AI search is not just about ranking for a single keyword anymore. It is about being the page that an AI system keeps coming back to when it fans out from that keyword into 5, 15, or 50 related follow-up questions.

That hidden layer is what we now call AI overview fan-out queries. If you ignore them, you might rank for the main term, but you will rarely get cited in AI overviews. If you deliberately target them, your chances of appearing inside the AI answer box can jump dramatically.

A recent analysis of AI overviews found that pages ranking for multiple fan-out queries are 161% more likely to be cited than pages that rank only for the main keyword. In other words, comprehensive topical coverage across a set of related queries is now a ranking asset, not just a nice-to-have.

In this guide, we will dissect what fan-out queries are, how Google and other AI search systems use them, and how to build a practical process that lets you find, prioritize, and rank for them.

What are AI overview fan-out queries (and why are they so powerful)?

At a high level, fan-out queries are the secondary, tertiary, and supporting searches that surround a primary user query.

When a user types something like “best protein powder” and triggers an AI overview, the system is not just answering that one phrase. It is traversing a network of related intents and questions such as:

  • “best protein powder for women”
  • “whey vs plant protein”
  • “how much protein powder per day”
  • “is protein powder safe long term”
  • “protein powder without artificial sweeteners”

Those related questions are the fan-out. In an AI-first search experience, they are not just side traffic. They are the backbone of how the system understands context, risk, and completeness.

Several studies now confirm this behavior:

  • An in-depth analysis of AI search patterns by ALM Corp found that pages ranking across a broad “query fan-out” are consistently preferred by AI systems, especially for complex or high-stakes topics such as finance and healthcare. Their guide on the query fan-out impact argues that “ranking breadth across intent variants” is a stronger predictor of AI visibility than a single top-3 position on the head term.
    Source: ALM Corp: Query Fan-Out Impact

  • Chris Long has argued that fan-out queries must be a core part of any AI search strategy and highlighted how AI overviews pull from pages that cover more of the topic graph instead of those optimized for just one phrase.
    Source: LinkedIn: Fan-out queries must be part of SEO

  • A study covered by Search Engine Land reported that pages ranking for multiple fan-out queries around a topic were 161% more likely to be cited in AI overviews than pages that only ranked for the primary query.
    Source: Search Engine Land: AI overview fan-out ranking study

  • Geminis Digital Hub and Stan Ventures both summarized similar findings: the more nodes of the fan-out graph your content ranks for, the more frequently AI overviews call your page as a citation.
    Sources:

So what is actually happening inside the AI?

In simple terms, AI overview systems:

  1. Model a “topic graph” around a head query.
  2. Identify crucial sub-questions and clarifications (the fan-out).
  3. Evaluate which URLs provide robust coverage across that graph.
  4. Prefer those URLs as core citations because they are more likely to remain useful as the user refines or pivots their search.

You are not just competing on “best protein powder”. You are competing on “best protein powder + all the natural follow-ups”.

How do AI overview fan-out queries actually work in practice?

To work with fan-out queries, you need a mental model of how they are produced and how search engines use them.

Fan-out is not just “long tail”

A lot of SEOs try to interpret fan-out queries as a rebrand of long-tail keywords. That is incomplete.

Long-tail is about low-volume, specific variations discovered historically in logs (what people have already searched). Fan-out is about predictive, intent-driven expansions (what people are likely to ask next).

For a query like “AI content detection”, a traditional keyword model might surface:

  • “ai content detection free”
  • “ai content detection tool”
  • “ai content detector accuracy”

A fan-out model would also surface:

  • “how accurate is ai content detection for legal documents”
  • “can ai content detectors be fooled”
  • “how to reduce false positives in ai detection”

Those last three might have low or even zero historical search volume, but they are still part of the AI’s internal reasoning graph.

The AI overview fan-out lifecycle

You can think about the life of a single AI overview result in four stages:

  1. Trigger:
    A user enters a query that qualifies for an AI overview, such as “how to start a Wyoming LLC”.

  2. Expansion:
    The system creates a query fan-out that includes:
    • Clarifying questions: “Wyoming LLC vs Delaware”, “Wyoming LLC tax benefits”.
    • Modal queries: “steps to start Wyoming LLC”, “cost to form a Wyoming LLC”.
    • Risk checks: “is a Wyoming LLC anonymous”, “legal risks of Wyoming LLCs”.
  3. Source selection:
    The system seeks URLs that:
    • Address several distinct nodes in that fan-out graph.
    • Exhibit consistent entity accuracy (for states, fees, requirements).
    • Use clear headings and answer styles that can be cited across multiple question variants.
  4. Citation reuse:
    When the user clicks on follow-up questions or refines the search, the AI overview prefers to reuse previously vetted URLs that:
    • Already proved relevant on adjacent queries.
    • Have robust topical coverage and a clear structure.

