What is AI visibility (share of model)?

What "AI visibility" means

AI visibility is the degree to which an AI assistant — ChatGPT, Perplexity, Gemini, Claude, or Copilot — accurately describes, compares, and recommends a company when a user asks a relevant question. As of 2026, it is a distinct discipline from search-engine optimization: a company can rank on page one of Google and still be invisible to, or misrepresented by, the language models that an increasing share of buyers ask first.

"Share of model" is the AI-era analogue of share of voice. It measures how often, and how prominently, a brand appears across a large, repeated sample of AI answers for the queries that matter to its category. A generator manufacturer with a 40% share of model for "best home standby generator" is named in roughly four of every ten relevant AI answers; a competitor at 5% is effectively absent from the conversation buyers are actually having.

This guide defines the concept for marketing and growth teams and explains the mechanics that determine it. For the practical playbook, see how to get your company recommended by ChatGPT and Perplexity; to audit your current standing, see is your company represented in AI search?.

Why AI visibility is not the same as search ranking

Search ranking orders a list of links for a human to click; an AI answer synthesizes a single response and names only a handful of entities. The retrieval and citation pipeline behind that answer rewards different signals than a ranking algorithm does. Google itself documents that structured data helps machines understand a page's meaning rather than just its keywords, which is why a clean, machine-readable profile outperforms keyword-dense copy when a model is deciding whom to name.

Three differences matter most for a growth team:

• The result set is tiny. A model typically names two to five entities, not ten blue links. Being "good enough for page two" is being nowhere.
• Disambiguation is decisive. A model must be certain which "Acme Restoration" you are before it will confidently recommend you. Ambiguous or thinly-described entities get dropped in favor of ones the model can identify with high confidence.
• The source is synthesized, not linked. Models often fold facts into an answer without a visible citation, so the quality and structure of the underlying data — not just a backlink profile — drives whether your facts are the ones that surface.

What determines a company's share of model

Share of model is the output of a pipeline: a model has to reach your data, parse it cleanly, identify your entity unambiguously, retrieve your content over a competitor's, and then choose to surface you. Each stage is improvable.

1. Reachable, structured data. Models read structured profiles — Organization, Product, and Service data expressed as JSON-LD — far more reliably than prose buried in a brochure site. The schema.org Organization vocabulary is the shared language models and search engines use here.
2. Entity disambiguation. A sameAs graph that links your entity to its own domain, Wikidata, Crunchbase, and LinkedIn lets a model resolve "which company" with confidence. Wikidata in particular is a backbone source for the knowledge graphs behind many assistants.
3. Topical density. Models weight sources by how thoroughly they cover a topic. A company named across a directory category, comparison surfaces, and related guides accrues more citation weight than one mentioned once.
4. Freshness. Bing-powered surfaces (Copilot, and ChatGPT search) reward fast re-indexing via IndexNow, so recently-updated, dated content is favored.

A neutral, well-structured directory is a high-leverage place to influence all four at once, because it is exactly the kind of source models retrieve from when comparing companies in a category.

How share of model is measured

Measuring share of model means sampling, not guessing. The method is to define the buyer queries for a category, ask each across multiple assistants on a repeated schedule, and record which entities are named, in what position, and with what described attributes. Because model outputs vary run to run, a single answer proves nothing; a distribution across dozens of runs is the signal.

The output is three numbers a marketing team can act on: presence (are you named at all), prominence (named first, or last), and accuracy (are the described facts correct). A company can have high presence but poor accuracy — frequently mentioned, but with an outdated product line or the wrong service area — which is its own, very fixable, problem.

What a company actually controls

A company cannot buy a citation, and on a credible source it should not be able to. What a company controls is the quality, structure, and freshness of the information a model reads about it. The disaster, resilience, and emergency-response directory on this site is built so that companies can author that information directly: see the Local and Brand plans for how that works, and list your company to claim or add your entity.

Concretely, a company improves its share of model by correcting the facts (always free to do), publishing a complete structured profile, building out its product and service entities, and keeping everything current. Brands researching the category-level picture can compare how players like Generac, EcoFlow, and Viasat are represented today, and where the gaps are. Where amplification is offered it is always labeled and capped — it changes prominence within clear limits, never the underlying facts or the organic ranking.

Where AI visibility fits a 2026 marketing plan

AI visibility belongs next to SEO, not instead of it, because the two share an engine room — structured data, entity clarity, and topical authority feed both. For the contrast in detail, read AI visibility vs SEO. The teams seeing the earliest returns are in categories where buyers research with assistants before they ever open a search engine: backup power, satellite internet, public adjusting, and disaster recovery services tied to events like hurricanes. In those categories, the company that is accurately and prominently represented in AI answers is, increasingly, the company that gets the call.