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Google named it publicly at I/O 2025. The patent had existed quietly for years before that. But query fan-out is now one of the most consequential mechanisms in AI search, and most SEOs are still optimising as though it doesn’t exist.
Here’s what’s happening. When someone types a prompt into Google’s AI Mode, the system doesn’t run one retrieval. It expands that single query into ten, sometimes twenty parallel sub-queries, retrieves content across all of them simultaneously, and then assembles a synthesised response. The user asked one question. The AI went looking for a dozen answers.
Take “how to start a podcast.” A traditional search engine matches those words to ranked pages. An AI system running fan-out splits that prompt into sub-queries covering recording equipment, hosting platforms, episode structure, RSS mechanics, audience growth strategy, and monetisation options, all at once, pulling from whichever content best satisfies each thread. Your single well-optimised page on podcasting might answer one of those threads cleanly. The other eleven threads? That’s where your competitors live in the response.
This is the core problem for SEO practitioners right now. Ranking well for a primary keyword no longer guarantees you appear in the full AI-generated answer. The AI isn’t reading your page and deciding whether it ranks. It’s running a retrieval operation across multiple intent dimensions, and your content either shows up across enough of them to earn meaningful inclusion, or it doesn’t. Check out our guide on what query fan-out is.
The good news is that this is an analysable, fixable problem. You can audit where your content is getting picked up, identify which sub-query clusters you’re missing, and build a remediation plan that improves your coverage systematically. That’s what this guide covers.
What Query Fan-Out Actually Changes About Visibility
Before the analysis makes sense, the mental model needs updating. Traditional SEO optimisation centres on a single URL earning a high position for a target keyword. You write a page, you build authority to it, you track its position. The logic is clean.
AI Mode doesn’t work like that. The retrieval layer isn’t looking for your best page on a topic. It’s assembling a multi-part answer, and it sources different components from different places. Your page might contribute the section on pricing. A competitor’s page might contribute the section on implementation. A third-party review site might contribute the section on user sentiment. The final response looks unified to the reader, but it was built by pulling fragments from dozens of sources across as many sub-queries.
What this means practically is that your content footprint matters far more than your content depth on a single URL. A site with ten solid pages covering ten distinct angles of a topic will outperform a site with one exhaustive pillar page, because the AI’s fan-out process finds coverage, not comprehensiveness. It’s not rewarding the longest answer. It’s rewarding the site that keeps showing up across the retrieval threads.
This also changes how you think about competitor analysis. The question isn’t just “who ranks above me on this keyword?” It’s “which pages are being pulled into AI responses for the sub-queries surrounding my topic, and are any of mine among them?” Those are different questions with different answers, and the second one is the one that matters now.
How to Audit Your Query Fan-Out Coverage
The audit starts with understanding which sub-queries exist around your target topics, then checking whether your content appears in retrieval for any of them. You can do this without proprietary tools, though tools accelerate it considerably.
Start by picking a primary topic you want to rank for in AI-generated responses. Don’t start with a keyword, start with the user’s underlying goal. “Project management software” is a keyword. “Choosing the right project management tool for a growing team” is the user’s goal, and it’s the goal that the AI fan-out process is built around. Once you have the goal framed correctly, you can begin mapping the sub-query territory.
Open AI Overviews, ChatGPT, or Perplexity and prompt them with your topic. Read the full response carefully, not for whether you appear, but for how the response is structured. Count the distinct angles it covers. A response to a B2B software query might cover feature comparisons, pricing tiers, integration capabilities, onboarding complexity, customer support quality, and G2 or Capterra ratings. Each of those sections came from a different retrieval thread. Each represents a sub-query the AI ran in parallel. Write them down. That list is your fan-out map for that topic.
Now you have something concrete to work with. You can test each sub-query thread individually, check what sources the AI pulls, and identify where your content appears versus where it goes silent.
