AI availability / Definition v1.0 / September 2026
AI availability.
AI availability is how easily a brand gets chosen when an AI assistant answers a buyer’s question, and how easily an AI agent can read, verify and buy from that brand. It is the third availability, after the mental and physical availability that marketing science uses to explain how brands grow. Like market share or brand awareness, it is a measure, not a technique. This page is the reference definition we measure against.
On this page
The two halves
AI availability has two halves. They are measured differently, they move at different speeds, and a brand can be strong in one and absent in the other, so we never add them into one number.
The mental half
Answer availability
Whether the brand comes up when assistants answer the buying questions of its category, and how often.
- The question
- Does your company come up when an assistant answers the buying questions of your category, and how often?
- How it is tested
- A defined sample of recorded answers across 8 to 16 buying situations, several phrasings each, from three assistants, in two settings: from memory and with web search, reported separately.
- What the result does not mean
- Inclusion in answers is not sales, not customer preference and not a rank. It shows whether you are in the conversation, and where.
The physical half
Agent availability
Whether an AI agent can find the brand’s pages, read them without a browser or a login, trust the facts on them, and follow a path to purchase.
- The question
- Can an automated system find your pages, read them, trust the facts on them and follow a path to buy?
- How it is tested
- Ten checks of the public surface, fetched live: crawler access per bot class, entity records, structured data and its freshness, readability without JavaScript or login, feeds and merchant programs where relevant, a plain path to purchase.
- What the result does not mean
- A passed check shows a door is open, not that an agent will walk through it. Confirming that an agent can complete a purchase needs a separate test of that journey.
What it means for your business
It matters to marketing because buyers now ask assistants which companies to consider, and the answers differ more than most teams expect. In our CRM study, the same buying question produced different shortlists in different assistants: ChatGPT named Salesforce in 87% of answers, Gemini in 17%.
One company may appear often but be described inaccurately. Another may have clear product information but rarely appear in relevant answers. The right work depends on which problem the evidence shows. We keep the two halves separate so a single score does not hide the decision you need to make.
The metrics
- Share of answers
- For one buying situation: the share of recorded answers that include the brand, with a 95% confidence interval. Across situations: weighted by how important each situation is in the category.
- AI penetration
- The share of buying situations in which the brand appears at all, above the noise floor.
- Network size
- How many distinct buying situations the brand is attached to, weighted by their importance.
- AI mental market share
- AI penetration multiplied by network size, as a share of the category. The machine analog of mental market share in the Ehrenberg-Bass tradition, where mental market share equals mental penetration times network size. In our industry reports it is published as “share of brand mentions”.
- Agent-readiness level
- Checks passed out of ten, summarised as level 1 Falling behind, 2 The basics only, 3 AI-readable, 4 Agent-ready.
Where it comes from
The Ehrenberg-Bass Institute’s research established that brands grow through two things: mental availability, being thought of in the situations where buyers enter a category, and physical availability, being easy to find and buy. Its work on category entry points gives us the sampling frame: the buying situations a category is entered through, phrased the way real buyers phrase them.
AI assistants have added a layer between those two. They are asked the buying question, they answer from memory or from a live search, and increasingly they buy on the buyer’s behalf. AI availability is the availability framework applied to that layer: the same logic, a new respondent. It is our own extension of published research, not an endorsement by its authors.
The idea that AI has become a third place where a brand must be available is now discussed widely in marketing. Our contribution is a definition that can be measured and repeated: buying situations as the sampling frame, recorded answers as the evidence, and an interval around every number.
Primary sources for the framework: Sharp, B. (2010), How Brands Grow, Oxford University Press. Romaniuk, J. and Sharp, B. (2016), How Brands Grow Part 2. Romaniuk, J. (2023), Better Brand Health.
How it is measured
- The machine is the respondent. No surveys. We put the buying questions to ChatGPT, Gemini and Claude directly, around a thousand times per category, through their official developer interfaces.
- Buying situations, not random prompts. 8 to 16 situations per category, several phrasings each, from memory and with live search, reported separately.
- Statistics that admit noise. Every share of answers is computed with its confidence interval, and every report states its own reliability and minimum detectable change. When a difference is within noise, the report says so.
- Pre-registered designs. For published reports the design is frozen before the first question is asked, so nothing is selected after seeing the data.
- The agent half from evidence. Crawler access, entity records, structured data and its freshness, readability without JavaScript or login, a plain path to purchase. Checked live, not self-reported.
The complete measurement rules, versioned, are in the methodology.
To run a simple version for your own company, follow our guide: How to find out what ChatGPT says about your company.
What it is not
- Not rank tracking
- Rank order inside an AI answer is close to random between runs. Membership in the answer is the construct that persists, so we report inclusion, never position.
- Not share of voice from a prompt list
- A handful of prompts someone picked, run once, produces a precise-looking number with no meaning. Availability needs a sampling frame, repeats and intervals.
- Not a sales promise
- AI availability is a leading indicator. The evidence that it drives sales is early, and we say exactly how strong it is rather than promising an outcome.
- Not guaranteed
- Nobody can guarantee a place in a stochastic system. We measure, we say what to fix, and we measure again.
Questions
What is AI availability in marketing?
It is how likely AI assistants are to name your brand when buyers ask them for options, plus whether AI agents can read and buy from your website. Marketers can use it the way they use mental availability: as a measure of how likely the brand is to be considered, now for buyers who ask an assistant first.
How can I check it myself?
Ask the buying questions of your category in ChatGPT, Gemini and Claude, several times each, in fresh chats with memory off, and count how often you are named. Our free step-by-step guide shows how.
Is AI availability the same as AI visibility?
No. AI visibility tools count how often a brand appears in a set of prompts someone chose. AI availability is sampled from the buying situations of a category, measured with confidence intervals, reported with its stability, and includes the agent half: whether an AI agent can read and buy from the brand at all.
Does better availability guarantee more sales?
No. It identifies issues and opportunities, but sales depend on other factors too. An observed change in AI answers is not proof that it caused a commercial result.
How fast does it change?
The search half moves within weeks when the sources assistants read change. The memory half moves over quarters, because it depends on what the models learned in training. Agent availability changes as soon as the pages do.
Can it be bought?
Paid placements inside assistants are beginning to appear. They are a separate line. We measure organic availability, and where paid placement exists in a category we report it separately.
Who should care?
Any category that is bought after comparison: software, services, travel, finance, durable goods. If your buyers ask an assistant before they ask you, AI availability decides whether you are in the answer.
Cite as
The definition is versioned; changes are listed here with their date.