Full Definition
Inclusion Rate is the percentage of prompts in a defined prompt set where a company is mentioned by name in the AI-generated response. It is the baseline AEO metric: before measuring how prominently a company appears, how often it is cited as a source, or how favorably it is positioned, you need to know whether it is part of the conversation at all. Inclusion Rate answers that question.
To measure Inclusion Rate, define a set of commercially relevant prompts, typically 30 to 50 questions a buyer might ask an AI tool during research or evaluation, run them against one or more AI platforms, and record whether the company name appears anywhere in the response. The percentage of prompts that include a mention is the Inclusion Rate. Importantly, this counts any mention, whether the company is recommended, mentioned as an alternative, or even referenced critically. Inclusion Rate is a presence metric, not a sentiment metric.
Inclusion Rate should be tracked separately for each AI platform, since ChatGPT, Gemini, and Perplexity surface different companies at different rates for the same prompt. It should also be tracked over time to measure the impact of AEO initiatives, with enough prompt-set consistency between measurement cycles to make results comparable.
A low Inclusion Rate indicates a fundamental visibility gap: the AI model does not associate the company with the topics its buyers are asking about. Addressing this typically requires work on messaging clarity, content breadth, and third-party authority signals. Technical optimization alone rarely moves Inclusion Rate significantly, because the gap is almost always a content and authority problem, not a structural one. In a recent AEO Wrangler engagement with a B2B healthcare technology company, technical readiness scored 8.2 out of 10 while open-discovery Inclusion Rate on category prompts remained well below 20%, confirming that structural technical readiness and AI visibility are distinct problems requiring distinct solutions. (AEO Wrangler client engagement, 2026)
Inclusion Rate is most useful when read alongside Citation Rate and Share of Answer. Inclusion Rate tells you whether you are in the conversation. Citation Rate tells you whether you are trusted within it. Share of Answer tells you how your presence compares to competitors across the same prompts. All three together give a complete picture of AI visibility.
What is a good Inclusion Rate for a B2B company?
There is no universal benchmark that applies across categories and platforms, but a useful working frame is this: an Inclusion Rate below 20% on open discovery prompts typically indicates the company is not yet meaningfully visible in AI-generated answers for its category. An Inclusion Rate above 60% suggests the company is well-established in the conversation for those prompts. The more important comparison is internal: measuring Inclusion Rate across different prompt categories, across different platforms, and over time reveals where the gaps are and whether the work is moving the needle. A company in a narrow B2B niche may achieve 80% Inclusion Rate with relatively modest AEO work simply because AI platforms surface fewer vendors. A company in a crowded category may work significantly harder to reach 40%.
Does a high Inclusion Rate mean a company is being recommended positively?
Not necessarily, and this is worth understanding before reporting Inclusion Rate to leadership. Inclusion Rate counts any mention: a recommendation, an alternative to consider, a comparison point, or even a critical reference. A company that appears in AI responses primarily as a cautionary example or as a vendor with known limitations will score well on Inclusion Rate while having a very different actual visibility profile than one appearing as a consistent top recommendation. For a fuller picture, qualitative review of how the company is characterized in responses is important alongside the quantitative metric. Inclusion Rate tells you the company is in the conversation. Reviewing actual response text tells you what role it is playing there.
Why does Inclusion Rate vary so much between AI platforms for the same company?
Three factors drive most of the variation. First, training data: ChatGPT's base knowledge reflects its training cutoff and weights sources differently than Perplexity's live retrieval or Gemini's hybrid approach, so a company well-covered in recent trade press may score better on Perplexity than ChatGPT until the next training cycle. Second, retrieval architecture: platforms that retrieve live web content surface companies that are well-indexed and recently active differently than platforms drawing primarily on static training data. Third, category modeling: each platform has developed its own internal representation of which vendors belong in which categories, shaped by its training data and user feedback, and these representations don't always align. Tracking Inclusion Rate separately per platform is essential for this reason: a low score on ChatGPT and a high score on Perplexity tell you something specific about where your content and authority work needs to focus.