One user query no longer triggers one search. Google AI Mode fires 9-11 parallel sub-queries, while ChatGPT runs 2.3-2.8. If your content only ranks for the surface query, you miss the other 8-10 citation opportunities per user prompt. Query fan-out is the hidden volume multiplier, and most brands are invisible across 90% of the actual search surface.
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Overview: Query Fan-Out in AI Search
Query fan-out is the core mechanism behind modern AI search. Instead of answering a single query, AI systems break one question into multiple sub-queries and search them simultaneously.
These sub-queries cover different angles of the user’s intent, even things not explicitly asked. The AI then combines results from all these searches into one final, summarized answer.
This fundamentally changes visibility. Content is no longer selected based on one keyword, but on how well it answers multiple related subtopics within a query.
Bottom line: To win in AI search, content must go beyond single keywords and comprehensively cover all key sub-intents behind a query.
What Fan-Out Actually Is (And Why It Breaks Old SEO Logic)
Query fan-out is when an AI search engine breaks down a single user prompt into multiple sub-queries and runs them in parallel. Your customer types one thing. The engine fires 9-11 separate searches behind the scenes. Each sub-query pulls different results. Each result gets a chance to be cited. Your page either ranks for all of them, some of them, or none of them.
Traditional SEO ranked a single page for a single keyword. You optimize for “best CRM.” You rank for “best CRM.” Done.
Fan-out demolishes that logic. One user asking “best CRM for my B2B SaaS team” doesn’t trigger one query anymore. It triggers ten. Maybe your content ranks for the parent query but misses the sub-queries on pricing, security compliance, integrations, and onboarding speed. Your competitor ranks for all ten. They get cited multiple times in the same response. You get cited zero.
How Google AI Mode Decomposes a Single Query (With a Concrete Example)
Google AI Mode doesn’t just search once. According to research from ekamoira, 59% of prompts trigger 5-11 simultaneous sub-queries, with an average of 9-11 for complex queries.
Let’s walk through a real example: a founder asks, “What’s the best CRM for a 20-person B2B SaaS startup that needs tight Slack integration and doesn’t break the budget?”
That single query likely decomposes into something like this:
1. “Best CRM B2B SaaS startups” 2. “CRM Slack integration comparison” 3. “Affordable CRM under $500/month” 4. “CRM for small teams” 5. “CRM setup and onboarding time” 6. “CRM mobile app quality” 7. “CRM customer support ratings” 8. “CRM security compliance” 9. “CRM contract terms for startups” 10. “CRM versus Hubspot vs Pipedrive 2026”
Each sub-query runs in parallel. Each one pulls results. The AI then stitches them together into a single, comprehensive answer. If your page ranks for sub-query #2 but not the others, you’re one citation in a ten-part answer. Your competitor who has content covering all ten angles gets cited across multiple sub-queries. That’s visibility multiplied by 10x.
Google AI Mode has 75M daily active users and processes over 1 billion monthly queries, according to Digital Applied. That’s a lot of fan-out happening every single day.
How ChatGPT Fan-Out Differs From Google (Shorter Chains, But Refined)
ChatGPT doesn’t fan-out quite as aggressively as Google. According to Peec.ai’s analysis of 20 million search queries, ChatGPT issues 2.3-2.8 sub-queries per prompt on average. When it does search, the word count per sub-query has doubled from 6 to 12 words since October 2025, meaning each search is more specific and refined.
ChatGPT search also activates less frequently than it did a year ago. Semrush data from February 2026 shows search activation on just 34.5% of queries, down from 46% in late 2024. The reason is simple: ChatGPT’s training data is already comprehensive for many questions. It searches when it needs fresh data, not by default.
But when it does search, those 2-3 sub-queries are surgical. They’re not generic. A customer asking about CRM setup workflows might trigger a search for “CRM implementation timeline” and “CRM data migration process” but skip the “what is a CRM” query entirely because ChatGPT knows that already.
The practical implication: your content needs to cover mid-funnel and execution-focused content, not just top-of-funnel definitions. ChatGPT’s fan-out targets the refinement layer, not the awareness layer.
Shorter chains but more refined. Each sub-query is narrower, citations are fewer but more decisive.
The 4-Step Audit
Pick your money query. Ask Google AI Mode. Map sub-queries. Count how many your content wins.
