Query fan-out: why AI search never answers the question you typed

When you pose a query to an AI assistant that incorporates web search, it typically does not conduct that specific search. Instead, it breaks down the question into several smaller inquiries, conducts searches for each one, peruses the outcomes, and then generates an answer from the amalgamation of results. This process is known as query fan-out, and it influences how one should approach content creation for AI visibility. Here's how it works:

The AI model rewrites your prompt into a series of search queries. It then retrieves each query from a search index, fetching the top results for every query. The model extracts relevant passages from those pages and synthesizes them into a single response, potentially citing the pages it utilized. For a query like 'best CRM for a 5-person agency', the fan-out could encompass queries such as 'best CRM for small agencies 2026', 'CRM pricing per user small team', 'CRM with client portal for agencies', 'CRM project management integration', and 'simple CRM setup time'.

The exact queries may vary between different AI products and can change with each query execution, although the general pattern remains consistent. This mechanism is publicly described by Google's AI Mode and other sources.

This concept of query fan-out challenges traditional SEO practices, which emphasize optimizing individual pages for specific keywords. Fan-out complicates this approach in two significant ways: you can achieve visibility without ranking for the primary keyword, and the unit of content that gets cited is a passage, not a full page.

To optimize for AI search, one should focus on creating concise, easily digestible sections that directly answer specific sub-questions related to the main query. For instance, if the main query is 'best CRM for a 5-person agency', identifying sub-questions like 'what is a CRM', 'CRM pricing per user', 'CRM with client portal', and 'how long does CRM setup take' can help structure the content more effectively.

A practical strategy involves drafting a list of main questions the page should answer, generating likely sub-questions, and mapping each sub-question to a corresponding section on the page or another page on the site. This ensures that the content is structured in a way that is easily accessible and usable by AI models, thus improving the likelihood of being cited in AI-generated responses.