Mapping Sub-Query Clusters to Your Content
Take the sub-query list you built and run each one through the AI tool again as a standalone prompt. “What integrations does [category] software typically offer?” Pull the response. Note which sources it cites, which brand names it mentions, and whether any of your pages appear. Do the same for pricing, onboarding, support quality, and any other thread your fan-out map identified.
What you’re building is a coverage matrix: your topic across the top, each sub-query cluster down the side, and a simple yes or no for whether your content appears in the retrieval. Most sites, when they run this exercise honestly, find they’re showing up in two or three threads and invisible in five or six. That gap is the opportunity.
The clusters where you’re invisible usually fall into one of two categories: either you have no content covering that angle, or you have content that exists but isn’t structured in a way the AI can cleanly pull from. Both are fixable, and the fix for each is different, which is why the audit step matters before you start creating or rewriting anything.
Finding the Gaps Where Competitors Show Up Instead of You
Once you’ve built your coverage matrix, run the same exercise on two or three competitors. Specifically the ones appearing in AI responses for the sub-query threads where you’re absent. Look at the pages the AI is pulling from. What are those pages doing that yours aren’t?
Pay attention to three things. First, whether the competitor’s page addresses the sub-query as its primary focus or just touches on it incidentally. AI retrieval tends to favour pages where the sub-query angle is the main point of the content, not a paragraph buried in a broader piece. Second, look at the structure: are they using clear headers that mirror the sub-query language? AI systems parse heading structure heavily when deciding which fragment of a page answers which retrieval thread. Third, check whether the page contains a direct, extractable answer near the top. The AI isn’t reading to the end. It’s pulling from wherever the clearest answer sits.
How to Optimise Content for Fan-Out Retrieval
The optimisation work breaks into two tracks: creating content that covers the sub-query clusters you’re currently missing, and restructuring existing content so that AI systems can retrieve specific sections from it cleanly. You’ll almost certainly need both.
On the creation side, the fan-out map you built in the audit tells you exactly what to write. Each sub-query cluster where you have no coverage is a content brief. A page written specifically to answer “what does onboarding look like for [category] software?” will get picked up in the onboarding retrieval thread in ways that your general product page never will. The specificity is the point. Fan-out retrieval rewards pages that are authoritative on a narrow angle, not pages that are comprehensive on a broad topic.
This is a shift in how most SEO content gets planned. The instinct is to write a giant pillar page and cover everything. For AI fan-out, you’re better served by a cluster of focused pages, each of which owns one sub-query thread cleanly. The pillar page still has value for traditional search and for internal linking equity, but it won’t carry your AI visibility on its own.
Writing for Sub-Query Depth, Not Just Primary Keywords
Each piece of content you create or revise for fan-out coverage needs to pass a simple test: if an AI ran the sub-query your page is targeting, would your page surface as the clearest available answer? That means writing with the sub-query’s specific intent at the centre, not as a secondary consideration.
Practically, this means opening each page with a direct answer to the sub-query, not a preamble. If you’re writing about onboarding complexity for project management software, the first two sentences should deliver a clear, extractable statement about what onboarding typically involves. Then you build depth from there. AI retrieval systems pull from the top of pages more reliably than from the middle or bottom, so burying your most relevant content three paragraphs in loses you retrieval even if the content is excellent.
The language you use also matters. AI fan-out systems reformulate queries, which means the sub-queries they run often use different phrasing than your target keyword. Writing naturally and covering the semantic territory of a topic, rather than optimising for an exact phrase, gives your content a better chance of matching across the range of phrasing the AI might use in retrieval.
Structuring Pages So AI Can Pull from Them Cleanly
Structure is where many pages fail in AI retrieval, even when the content itself is strong. The problem is that AI systems need to extract a specific fragment from your page to answer a specific sub-query, and if your page isn’t structured to make that easy, the system pulls from a cleaner source instead.
Use headers that describe the specific topic of each section in plain language. “Pricing” as a header is acceptable. “How much does [category] software typically cost?” is better, because it mirrors the sub-query language more closely. Within each section, keep your most important statement in the first sentence. Don’t save your conclusion for the end of a section; AI retrieval doesn’t always read that far. Schema markup helps, particularly FAQ schema, because it creates an explicit map of questions and answers that AI systems can parse without needing to interpret your page structure.
