The Fan-Out Revolution: Mastering AI Reasoning Journeys in B2B Search

Introduction

The mechanics of digital discovery have moved beyond the simple matching of keywords to a more complex process of Retrieval Augmented Generation (RAG). In 2026, AI models do not just retrieve a single page; they execute a series of follow up searches, known as "fan out" queries, to synthesise a comprehensive response. At 3P Digital, we have observed that brands winning these citations are those that provide high quality Main Content (MC) tailored to specific user intents. This article explores how to align your technical SEO and content strategy with the reasoning journeys that define modern search.

Key Takeaways

• Fan-Out Queries are the New Standard: AI models generate 15 to 25 sub queries to satisfy complex user needs.

• Specificity Drives "Needs Met" Ratings: Content that identifies a specific audience (ICP) performs better than generic information.

• Deal-Breaker Content Wins: Addressing technical limitations and support expectations prevents competitors from capturing your citations.

• LinkedIn as a Trusted Source: Professional social content is increasingly used as a reputation signal for AI models.

• Data Integrity Matters: Relying on extrapolated "LLM volume" is less effective than using actual intent clusters from first party data.

Summary Table: The Transition to Reasoning Journeys

Article

Understanding the Fan-Out Phenomenon

A reasoning journey begins when a user asks a broad question, such as "best project management software." Behind the scenes, the AI model generates a variety of specific sub searches regarding integrations, user limits, and comparative pricing. These "fan out" queries are designed to ensure the final response "Fully Meets" the user's needs.

During my time on the Google CEO Advisory Board in 2019 and 2020, the focus began shifting toward how systems can interpret "Know Simple" facts versus complex topics. For B2B brands, this means your content must be structured to answer these hidden queries. If an AI asks "Can this software handle 500 users?" and your site does not have that specific data in its Main Content, you will be excluded from the final recommendation.

The ICP Niching Strategy

The Google Search Quality Evaluator Guidelines state that a page must have a clear and beneficial purpose. For B2B companies, that purpose is often tied to a specific type of user. Generic content is frequently rated lower because it fails to satisfy the specific requirements of a niche audience.

You can improve your search performance by explicitly stating who your content serves. This should be reflected in your descriptive MC titles and your first paragraph. Leading brands now include a dedicated "Who We Serve" page. This creates a clear signal for the AI regarding the "Authoritativeness" of your brand for a specific cohort, which is a critical component of the E-E-A-T framework.

Answering the Deal Breakers

The B2B buying process is driven by risk mitigation. Buyers ask specific questions about support times and existing tech stack integrations. Steve from Notebook Agency refers to these as "deal breaker questions." In the context of Google's guidelines, these are the "Satisfaction" signals that determine if the MC is sufficient for the page's purpose.

To find your deal breakers, you can use the Deep Research features of modern AI tools. By entering your primary topic, you can observe the qualifying questions the model asks. These questions represent the information gaps the AI is trying to fill. Answering these points on your website ensures that your content is accurate, comprehensive, and helpful for the intended user.

The Citation Source Hierarchy

The sources cited by AI models have changed. While listicles and comparison pages remain strong, LinkedIn has emerged as a significantly underrated authority signal. Because it is a platform indexed by major models like ChatGPT (via Microsoft), expert posts are frequently cited as evidence of first hand Experience.

Reputation research now includes what independent sources and professional communities say about a content creator. A high effort post on a professional network that generates meaningful discussion can serve as a stronger authority signal than a traditional backlink on an obscure site. AI systems look for "Internet wide agreement" to verify if a content creator is a trusted expert in their field.

FAQs

What is a fan-out query in AI search? A fan out query is a sub search generated by an AI model to gather specific details—like pricing or technical specs—needed to provide a complete answer to a user's initial broad question.

How does ICP niching improve my SEO? Defining your Ideal Customer Profile (ICP) helps search engines and AI models match your content to the specific users who will find it most helpful. This increases your "Needs Met" rating and signals high Authoritativeness for that niche.

Why should I focus on "deal-breaker" questions? Deal breaker questions address the specific concerns that prevent a sale, such as integration issues. Answering these ensures your content is satisfying for users and more likely to be cited by AI models.

Is LinkedIn really a valid source for AI citations? Yes. LinkedIn posts from identified experts are considered high quality Main Content and are used by AI models to verify first hand experience and professional reputation.

How can I identify the reasoning journeys of my customers?

Use AI research tools to see what qualifying questions the model asks before it completes a search. These questions mirror the internal "reasoning" the AI uses to filter for the best results.

References

* Purpose of a Webpage. * Identifying the Main Content (MC). * Who is Responsible for the Website. * Primary Content Creators. * Reputation of Content Creators. * Experience and Expertise. * Authoritativeness. * Know Queries. * Queries with Multiple User Intents. * Rating Using the Needs Met Scale. * Fully Meets (FullyM) Standards.