I remember when SEO was mostly about matching one keyword to one page. Research the phrase, optimise the heading, add internal links, and compete for rank. That model still matters, but it is no longer the full picture.
Today, AI search engines do not always treat a query as one query.
They often break one question into several related searches, run them at the same time, and combine the findings into one answer. Google explicitly describes this in AI Mode as its “query fan-out” technique, where a question is split into subtopics and multiple searches are issued simultaneously.
For brands, this changes content strategy completely. You are no longer only trying to rank for a single phrase. You are trying to be relevant across the full set of sub-questions an AI system may generate from that phrase.
If your content is clear, structured, and connected to related intent, you have a better chance of being surfaced, cited, or used in AI-generated responses. That is the same principle I keep returning to in AI-ready SEO strategy and in practical search engine optimisation services: clarity first, strategy second, automation only after that.
What is a fan out query?
A fan out query is a search process where one user question is expanded into multiple related sub-queries so the system can gather broader and deeper information before delivering one final answer.
In simple terms, instead of searching only for exactly what you typed, the AI explores the topic around it.
For example, a user may search:
“Best SEO strategy for a local skincare clinic in 2026”
An AI system may fan that out into questions like:
- best local SEO strategies for clinics
- SEO for skincare businesses
- technical SEO for service websites
- content strategy for local healthcare brands
- how AI search affects local business visibility
Google has publicly said AI Mode uses query fan-out by breaking a question into subtopics and issuing many searches at once. It has also extended that logic into visual search experiences, where image-based questions are expanded into multiple searches about the image and its objects.
From an SEO perspective, that means visibility depends less on one exact-match keyword and more on whether your content covers the surrounding context well.
That is why strong topical coverage matters. A thin page targeting one phrase is easier to miss. A connected content ecosystem is harder to ignore.
How does a fan out query work?
A fan out query works by taking one broad question, splitting it into smaller intent-based searches, retrieving information from several sources, and then merging the findings into one usable answer.
That is the short answer.
Here is what usually happens behind the scenes:
1. The system reads the intent, not only the words
AI search tries to understand what the user really wants. A question like “best CRM for a small remote team” is not just a software query. It includes budget, team size, use case, collaboration needs, and comparison intent.
2. It creates multiple sub-queries
Instead of relying on one retrieval path, the system expands the request into related searches. Google says this helps Search go “deeper into the web” than a traditional query.
3. It searches in parallel
Those sub-queries are run simultaneously, not one after another. That is the “fan-out” part.
4. It evaluates relevance and trust
The system then compares what it found across those searches and prioritises sources that appear credible, relevant, and directly useful.
5. It synthesises one answer
The user sees one summary, recommendation set, or explanation, even though several searches may have powered it.
This is why content formatting matters so much now. If your article clearly answers one question per section, uses descriptive headings, and supports claims with evidence, it is easier for AI systems to extract. That matches the writing approach I already use across industries like healthcare, beauty, travel, and service businesses: answer first, detail second, then strengthen the page with structure and trust signals. It also aligns naturally with foundational technical SEO and off-page SEO because authority still shapes what gets surfaced.
Best tools for fan out query research
The best tools for fan out query research are the ones that help you uncover question variations, related intent, and topical gaps, not just search volume.
There is no single perfect tool yet. I would use a stack.
1. AlsoAsked
Best for: mapping real question paths
AlsoAsked is useful when you want to understand how one question branches into related questions. That makes it a strong fit for fan-out style research, because it shows how search behaviour naturally expands from one topic into many. Its Deep Search feature is designed to return a larger question set around a topic.
2. AnswerThePublic
Best for: finding conversational query angles
AnswerThePublic helps surface the kinds of “what,” “how,” “why,” and “when” questions users ask around a topic. For content writers, this is valuable because fan-out queries are often phrased as natural-language questions rather than traditional keyword strings.
3. Semrush Keyword Magic Tool and Keyword Overview
Best for: validating which question clusters deserve priority
Semrush is useful when you need metrics, competition data, intent data, and topic validation. It is less about visualising fan-out directly and more about helping you turn the fan-out into an actionable content plan. Semrush also defines query fan-out in the AI search context as splitting one query into multiple sub-queries and merging the results into a single response.