Internal linking deserves attention here too. Linking between your sub-query-focused pages with descriptive anchor text helps AI systems understand the relationship between your content clusters, reinforcing the signal that your site has depth across this topic’s territory, not just one strong page.
Monitoring Whether Your Optimisations Are Working
The hardest part of optimising for AI fan-out right now is that the measurement infrastructure is still catching up. Google doesn’t give you a fan-out coverage report. ChatGPT doesn’t have a Search Console equivalent. You’re working with proxies, and you need to be systematic about which proxies you track.
The most direct method is manual prompt testing. After making optimisation changes, run the sub-query threads from your fan-out map through the AI tools you’re targeting, and note whether your pages appear in citations or responses where they didn’t before. Do this at a fixed cadence, every two to three weeks, and log the results in a simple spreadsheet. Changes to AI retrieval can take several weeks to register, so testing too frequently creates noise. Testing too infrequently means you miss the signal when changes land.
For tools-based monitoring, Perplexity is currently the most citation-transparent of the major AI search tools, meaning you can see exactly which URLs it pulled from for a given response. Make it part of your monitoring routine. Bing’s AI responses through Copilot also surface source citations that are worth tracking. Neither gives you aggregate data across all queries the way GSC does for traditional search, but they give you ground truth for the specific sub-query threads you’re testing.
Google Search Console still matters here, even though it doesn’t report AI Mode directly. Watch for impression growth on pages you’ve restructured for fan-out coverage. If a page was earning impressions for the primary keyword and starts earning impressions for a broader set of related queries, that’s a signal its content is being retrieved across a wider range of search intent, which correlates with improved fan-out coverage even if you can’t see the AI Mode data directly.
Set a quarterly review cadence where you rerun the full fan-out map exercise for your core topics. New competitors enter the space, the AI tools update their retrieval behaviour, and the sub-query landscape shifts. The sites that maintain AI visibility are the ones that treat fan-out coverage as an ongoing programme, not a one-time fix.
Building a Content Programme That Fan-Out Actually Rewards
The SEOs who’ll come out ahead in AI search aren’t the ones who found a clever workaround. They’re the ones who built genuine depth across the sub-query territory of their topics, and did it consistently enough that AI retrieval systems keep finding their content across multiple threads.
That means the fan-out analysis you run isn’t a project with an end date. It’s a recurring input into your content planning. Your content calendar should be partially driven by the sub-query clusters where you have no coverage yet, because those gaps represent retrieval opportunities your current programme is leaving on the table. Every time you identify a cluster where a competitor’s page is being pulled into AI responses and yours isn’t, you have a specific, testable brief to work from.
The structural work matters just as much as the creation work. A site full of content that AI systems can’t extract cleanly from is a site that’s invisible in AI Mode regardless of how strong the writing is. Audit your heading structures, your opening sentences, your schema implementation. These aren’t nice-to-have refinements. They’re the difference between a page that gets retrieved and one that gets skipped.
What’s worth being clear-eyed about is that this takes time. Retrieval patterns in AI search shift over weeks, not days, and a content programme built around fan-out coverage needs six months of consistent execution before you can judge it fairly. The sites that abandoned traditional SEO entirely for AI search, or treated fan-out optimisation as a quick win, are already discovering that the fundamentals haven’t changed as much as the hype suggested. You still need strong content, clear structure, and a coherent site architecture. What’s changed is the scope: you need those things across a much wider range of sub-topics than a traditional keyword-focused programme would have required.
The query fan-out mechanism isn’t a problem to solve once. It’s the new shape of how AI systems read the web, and building a content programme that works with that shape rather than against it is the clearest path to sustained AI search visibility your site can take.
If you want assistance with your organic B2B strategy, we are here for you! You can read more about our AI SEO services here, or contact us directly to learn how we can best support you in reaching your business goals.