The Hidden Volume Multiplier Most Brands Miss
Here’s the uncomfortable truth: 68% of pages cited in AI Overviews are not in the Google organic top 10. According to Surfer SEO’s study of 173,000 URLs in December 2025, traditional ranking position is nearly irrelevant in AI search. What matters is whether your content answers the sub-query.
A page ranking at position 47 for “CRM security compliance” beats a top-10 page about “best CRM overall” if the user (via the AI engine) is decomposing the query around security concerns.
Think about the volume multiplier this creates. If 1,000 people ask your target query each month, and 80% of them trigger fan-out into 9 sub-queries, you’re looking at 7,200 sub-query opportunities. If your brand is only visible on the parent query, you’re capturing 1,000 impressions. If you’re visible on seven of those nine sub-queries, you’re now capturing 5,600 impressions. That’s a 560% increase in visibility, not because search volume went up, but because you started answering the sub-queries.
Most brands have no idea this hidden multiplier exists. They’re still optimizing for the headline keyword and ignoring the decomposition layers underneath.
What Content Earns Citations Across Multiple Sub-Queries
Not all content ranks equally across sub-queries. There’s a pattern to what gets cited.
Specificity wins. Content that answers a narrow question (not a broad one) gets pulled for sub-queries. “The Top CRM for Slack Integration” beats “Top 10 CRMs Overall” because the engine can use the specific piece for the integration sub-query without diluting it with unrelated options.
Data density matters. Sub-queries are fact-retrieval operations. They’re looking for comparison tables, pricing data, feature matrices, and direct answers. Content that buries the answer in narrative prose doesn’t perform well. Structured data, answer sections, and clear formatting increase citation velocity across sub-queries.
Execution-ready content ranks higher. If the sub-query is “how to migrate data to CRM,” generic CRM advice doesn’t cut it. Step-by-step guides, timelines, checklists, and vendor-specific instructions do. This is why case studies and how-to content perform so well in AI search.
Coverage of pain points drives citations. Sub-queries often attack anxieties. “CRM setup hidden costs,” “CRM learning curve,” “CRM customer support response time.” Content that names and solves these specific pain points gets picked for those sub-queries even if it’s not a general CRM buyer’s guide.
At upGrowth Digital, we’ve seen this pattern drive citation gains for clients like Lendingkart, who increased lead volume by 5.7x by mapping content to hidden fan-out sub-queries instead of just the parent keyword.
The 4-Step Audit to Find Your Fan-Out Gap
Step 1: Map your target query decomposition. Pick your core customer question. Ask yourself: what 7-10 sub-queries would an AI engine need to answer this comprehensively? Document each one. Tools like Peec.ai and ekamoira publish real fan-out data for high-volume queries, but you can also reason through this using customer support tickets and sales call transcripts. Where do prospects hesitate? What clarifications do they ask for? Those are your sub-queries.
Step 2: Audit your content portfolio against each sub-query. For each sub-query, ask: do we have content that directly answers this? Not tangentially. Directly. Most brands find they have strong content for maybe 3 of the 10 sub-queries and weak or missing content for the other 7.
Step 3: Identify the citation gaps. Use an AI citation tool (like our LLM Citation Share Gap Calculator) to see which queries you’re currently cited for and which ones your competitors own. This shows you the exact sub-queries where you’re losing ground.
Step 4: Build sub-query-specific content. Create focused pieces targeting each sub-query. Not one monster guide covering all 10 angles. Ten targeted pieces, each answering one decomposition layer. This approach increases your citation probability by 7-10x because each piece is purpose-built to satisfy one specific sub-query.
A: Not equally. Simple queries like “weather today” don’t fan-out. Complex, multi-faceted queries do. The ekamoira data suggests 59% of prompts trigger 5-11 simultaneous sub-queries, which means roughly 6 out of 10 customer questions are fanned out. That’s worth optimizing for.
Q: How do I know what my specific fan-out looks like?
A: You can’t always. But you can use customer data as a proxy. Look at your support tickets and sales call transcripts. When customers ask your core question, what follow-ups do they make? What clarifications do they request? Those follow-ups are usually the sub-queries the AI engine will decompose into. That’s your fan-out structure.
Q: Does ranking for the parent query guarantee visibility on sub-queries?
A: No. In fact, the ekamoira research shows that pages ranking in the top 10 for the parent query often rank poorly or not at all for the sub-queries. Your top-10 general guide doesn’t automatically get cited for the specifics. You need specific content for each sub-query.
Q: Can I use one piece of content to cover multiple sub-queries?
