustworthy enough for an AI system to recommend a brand with confidence on a shopper’s behalf. It’s part of a wider shift towards agentic readiness, one that touches far more than just search.

The two disciplines aren’t in competition. Much of the groundwork that makes a brand good at SEO – clear product information, a well-organised catalogue, and genuine reviews – also tends to help an AI system understand and describe that brand accurately. GEO builds on those fundamentals rather than replacing them, but it probably asks more of them, since an AI system has to understand a product well enough to explain it to someone, not just index it against a search term.

Factors that influence a brand’s AI search visibility

Structured, specific product data

Clear, well-organised product information gives an AI system something concrete to work from when it’s trying to understand what a product is, who it’s for and why it might suit a particular query. A product titled “Item 4471” with a two-line description offers very little to reason from, compared with detailed attributes, materials, fit information and use cases. It’s worth being precise about what this actually earns a brand, though.

A 2026 Ahrefs study of 1,885 pages found that technical schema markup on its own did not reliably increase how often a page was cited in AI-generated answers. What appeared to move citation rates more was content that was clearly structured, easy to extract from the visible page, and corroborated elsewhere, suggesting that genuine clarity and consistency may matter more than the markup wrapped around it.

Structured data is still worth doing. It helps AI systems and search engines understand a catalogue accurately; it just doesn’t earn a brand a place in the answer on its own.

Consistency across sources

AI systems don’t just draw on a brand’s own website. They also take in information from review platforms, social channels, marketplaces or anywhere else a brand is mentioned, so alignment between all of these is likely to help, since it gives an AI tool a clearer, less contradictory picture to work from. Where a return policy reads differently in two places, or a product description doesn’t match what reviewers describe, the system has less to go on, and may end up drawing its own conclusions rather than reflecting the brand as intended.

Visible, credible reviews

Reputation signals carry weight in AI-led recommendations, much as they do with human shoppers browsing reviews before a purchase. Reviews that are genuine, current and easy to find, both on a brand’s own site and on trusted third-party platforms, are a reasonable bet for building the kind of credibility an AI system might look for before naming a brand over a competitor.

Clear, well-structured content

AI tools work best with content that’s easy to parse: clear headings, direct answers to real customer questions, and language closer to how people actually ask about products, rather than dense marketing copy written for a different era of search. Buying guides, detailed FAQs and comparison content are generally thought to perform well here, provided they genuinely answer the question rather than simply working a keyword in.

None of this replaces the fundamentals of running a strong ecommerce business. It simply means those fundamentals now need to be legible to a new kind of audience, one that reads at scale, compares constantly, and may recommend a brand before a shopper has even reached its site.

What this looks like in practice

Two brands selling a similar product could plausibly end up with very different outcomes in an AI-generated answer, even where the products themselves are genuinely comparable. A brand with structured, specific product data, consistent information across its channels, and a healthy base of recent, genuine reviews gives an AI system more to work with when describing the product and deciding whether to recommend it.

A brand relying on thin product descriptions, inconsistent policies across its own site and third-party marketplaces, or a handful of old reviews gives that same system less to go on, and may be more likely to be left out of the answer altogether, regardless of how relevant the product actually is to the shopper.

A rough sense of your AI search visibility

Without running a proper diagnostic, there’s no way to know precisely what your brand’s AI search visibility currently looks like, or how that compares to similar merchants.

That said, a few questions can offer a useful starting steer.

• If you searched for your own product category on ChatGPT or a similar tool, would your brand be named in the answer, and if so, how is it being described?
• Do your product descriptions actually answer the questions a customer would ask before buying, or do they mostly describe features without context?
• Do your reviews say broadly the same thing about your brand wherever they appear, from your own site through to third-party platforms and social channels?
• If a competitor’s product page and yours were placed side by side, would an AI system have enough clear, structured information to confidently tell the two apart?

If those questions are hard to answer with confidence, that’s not unusual. This is genuinely new territory for most brands, and it’s rarely been measured properly. Semrush’s 2026 AI Visibility Index, built from 126 million AI search prompts analysed between January and April 2026, found that 45% of marketing leaders cannot accurately measure their brand’s AI search visibility, and only 9% have tools to track it fully across platforms.

This is quickly becoming a core part of the customer journey, and the brands paying attention to it early are the ones best placed to benefit.

Work with Swanky on your brand’s AI search visibility

Swanky helps ecommerce brands understand and improve how they show up in AI-led discovery, from the technical foundations that make a catalogue easy for AI systems to understand, through to the content and reputation signals that build genuine credibility. This sits within our wider agentic commerce readiness work, helping brands prepare for a landscape where AI plays a growing role in discovery and purchasing.

If you want a clearer picture of your brand’s AI search visibility, get in touch with our team.