The citation preference is where that 161% uplift emerges. A site that only answers “how to start a Wyoming LLC” in a narrow way might get one citation, if any. A site that also covers “Wyoming vs Delaware”, “cost breakdown”, and “ongoing compliance” becomes a stable citation across many nodes of the fan-out.

In classic SEO, you could get away with building one page per keyword and hoping to land in the top 3. The cost of switching URLs between queries was low for the search engine.

In AI search, each new citation is expensive. The system has to:

  • Evaluate factual accuracy.
  • Harmonize style with the generated answer.
  • Maintain consistency across follow-up questions.

Reusing a trusted source across the fan-out reduces that cost. It is computationally and qualitatively cheaper to cite you again than to test a new site for each follow-up question.

That is the opportunity: you win not just the initial query, but the cascade of follow-ups.

How do I identify fan-out queries for any topic?

Finding fan-out queries is half detective work, half systems thinking. The trick is not hunting for just more keywords, but building a map of likely follow-up questions.

Here is a 6-step process you can run for any topic.

Step 1 - Start with a main query that actually triggers an AI overview

Begin by testing your primary topic in search environments where AI overviews are consistently available.

For example:

  • “seo content strategy”
  • “ai image copyright rules”
  • “b2b saas onboarding checklist”

Note which ones reliably show an AI overview. You want to anchor your analysis there.

If you are in a region where AI overview is not rolled out, you can use a VPN or collaborate with a teammate who has access. Fan-out discovery for AI overview optimization depends on seeing how AI search behaves in real contexts.

Step 2 - Capture on-SERP fan-out signals

The search results already leak parts of the fan-out graph.

Collect the following:

  1. People Also Ask questions (PAA):
    Expand at least 10 to 20 PAA entries. Many of these are either exact or near-identical to the AI system’s internal follow-up queries.

  2. AI overview follow-up chips and buttons:
    When the AI overview appears, look at:
    • Clickable refinements (for example “for beginners”, “for small businesses”).
    • Inline follow-up questions that appear under the answer.
    • Tabs like “Pros and cons”, “Step-by-step”, “Compare”.
  3. Related searches at the bottom:
    These often surface lateral expansions and comparisons that the AI will include in its reasoning (for example “seo content vs content marketing”).

Document everything in a spreadsheet. Label:

  • Column A: Main query
  • Column B: PAA question
  • Column C: AI follow-up chip / question
  • Column D: Related search
  • Column E: Intent type (explanatory, how-to, comparison, risk, tool, pricing)

You are building a primitive version of the AI’s topic graph.

Step 3 - Analyze SERP overlap to spot “hub” URLs

Next, identify which pages are already winning multiple nodes of the fan-out.

Search for:

  • The main query.
  • The top 10 to 20 PAA questions you collected.
  • The most important related searches and follow-up prompts.

Track:

  • Which URLs recur across many of these searches.
  • How often your own site appears compared to competitors.

In many niches you will see a small set of “hub” pages that rank across 5 or more of these variations. Those URLs are your current AI overview favorites.

You can confirm their importance by cross-referencing with coverage from the AI overview and industry analyses like the fan-out impact research from ALM Corp. Their work highlights how such hub pages often become persistent AI citations because they cover more of the fan-out map than specialized, single-keyword posts.
Source: ALM Corp: Query Fan-Out Impact

Step 4 - Use tools to expand the fan-out cluster

Now that you have the initial fan-out scaffold, deepen it with tools and data.

Useful sources include:

  • Keyword clustering tools:
    Group related keywords into clusters by similarity. This often reveals missing angles (tools, frameworks, edge cases) that your initial manual pass did not catch.

  • On-site search logs:
    Look at what your users search for internally after they land from a main topic. Those second searches are literal fan-out behavior on your own property.

  • Support tickets, sales calls, and chat logs:
    These reveal how real people refine their questions in conversation, which often mirrors what they will ask an AI.

  • Competitor content tables of contents:
    Long, structured guides from competing sites often mirror the structure AI systems want to see. Their H2s and H3s frequently match latent fan-out questions.

Add all of this to your spreadsheet and assign every query to one of these buckets:

  • Definition / overview
  • Step-by-step / how-to
  • Comparison
  • Tool / solution
  • Cost / ROI
  • Risk / mistake / compliance
  • Use case / example

You are turning a messy list into a structured fan-out model.