A: You can try, but it usually fails. Long-form comprehensive content sounds smart. In practice, AI engines prefer targeted answers. A 3,000-word mega-guide on CRM selection gets diluted when the engine is looking for a precise answer on CRM security. Split it into 10 focused pieces instead.
Q: Which AI engine has the most aggressive fan-out?
A: Google AI Mode. It runs 9-11 sub-queries per complex prompt versus ChatGPT’s 2.3-2.8. If you’re optimizing for volume, focus on Google’s fan-out decomposition first. ChatGPT’s fan-out is more surgical but affects a smaller search volume because ChatGPT search activation is down to 34.5% of queries.
Q: Does fan-out change my keyword strategy?
A: Completely. Traditional SEO targets one keyword per page. Fan-out strategy targets clusters of 7-10 related micro-keywords, each with its own focused piece of content. This shifts you from a monolithic content architecture to a modular one where each piece serves one decomposition layer.
The data is clear: fan-out is real, it’s massive, and most brands are optimized for 1 out of 10 citation opportunities. Your competitors are already building fan-out strategies. By the time you move, you’ll be playing catch-up on content you haven’t even created yet.
The first step is to measure your fan-out gap. Run your core customer query through Google AI Mode and ChatGPT search. Document the sub-queries each engine decomposes into. Then use our Run the LLM Citation Share Gap Calculator to see which sub-queries your brand is currently cited on and where your competitors own the space. That’s your gap. That’s your opportunity.
Once you see the gap, the fix is straightforward: build fan-out-aligned content. Not 10 rewrites of the same thing. 10 new pieces, each targeting one sub-query layer. If you’re serious about AI visibility in 2026, this isn’t optional. This is foundational. If you want guidance on building a fan-out content strategy mapped to your specific market, Book your GEO audit here. We’ll map your decomposition landscape and build the strategy to own it.
For Curious Minds
Query fan-out is the process where an AI search engine deconstructs a single user prompt into numerous, parallel sub-queries to gather comprehensive information. This breaks the old SEO model because your content is no longer competing for one ranking; it is competing for citation opportunities across 9-11 different, simultaneous searches conducted by the AI. If your page only addresses the surface-level query, you become invisible across 90% of the actual search surface being explored. Success now depends on anticipating and covering these decomposed informational needs within your content.
To achieve this, you must expand your content's scope:
Anticipate Sub-Queries: For a topic like 'best CRM', think about the inherent questions about pricing, integrations, security, and support.
Build Comprehensive Resources: Instead of a narrow article, create a resource that covers these related concepts, mirroring how an AI like Google AI Mode stitches together answers.
Focus on Topical Authority: Demonstrate deep expertise across the entire topic cluster, not just a single keyword, to be seen as a reliable source for multiple sub-queries.
This shift from a single keyword to a multi-threaded query environment is the single biggest change to search in a decade. Explore how to map your content to this new reality in the full analysis.
Query fan-out shifts the goal of content creation from 'ranking' for a keyword to 'being cited' within a synthesized AI answer. Ranking for the primary term is merely the entry point; true visibility comes from being cited for the 9-11 sub-queries that Google AI Mode runs in parallel. Your content must be a repository of answers, not just an answer to one question. This is because the AI is assembling a mosaic of information, and you want your brand to be the source for as many tiles as possible.
According to research from ekamoira, 59% of prompts trigger this multi-search behavior, meaning the majority of searches operate this way. To adapt, your content must be structured to address the likely decomposition of a user's prompt. A page about the 'best CRM' must also thoroughly cover related topics like 'CRM Slack integration,' 'affordable CRM pricing,' and 'CRM security compliance' to maximize citation potential. Without this depth, you concede visibility to competitors who do. Learn how to structure your pages for maximum citation share in our complete guide.
Your content strategy must be tailored to the distinct fan-out behaviors of each platform. For Google AI Mode, the goal is breadth and comprehensiveness to match its aggressive 9-11 sub-query model, while for ChatGPT, the focus should be on depth and execution-oriented details to satisfy its more refined searches.
Consider these distinct approaches:
For Google AI Mode:Create pillar pages or content hubs that anticipate and answer a wide range of related questions. A single resource should cover everything from top-level comparisons ('Hubspot vs Pipedrive') to granular details like contract terms and support ratings. This expansive approach maximizes your chances of being cited across multiple parallel queries.
