Want to Win AI Search? Start with Fan-Out Queries
Still planning content one keyword at a time? If you came of corporate age during the Golden Age of SEO (aka before LLMs entered the scene) that strategy makes sense. But hyper-focusing on individual keywords means you’re optimizing for a search engine that no longer exists.
Instead, plan your content the same way LLMs create answers.
Here’s what happens when someone types a question into Google. Unlike the previous days of top-10 blue links, AI Overviews and AI Mode don’t simply retrieve the top-ranking pages for the query. Instead, they silently break the question into roughly eight to 12 subqueries, which are all run in parallel.
From those subqueries, the LLM identifies specific relevant passages from across the web to synthesize an answer. This is called query fan-out, and it’s increasingly how your everyday searcher gets their information.
Because of query fan-outs, ranking #1 for any individual keyword won’t guarantee you’re cited in AI responses. One Surfer SEO study found that 67.82% of pages cited by AI weren’t in the top 10 at all. Not that ranking isn’t important: Brands are an astonishing 161% more likely to get cited in the AI response if they rank for fanout queries and the main query itself.
Winning SEO in 2026 requires going beyond keyword identification to plan out the constellation of fan-out queries you want to answer, too.
How fan-out queries actually Work
Google’s search pipeline runs in four stages:
The LLM breaks your query into subtopics
Then it retrieves passages on each subtopic from across its index
Sources are streamlined to a manageable size
An answer is synthesized from what remains
Step three: the streamlining. This is where content strategy comes in handy. iPullRank’s Mike King found that extractability, which includes things like heading-query match and schema markup, is one of the biggest predictors of which passages survive this phase. That means that stellar organic content strategies should place heavy focus on fan-outs.
One mental hurdle: If you’re used to logging keyword volume, fan-out subqueries may leave you anxious. Many fan-out queries go beyond long-tail, showing minimal or no monthly search volume in tools like Semrush or Ahrefs. That’s a core problem with keyword-first ideation; many super-important subqueries won’t come up in traditional keyword research.
You’ll need to reverse engineer your content strategy.
How to reverse engineer the fan-out
Don’t skip this step; it’s the only way to ensure your content strategy is targeted correctly.
There are a few practical ways to reverse engineer your fan-out subqueries. One easy way: Use a dedicated simulator. Qforia requires a Gemini API key; it will return 20–30 potential subqueries sorted by intent. AlsoAsked can be a powerful fan-out tool, too. Or, you can mine what you already have. Your Search Console long-tail queries often mirror the subqueries that AI models generate.
Take a kitchen tools brand eyeing the query “best knife sharpener.” A fan-out simulator may surface queries like:
Electric vs. manual sharpener
How often you should sharpen kitchen knives
Sharpening angle for Japanese vs Western knives
Best budget knife sharpener under $50
In the olden days, you may have crammed all of these subqueries into H2s—and that’s not always a bad option now, especially for queries that truly don’t merit their own article or who don’t align with brand priorities or expertise. Generally, though, strategists should turn each subquery into its own asset:
A comparison table pitting electric against manual sharpeners
An explainer video and article on sharpening frequency
A dedicated technique guide by knife type
Budget-focused roundups.
This creates four focused and independently citable pages, not one guide competing against itself.
From fan-outs to content calendar
Once you have a picture of the fan-out, you can transform those subqueries into a plannable category. Here’s how:
Cluster subqueries by theme and intent. You’ll find natural groupings, like comparison questions, troubleshooting, or “what’s the best…” type queries.
Match cluster to format, not just topic. Comparison subqueries need tables by necessity (and ideally shareable images, too!). How-to queries require checklists, and broad informational articles need FAQ blocks. This shouldn’t be an afterthought.
Build a pillar page for the core query. Just because fan-out queries are important doesn’t mean you should ignore the main keyword. The key: Keeping each page tightly focused. Skip the ultimate guides in favor of shorter, more digestible pieces, even for your core pillar.
Structure your sections to stand alone. AI models pull passages, not pages. Each H2 or H3 should function as a self-contained answer.
Prioritize the gap, not just the volume. As most fan-out subqueries will show no measurable search volume, you’ll have to use SERP and answer analysis to identify queries where citations are thin or dominated by only one or two competitors. That indicates a great opportunity.
One caveat…
Before you overhaul the calendar, here’s one thing to keep in mind: Fan-out queries are probabilistic, and not a fixed map. Precise subqueries will shift based on everything from phrasing to location and even which specific model is handling the query. Simulators can’t predict all these variations.
What’s not in question is the direction of change. AI isn’t a passing experiment, especially not in the world of digital growth. Content teams that make mapping fan-out queries part of their ideation process will enjoy a head start over those still planning one keyword at a time.
Overwhelmed? It’s not something you have to do alone. At Masthead, we can transform your key queries into a full organic content strategy designed around winning fan-out queries. Let's chat about how we can help.