Step 5 - Prioritize fan-out queries by “AI leverage potential”

Not all fan-out nodes are equal. Some are far more likely to influence AI overview citations.

Instead of only looking at search volume, prioritize by what we can call AI leverage potential:

  1. Cross-intent relevance:
    Does this question show up:
    • As a PAA for multiple different head terms?
    • Across several related keyword families?

  2. SERP hub reuse:
    Do the same 2 or 3 URLs keep ranking for this question alongside others in the cluster? That suggests AI systems see it as a central node in the topic graph.

  3. AI overview proximity:
    Does this question appear:
    • Inside the AI overview follow-up questions frequently?
    • As a clickable refinement under the AI answer?
  4. Depth-critical angle:
    Is the question necessary for safe or correct advice? For example:
    • “side effects of intermittent fasting”
    • “legal risks of scraping competitor data”

AI systems tend to emphasize these critical angles because they reduce user risk and hallucination exposure.

Assign an AI leverage score (for example 1 to 5). Tackle 4s and 5s first.

Step 6 - Decide which fan-out to cover on one page vs multiple pages

This is where strategy meets information architecture.

You have three basic patterns:

  1. Single master guide that covers a large fan-out cluster:
    Best for:
    • Topics where users want an end-to-end, comprehensive resource.
    • Queries where the SERP is dominated by long-form guides.
    • Situations where AI overviews favor single, robust citations.
  2. A master guide plus dedicated child articles for deep fan-out nodes:
    Best for:
    • Complex topics with several deep rabbit holes.
    • Clusters where the SERP shows both long guides and specialized posts.
    • Scenarios where you want multiple internal URLs eligible for citations.
  3. Multiple, narrow pages interlinked but no single “anchor”:
    Usually the weakest approach in AI-first search because it fragments your authority. AI systems prefer content that is self-contained enough to cite in several contexts.

As the ALM Corp research and summaries from sources like Stan Ventures suggest, the winning pattern in AI overview optimization is usually a strong anchor page plus a few supporting deep dives that interlink tightly. This combination provides both breadth (for fan-out coverage) and depth (for specific edge cases).
Sources:

What does it mean to “rank for fan-out queries” in tactical terms?

Once you know what the fan-out looks like, the next step is execution: structuring content and optimization so that AI search systems repeatedly select you as a citation.

Here is a practical framework.

1. Design content around question modules, not just topics

AI overviews are question-driven. Your pages should be too.

For each high-priority fan-out node, create a distinct module on the page:

  • Use the fan-out question (or a close variant) as an H2 or H3.
  • Answer it directly in the first 1 to 2 sentences under the heading.
  • Expand into supporting detail, tables, or examples after the direct answer.

A simple pattern that works:

H2: Is intermittent fasting safe for women over 40?
Intermittent fasting can be safe for many women over 40, but it carries higher risks if you have a history of disordered eating, thyroid issues, or unmanaged stress. Most research recommends moderated fasting windows and medical supervision, not aggressive protocols.

That opening line is exactly the sort of quotable, self-contained statement AI systems like to reuse.

2. Standardize answer shapes AI systems can reliably cite

If you want to be the citation that appears across multiple fan-out queries, your content needs predictable shapes:

  • Definitions: 1 to 2 clear sentences that define the term in user-friendly language.
  • Steps / processes: Numbered lists with 5 to 9 steps, each with a verb-led label and 1 to 2 explanatory sentences.
  • Comparisons: Tables that line up features, pros, cons, and use cases.
  • ** Pros / cons:** Bulleted lists separated under clear subheadings (“Benefits”, “Drawbacks”).

For many fan-out queries, AI systems are essentially looking for:

  • A definition to paraphrase.
  • A process to outline.
  • A list of factors to weigh.

If your page already contains those in a clean, structured form, you are far more likely to be fetched as a citation.

3. Beat the hub pages at their own game

Remember the hub URLs you identified that show up across many fan-out nodes?

Audit them with a ruthless eye:

  • Which fan-out questions do they answer thoroughly?
  • Where do they provide only shallow coverage or skip modules entirely?
  • Are there emerging angles (for example new regulations, new tools) they have not updated?

You can often create a “hub-plus” page that:

  • Retains all the essential modules.
  • Adds 3 to 5 high-leverage fan-out questions they have missed.
  • Updates data and examples to be more current and regionalized.

In AI search, freshness and completeness are strong tie-breakers between two similar sources. As analyses covered by Search Engine Land and Gemini Digital Hub argue, pages that stay ahead on topical completeness and recency often gain more AI visibility over time.
Sources:

4. Make entity coverage explicit and machine-friendly

AI overviews are built on top of entity graphs: people, locations, organizations, products, regulations.