For ChatGPT:Focus on mid-funnel, 'how-to' content. Since ChatGPT's search activation is lower (34.5% according to Semrush) and its sub-queries are longer and more specific, it is looking for procedural knowledge it doesn't already have. Content about 'CRM implementation timelines' or 'CRM data migration processes' will perform better than basic definitions.
Adapting your content's depth and focus based on the AI engine's behavior is critical for maximizing visibility. Discover more tactical differences in the full article.
A competitor can dominate the AI-generated answer by treating the user's prompt not as one question, but as ten. While one brand creates a page for 'best CRM for startups', a smarter competitor creates a comprehensive guide that explicitly addresses the sub-queries Google AI Mode will likely generate. This strategy is about mapping your content directly to the AI's internal search process. By doing so, you move from a single potential citation to being the source for the entire answer.
This is achieved by structuring content to cover:
Integrations: A detailed section on 'CRM Slack integration comparison'.
Budget: A transparent breakdown of 'affordable CRM under $500/month'.
Implementation: Clear data on 'CRM setup and onboarding time'.
Compliance: Specifics on 'CRM security compliance' like SOC 2 or GDPR.
When the AI runs its 9-11 parallel searches, this competitor's single, deep resource gets picked up repeatedly, while the narrowly focused page is cited once, if at all. This is how brands are multiplying their visibility, and you can find more examples in our full report.
This data provides clear evidence that ChatGPT is evolving from a generalist tool to a specialist that searches with surgical precision. The drop in search activation to 34.5% shows its internal knowledge base is sufficient for broad questions; it now only queries external sources when it needs fresh, specific, or procedural information. The AI is no longer asking 'what is', it is asking 'how to'.
The doubling of sub-query word count from 6 to 12 words, as noted by Peec.ai, confirms this shift. A 12-word query is not 'what is a CRM'; it is 'how to migrate data from Hubspot to a new CRM'. This means your content must move beyond top-of-funnel definitions and provide expert-level, mid-funnel guidance on implementation, workflows, and complex processes. Brands that continue producing high-level content will miss these highly specific citation opportunities. The full article explores how to pivot your content strategy to meet this demand for depth.
The sheer scale of over 1 billion monthly queries on Google AI Mode signifies that query fan-out is not a niche or future phenomenon; it is happening now and affecting a massive volume of user interactions. Each of these queries represents up to 11 opportunities for citation that most brands are currently missing. The urgency is that market leaders are already building content moats by capturing this 'hidden' search volume, creating a significant competitive gap.
This data from Digital Applied should be a wake-up call for marketers. The traditional SEO playbook, focused on ranking for a single term, is now obsolete at scale. Brands that fail to adapt are effectively becoming invisible to a growing user base that relies on AI-generated answers. The winning strategy is to develop comprehensive content that anticipates and addresses the full spectrum of sub-queries, from features and pricing to security and support. The full playbook for making this strategic shift is detailed further in the article.
To secure multiple citations, your content must be a comprehensive resource that mirrors how Google AI Mode deconstructs the query. Instead of one article, think of building a central hub that preemptively answers all the implicit questions a user has when searching for a B2B CRM. This approach shifts your focus from winning a keyword to owning the entire conversation.
Follow this stepwise plan:
Deconstruct the Prompt: Brainstorm 10-12 sub-topics a user would care about. This includes pricing tiers, specific integrations (like Slack), onboarding time, mobile app quality, and security compliance.
Structure Your Content: Create a long-form guide with dedicated, clearly labeled sections for each sub-topic. Use H2s and H3s like 'CRM Security and Compliance' or 'Comparing Hubspot vs Pipedrive'.
Gather Specific Data: For each section, provide concrete details, numbers, and direct comparisons. Avoid vague statements. Quantify your onboarding time or list your security certifications.
Answer Implicit Questions: Address topics the user did not explicitly ask but the AI will infer, such as contract terms for startups or customer support ratings.
This structured, data-rich approach makes your content an ideal source for the AI to pull from for multiple parts of its answer. Dive deeper into this implementation plan in the full article.
Reverse-engineering query fan-out requires a shift from keyword research to 'intent deconstruction'. The goal is to map out the entire universe of questions inherent in a single commercial prompt. You must think like the AI, breaking down a user's goal into its logical, informational components. This proactive approach ensures your content covers the full search surface, not just the entry point.
A practical workflow would be:
Identify the Core Intent: For 'best project management tool', the core intent is finding a solution to organize team tasks.
