What is AI visibility?

AI visibility is how likely a business is to be discovered, understood correctly and named when someone asks an AI assistant for a recommendation. It differs from search ranking because an AI answer usually names two or three businesses rather than listing ten links, so a business is either included or effectively absent.

SEO optimises for position in a list of links, measured in rankings, clicks and organic traffic. AI visibility concerns whether a business is named in a generated recommendation, measured in presence, position, citations, factual accuracy and share of voice. Strong SEO remains a foundation because AI systems rely on crawlable web information, but ranking well does not guarantee being recommended.

No AI vendor publishes a complete account of how recommendations are formed, and anyone claiming precise knowledge of the mechanism is overstating it. What is observable is that models draw on information they can retrieve about a business — its own site, business profiles, directories, reviews, press and third-party references — and that businesses described consistently and corroborated across independent sources appear more often than businesses that are not.

Yes, by measurement rather than by access to the platforms' internals. Running a fixed set of buyer-intent questions across AI platforms on a schedule, recording every answer, and scoring presence, position, citation and accuracy produces a repeatable measurement. Because the prompts stay frozen between cycles, movement in the result is attributable.

Usually because there is more corroborating information available about them. Competitors that appear repeatedly tend to be present across the sources a model can draw on — directories, association listings, review platforms, local press, industry publications — while the business that does not appear is often documented only on its own website. Identifying the specific sources behind that gap is the purpose of a source gap analysis.

Structured data helps by removing ambiguity about facts a machine would otherwise have to infer: what the business is called, where it operates, what services it provides. It is not a ranking mechanism and adding markup that does not match the visible page can do harm. Treat it as making existing, true information machine-readable, not as a lever.

Review platforms are frequently among the sources cited in AI answers about local businesses, so reputation can reach a recommendation indirectly through those sources. That is an observation about which sources get cited, not a claim that a specific rating produces a specific outcome. Greenbug measures which review sources actually appear in answers in a given category rather than assuming.

No. Nobody controls the output of an AI model, and any agency guaranteeing a recommendation or a ranking from ChatGPT, Gemini, Claude, Perplexity or Google AI is selling something it cannot deliver. What can be done is measuring current recommendation behaviour, improving the information and authority signals models draw on, and measuring again to see whether it changed.