For each topic cluster and its fan-out:

  • Name relevant entities clearly (for example “California Consumer Privacy Act (CCPA)”).
  • Provide simple, one-line explainers for each entity.
  • Use consistent naming and avoid ambiguous shorthand the first time an entity is introduced.
  • Where appropriate, include small definition boxes or callouts.

This makes your content a better substrate for AI. It can pull clean, self-contained explanations that remain consistent as users traverse the fan-out.

Internal links are your way of signaling your own topic graph.

For example, if you have:

  • A master guide on “AI image copyright rules”.
  • A deep dive on “fair use and AI-generated images”.

Then inside your master guide’s “Is AI art copyrightable?” section, include a clear internal link:

For a deeper breakdown of how fair use interacts with AI-generated art, see our guide to fair use and AI-generated images.

These links:

  • Help users navigate exactly like they would with fan-out queries.
  • Help search engines understand how your pages cluster around a topic.

Over time, the page that acts as the most connected and comprehensive hub tends to become the primary AI overview citation.

What does the 161% citation boost really mean for strategy?

The 161% number sounds impressive, but it is important to translate it into a strategic principle you can actually act on.

The core finding

Across the studies and summaries we have, including coverage from Search Engine Land, Gemini Digital Hub, and Stan Ventures, the key pattern is:

Pages that rank for more than one high-intent fan-out query within a topic are 161% more likely to be cited in AI overviews than pages that rank only for the primary query in that topic.

In simpler terms: multi-node ranking across a fan-out cluster dramatically increases your odds of becoming an AI overview source.

What this implies in practice

  1. Winning the head term is not enough:
    You might rank in position 2 for “seo content strategy” and still lose AI overview citations to a page that ranks position 4 on the head term but dominates 10 fan-out queries around “seo content templates”, “content briefs”, “content-led link building”, and similar nodes.

  2. Topical coverage beats isolated excellence:
    AI systems are effectively scoring you on “topic graph coverage”. If your content strategy is filled with one-off posts that never add up to a coherent fan-out cluster, your AI visibility will be erratic and fragile.

  3. Content consolidation often outperforms content sprawl:
    A 4,000 word guide that deeply covers 12 high-leverage fan-out questions can be significantly more valuable for AI search than 12 separate 400 word posts floating in isolation.

  4. Incremental fan-out wins compound:
    Each new fan-out query you capture:

    • Opens another gateway into your hub.
    • Gives AI systems another reason to reuse you as a citation.
    • Slightly increases your probability of being pulled into future variations and related topics.

Fan-out optimization is not about “tricks” to force AI overviews to cite you. It is about aligning your content strategy with how AI search actually thinks.

Real examples of fan-out query optimization in action

To make this concrete, let us walk through two hypothetical but realistic scenarios.

Example 1 - B2B SaaS onboarding

Primary query: “b2b saas onboarding checklist”

Initial AI overview fan-out might include:

  • “b2b saas onboarding best practices”
  • “customer onboarding vs implementation”
  • “how long should saas onboarding take”
  • “saas onboarding metrics to track”
  • “in-app vs human-led onboarding”

A typical SEO approach might target:

  • One post: “B2B SaaS onboarding checklist”
  • Another post: “B2B SaaS onboarding best practices”

A fan-out oriented approach would:

  • Create a single, deeply structured guide:
    • H2: What is B2B SaaS onboarding?
    • H2: B2B SaaS onboarding vs implementation
    • H2: Ideal onboarding length by product complexity
    • H2: In-app vs human-led onboarding (and when to use each)
    • H2: 11 onboarding metrics every B2B SaaS team should track
    • H2: Complete B2B SaaS onboarding checklist

Each of those H2s is a target for fan-out queries. If that one page ranks in the top 5 for 6 or 7 of them, AI overviews now see it as a go-to source.

In practice, you might still create separate deep dives on “onboarding metrics” or “in-app onboarding design patterns”, but they would be clearly subordinate to the master guide and heavily interlinked.