Brainstorm Functional Sub-Queries: What functional aspects matter? This leads to sub-queries on 'Gantt chart features', 'Kanban board customization', and 'time tracking integrations'.
Consider Constraint Sub-Queries: What are the user's limitations? This surfaces queries on 'affordable tools for small teams', 'open-source alternatives', or 'HIPAA compliant software'.
Analyze Competitor Content: Use tools to see what related topics top-ranking, comprehensive guides cover. This often reveals the sub-queries Google AI Mode already favors.
By building a content brief around this deconstructed map, you create a resource purpose-built for AI citation. Explore advanced techniques for this process in the complete guide.
This trend signals the end of keyword research as a standalone practice and the beginning of 'topic modeling' as a core competency. Traditional tools that provide volume for a single keyword are now showing only a fraction of the full picture. The future of content planning lies in identifying and mapping entire clusters of related sub-queries that an AI is likely to explore.
Content strategists must adapt in several key ways:
Expand Keyword Sets: Instead of one primary keyword, you need a basket of 10-15 long-tail keywords representing likely sub-queries.
Adopt a Hub-and-Spoke Model: Your core content 'hub' must address the main topic, while 'spokes' or internal sections must comprehensively cover each sub-query.
Prioritize Comprehensiveness: The metric for success is no longer just ranking for one term, but the breadth of related queries your content can satisfy. Research from ekamoira confirms this multi-query behavior is the norm.
The entire workflow of content planning must shift from a linear, keyword-first approach to a holistic, topic-first model. The full article outlines how to retool your team's processes for this new reality.
The declining search activation on ChatGPT signals a major devaluation of generic, top-of-funnel 'what is' content. The AI's vast training data already contains these definitions, so it has no need to search for them externally. This means the visibility and traffic for purely informational, awareness-stage content will continue to shrink as AI becomes the primary interface.
Brands must strategically shift their content focus downstream to where the AI still needs fresh information:
Mid-Funnel Execution: Create detailed guides on implementation, workflows, and best practices. Content like 'how to migrate your data to a new CRM' is something the AI will search for.
Unique Data and Research: Publish proprietary research, case studies, and data-driven insights. The analysis from Peec.ai is a perfect example of content an AI would need to cite.
Point-of-View and Strategy: Develop content that offers a strong, forward-looking opinion or strategic framework that is not yet part of the AI's training data.
Your content must provide value beyond what is already commoditized knowledge. Discover how to build a content moat with unique, defensible assets in the full analysis.
The most significant mistake is continuing to create narrow, isolated articles for individual keywords. This 'one page, one keyword' approach is a direct relic of traditional SEO and fails because it ignores that a single user prompt now triggers 9-11 parallel searches in Google AI Mode. Brands making this error are optimizing for 10% of the search and leaving the other 90% to their competitors.
The pivot requires a fundamental change in content architecture. Instead of producing five separate, thin articles on related topics, successful brands consolidate that expertise into a single, comprehensive resource. This central guide should be structured with clear headings that map directly to potential sub-queries, such as pricing, integrations, and competitor comparisons. This not only makes the content more valuable to a human reader but also makes it a perfect, multi-faceted source for an AI to cite repeatedly. The full article provides a framework for auditing and consolidating your content this way.
This siloed approach fails because AI engines like Google AI Mode are not looking for a single best page; they are assembling a composite answer from multiple data points. A collection of disconnected blog posts forces the AI to work harder, whereas a single, well-structured, comprehensive resource provides all the necessary information in one place. You are essentially making your content less 'citable' by fragmenting your expertise.
To align with AI's information processing, teams should adopt a topic cluster or pillar page model. This structure is inherently suited to query fan-out:
The Pillar Page: This central, long-form guide covers the main topic broadly and acts as the primary source for the parent query.
Cluster Content/Sections: Detailed sections within the pillar (or tightly linked supporting articles) address the specific sub-queries the AI will generate, from 'security compliance' to 'onboarding speed'.
This model, validated by research from firms like ekamoira on multi-query behavior, makes your domain the authoritative source on the entire topic, dramatically increasing your chances of being cited multiple times in a single AI response. Learn how to transition from silos to clusters in our detailed guide.
Amol has helped catalyse business growth with his strategic & data-driven methodologies. With a decade of experience in the field of marketing, he has donned multiple hats, from channel optimization, data analytics and creative brand positioning to growth engineering and sales.
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