4. Google Search Console
Best for: seeing what your site already captures
Search Console remains essential because it gives first-party performance data from Google. It will not show AI fan-out mechanics directly, but it will show the long-tail queries, pages, and topic patterns already driving impressions and clicks.
5. Otterly’s Query Fan-Out Tool
Best for: experimenting with AI search expansion patterns
Otterly offers a fan-out analysis tool specifically built to simulate how Google AI Overview, AI Mode, and ChatGPT may expand a query into related underlying searches. It is helpful for ideation, especially when you want to see how one seed topic could branch into multiple content opportunities.
My practical comparison
If I were building a client strategy today, I would use:
- AlsoAsked for question mapping
- AnswerThePublic for conversational phrasing
- Semrush for prioritisation
- Search Console for real performance insight
- Otterly for AI-search-specific testing
That combination gives you intent discovery, validation, and execution in one workflow.
How to optimise content for fan out queries
To optimise for fan out queries, build content that answers the main topic and its likely sub-questions in one connected structure.
That is the direct answer.
Here is the process I would actually use in an agency workflow.
1. Start with one core question
Choose one high-intent query that matters to the business.
Example:
“How can a beauty clinic improve visibility in AI search?”
2. Expand it into related sub-questions
Think about what an AI system may also need to answer:
- what is AI search visibility
- local SEO for beauty clinics
- technical SEO for clinic websites
- content that builds trust in healthcare-adjacent niches
- reviews, authority, and E-E-A-T signals
This is where tools like AlsoAsked, AnswerThePublic, Semrush, and your own sales conversations become more useful than a simple keyword list.
3. Structure every section around one question
This is the biggest practical shift.
Use headings such as:
- What is AI search visibility for clinics?
- How does local SEO support AI visibility?
- Which content formats build trust fastest?
- What technical fixes matter most?
Each section should answer the question immediately, then explain it. This makes the page easier for humans to scan and easier for AI systems to extract.
4. Build internal links around the topic
A fan-out-friendly page should not sit alone.
Link it to related service and blog pages so the site clearly signals depth. On Growwdigitaly, that could naturally include pages about SEO services, the main blog, and related educational pieces such as AI in SEO.
5. Add evidence and source credibility
AI systems do not only reward relevance. They also prefer content that feels trustworthy.
Use:
- named authorship
- recent examples
- cited sources
- clear definitions
- consistent terminology
Google’s own AI Mode documentation repeatedly frames fan-out as a way to find more relevant and credible content across the web.
6. Cover the topic broadly, not vaguely
Broad coverage does not mean padding.
It means addressing the real decision-making layers around the topic. In my experience, this is where content starts performing better across industries. When I worked across travel, beauty, and healthcare-related topics, the pages that won were rarely the ones stuffed with keywords. They were the ones that solved the entire question properly.
7. Refresh content regularly
Fan-out-driven discovery rewards freshness when the topic is changing. AI search, SEO workflows, and search behaviour are evolving fast, so stale pages lose usefulness sooner.
Why fan out queries matter for digital marketing agencies
Fan out queries matter because they shift content strategy from single-keyword targeting to intent coverage.
For agencies, that has three major implications.
Content briefs need to get smarter
A brief should no longer stop at one keyword and three supporting terms. It should include the main question, likely sub-questions, internal link opportunities, and proof points.
Topical authority matters more
If AI systems explore a topic from several angles, brands with thin, isolated pages are easier to skip. Brands with connected, useful content have more chances to appear.
Writers need to think like strategists
This is the part I care about most.
A good content writer today is not only writing paragraphs. They are shaping information so it can be understood, trusted, and retrieved. That means research depth, clear structure, and real user empathy matter more than ever.
Final thought
So, what is a fan out query?
It is the way modern AI search expands one question into many smaller searches before producing one answer.
And how should you respond to that as a marketer?
Do not write for one keyword alone. Write for the full question landscape behind it.
That means:
- answer first
- explain second
- connect related intent
- support claims with evidence
- build internal links that reinforce topic depth
That is how I would approach fan-out content for any modern brand, especially one that wants to stay visible as search becomes more AI-led.