# AI availability

**Definition v1.0, September 2026. Kesterley. This document is public.** Cite as: Kesterley (2026), AI availability, definition v1.0, kesterley.com/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.

## Two halves, reported separately, never averaged

- **Answer availability** (the mental half): whether the brand comes up when assistants answer the buying questions of its category, and how often. Measured as the brand's share of recorded answers across the category's buying situations, in two modes: what the model already knows (memory) and what it finds when it searches the web (search).
- **Agent availability** (the physical half): 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. Measured as a ten-check review of the public surface, summarised as four levels: 1 Falling behind, 2 The basics only, 3 AI-readable, 4 Agent-ready.

## 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 situation importance.
- **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 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 (mental market share = mental penetration x network size).
- **Agent-readiness level**: checks passed out of ten, summarised as level 1 to 4.

## Where it comes from

The Ehrenberg-Bass Institute's research established that brands grow through 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 the sampling frame: the buying situations of a category, phrased the way real buyers phrase them. AI assistants added a layer between the two: they are asked the buying question, they answer from memory or live search, and increasingly they buy on the buyer's behalf. AI availability is the availability framework applied to that layer. It is Kesterley's own extension of published research, not an endorsement by its authors.

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; the buying questions are put to ChatGPT, Gemini and Claude directly, around a thousand times per category, through official developer interfaces. Sampling frame: 8 to 16 buying situations per category, several phrasings each, memory mode and live search reported separately. Every share ships with its confidence interval; every report states its own reliability and minimum detectable change; differences within noise are called noise. Designs of published reports are pre-registered (frozen and hashed before measurement). The agent half is checked live from evidence: crawler access, entity records, structured data and its freshness, readability without JavaScript or login, a plain path to purchase. Full rules: kesterley.com/methodology (versioned).

## What it is not

Not rank tracking (rank order inside AI answers is close to random between runs; inclusion is the construct that persists). Not share of voice from a hand-picked prompt list. Not a sales promise (AI availability is a leading indicator; the causal evidence is early and stated as such). Not guaranteed (nobody can guarantee a place in a stochastic system).