Example 2 - Local legal services (geo angle)

Primary query: “divorce lawyer in Austin Texas”

AI overview fan-out could involve:

  • “cost of divorce lawyer in Austin”
  • “uncontested divorce in Texas requirements”
  • “how long does divorce take in Texas”
  • “Texas community property explained”
  • “do I need a lawyer for uncontested divorce in Texas”

A traditional local SEO playbook might create separate thin pages:

  • “Cost of divorce lawyer Austin”
  • “Uncontested divorce Austin”
  • “Austin divorce timeline”

A fan-out strategy would think in terms of a local topic graph:

  • A comprehensive Austin-specific divorce guide:
    • H2: Do you need a divorce lawyer in Austin for an uncontested divorce?
    • H2: Typical cost of a divorce lawyer in Austin (with ranges)
    • H2: How long divorce takes in Texas (and how Austin courts affect timing)
    • H2: Texas community property rules in plain English
    • H2: Alternatives to hiring a full-scope divorce lawyer in Austin

This guide could rank not just for “divorce lawyer Austin”, but also:

  • “cost of divorce in Austin Texas”
  • “do I need a lawyer for uncontested divorce in Texas”
  • “how long does divorce take in Texas Travis county”

The more of those fan-out queries you can own, the more likely you are to appear as the legal reference source in AI overviews for the main query and its variations.

How does this intersect with user behavior like “turning AI overviews on or off”?

You have probably seen increasing interest in questions like:

  • “How do I turn on AI overview for every search?”
  • “How to filter out AI from search results?”

These behavioral trends matter, but not in the obvious way.

1. You cannot rely on users opting out of AI overview

Some users will try to disable or avoid AI overviews, but:

  • AI search surfaces are being integrated across multiple products, from traditional SERPs to assistants.
  • Many users will experience AI answers by default on mobile, voice, and chat interfaces.

If your strategy is “I will just focus on blue links because users hate AI”, you are betting against platform direction. Fan-out optimization is about ensuring your content appears whether the user sees classic results or an AI-generated answer.

2. Users who refine heavily are exactly the ones fan-out favors

The users who are most likely to try to turn AI off are also often the most active refiners:

  • They click more PAA questions.
  • They adjust their query wording frequently.
  • They use multiple tabs and comparisons.

These are precisely the users who benefit from well-handled query fan-out: they ask more, therefore the AI leans more heavily on robust, multi-node sources.

If your content is structured to be the default citation across the fan-out graph, you will accompany these users through their refinements whether or not they love the AI layer.

Putting it all together: a practical system for fan-out led AI overview optimization

To embed fan-out thinking into your ongoing strategy, use this repeatable workflow every time you commit to a new topic cluster.

  1. Scope the head term:
    • Confirm it triggers AI overviews.
    • Note AI follow-up chips and buttons.
  2. Map the initial fan-out:
    • Harvest PAA, related searches, and AI follow-up prompts.
    • Label each with intent type.
  3. Identify hub URLs and gaps:
    • See which competitors win across multiple fan-out queries.
    • Audit their coverage and identify high-value gaps.
  4. Expand the cluster with tools and internal data:
    • Use keyword clustering and your own logs to surface missing fan-out nodes.
    • Prioritize questions that are cross-intent, frequently reused, or critical to safety and correctness.
  5. Design a hub-first content architecture:
    • Create one or more master guides that cover the lion’s share of high-leverage fan-out queries.
    • Add deep dives only where necessary for depth and ranking.
  6. Structure content for AI citation:
    • Clear question-based headings.
    • Direct, quotable answers.
    • Tables, steps, and definitions in consistent formats.
    • Entity-rich, machine-friendly writing.
  7. Monitor and iterate based on AI behavior:
    • Track where your pages appear inside AI overviews.
    • Note new follow-up questions or refinements the AI surfaces over time.
    • Fold those back into your content as new modules or updates.

Fan-out queries are not a trick. They are a lens through which to see what AI search already values: depth, coverage, and clarity across the full landscape of questions that cluster around a topic.

If you align your content with that landscape, the 161% citation uplift is not a surprise. It is almost inevitable.

Frequently Asked Questions

What are AI overview fan-out queries?

Fan-out queries are the secondary and tertiary searches an AI overview triggers when users click follow-up questions or refine their intent. They are the deeper, more specific queries AI systems anticipate and model around a core topic.

How do fan-out queries increase AI overview citations?

Studies show that pages ranking for several fan-out queries around a topic are 161% more likely to be cited in AI overviews. AI systems prefer sources that cover the full topic graph rather than a single head term.

How can I find fan-out queries for my niche?

You can mine fan-out queries by combining AI overview prompts, People Also Ask, related searches, topic clustering tools, and your own on-site search data. The goal is to map all natural follow-up questions a user would ask after the main query.

What is the best way to rank for fan-out queries?

Structure content around topical clusters, build modular sections that directly answer fan-out questions, and optimize for clarity and entity coverage so AI systems can reuse your page across multiple related queries.

Is traditional keyword optimization still useful for AI overviews?

Yes, but it is incomplete. You still need to target primary keywords, yet AI search requires planning for the entire query fan-out to become a preferred citation source.