Keyword research used to start with a fairly simple question:

What does the user type into Google?

AI search adds another layer.

Google can now take one complex search, break it into smaller subtopics, run several related searches, and use the results to build a response. Google calls this query fan-out. AI Mode and AI Overviews can use this process to explore parts of a question that the user never typed directly.

For marketers, this changes keyword research.

You still need primary keywords, search volume, search intent, and competitor analysis. But you also need to think about the extra questions an AI search system may need to answer before it can respond properly.

That is where query fan-out becomes useful.

The important limitation is this: Google does not give marketers a list of the exact internal fan-out queries generated for every search. You can estimate likely subqueries, validate them against real search behavior, and build better topic coverage, but you should not pretend you can see Google’s internal process.

This guide shows how to do that without turning one keyword into 50 thin articles.

What Query Fan-Out Actually Means

Google describes query fan-out as a process where its AI systems generate several related searches at the same time to gather more information for the original question.

Imagine someone searches:

How do I fix a lawn full of weeds?

A useful answer may need information about several things:

• removing existing weeds
• chemical treatments
• non-chemical treatments
• preventing weeds from returning
• lawn health
• timing
• grass type

The user entered one query.

The system may need several smaller searches to answer it well.

This is different from traditional keyword expansion.

A traditional keyword tool might give you:

• how to fix a weedy lawn
• fix lawn weeds
• lawn full of weeds
• lawn weed treatment

Those are mostly variations of the same phrase.

Query fan-out looks deeper. It asks what information is required to solve the problem.

For keyword research, that distinction matters.

Why Query Fan-Out Changes Keyword Research

Traditional keyword research often starts with a seed keyword and expands outward based on wording.

For example:

Seed keyword: email marketing software

Possible variations:

• best email marketing software
• cheap email marketing software
• email marketing software for small business
• email automation software

Useful, but still limited.

Now consider the question:

What is the best email marketing software for a small ecommerce store?

A useful answer may need to compare:

• pricing
• subscriber limits
• automation
• Shopify integration
• WooCommerce integration
• abandoned-cart emails
• deliverability
• templates
• analytics
• customer support
• migration difficulty

Those are not just keyword variations. They are decision factors.

This is the main value of query fan-out for SEO: it pushes you to research the full problem instead of collecting dozens of phrases that mean almost the same thing.

You Cannot See the Exact Hidden Queries

The phrase “hidden AI search queries” needs some care.

They are hidden because Google does not currently expose the complete set of internal fan-out searches behind an AI Mode or AI Overview response.

Search Console now has dedicated Generative AI performance reports for eligible sites. Those reports can show impressions, pages, countries, devices, and dates for visibility in generative AI features. Google was still rolling these reports out to a subset of sites when it announced them in June 2026.

That reporting is useful.

But it is not a window into every internal query generated by the model.

So when an SEO tool claims to show the exact queries Google secretly used, treat that claim carefully. Google itself says third-party tools do not have access to its internal ranking or AI systems.

What you can do is build a good approximation.

That is enough for content research.

Start With One Real Search Problem

Do not begin by asking an AI tool:

Give me 500 fan-out keywords.

You will get a long list, but most of it will be noise.

Start with one query that represents a real problem.

For example:

best cold email tool for recruitment agencies

Now ask what a buyer would need to know before making a decision.

Probably:

• monthly cost
• number of inboxes supported
• sending limits
• Gmail support
• Microsoft 365 support
• CRM integration
• email warm-up
• deliverability controls
• team features
• reporting
• sequence automation
• contact limits
• cancellation terms

That gives you your first fan-out map.

Some of these may become keywords.

Others may simply become sections within one useful page.

That distinction prevents content bloat.

Build Fan-Out Queries in Six Directions

A practical method is to expand the original query across six types of intent.

1. Definition and understanding

Ask what the reader needs to understand first.

For:

AI search optimization

Possible questions include:

• what is AI search optimization?
• how does AI search work?
• what is query fan-out?
• how is AI search different from Google Search?
• what is the difference between AI Overviews and AI Mode?

These questions establish the subject.

2. Comparison

People often need to compare options before making a decision.

Examples:

• AI SEO vs traditional SEO
• AI Mode vs AI Overviews
• ChatGPT Search vs Google AI Mode
• manual keyword research vs AI keyword tools

Comparisons are useful because they reveal decision criteria.

3. Constraints

Add the conditions that change the answer.

For example:

• for small businesses
• without paid SEO tools
• under a specific budget
• for ecommerce
• for B2B
• for beginners
• for local businesses
• with a small team

A search for:

keyword research

is broad.

A search for:

keyword research without paid SEO tools

has a much clearer problem.

4. Risks and objections

Look for what could go wrong.

Examples:

• can AI keyword research miss search intent?
• are AI-generated keywords reliable?
• does Google expose AI Mode queries?
• can creating pages for every fan-out query cause thin content?
• can AI search reduce organic clicks?

These questions are often missing from generic SEO articles.

They also make content more useful.

5. Process questions

Break the task into steps.

For query fan-out:

• how to choose a seed query
• how to identify subtopics
• how to validate fan-out queries
• how to group related keywords
• how to map queries to pages
• how to measure AI search visibility

Process queries are good candidates for guides and tutorials.

6. Follow-up questions

Think about what someone would ask after receiving the first answer.

Suppose the first query is:

How do I rank in Google AI Mode?

A reasonable follow-up could be:

Do I need special schema?

Then:

Does llms.txt help?

Then:

How do I measure AI Mode traffic?

These follow-ups expose content gaps.

Google specifically says there is no special AI schema or machine-readable AI file required to appear in its generative search features. Standard SEO fundamentals still apply.

That kind of factual answer is more valuable than another page titled “10 AI SEO Hacks.”

Use Google Search to Validate the Ideas

Once you have a fan-out map, validate it.

Do not publish based only on brainstorming.

Search the subtopics manually.

Look at:

• autocomplete
• related searches
• People Also Ask
• ranking pages
• forums
• videos
• product pages
• comparison pages
• repeated headings across competitors

You are trying to answer three questions.

First: Is there evidence people care about this subtopic?

Second: What type of page is Google already showing?

Third: Can your page add something useful?

If the search results are dominated by detailed tutorials, a 300-word glossary page is unlikely to satisfy the intent.

If the results are mostly shallow definitions, a practical guide may have room to compete.

Search Console Can Reveal Real Query Families

If your site already gets traffic, start with your own data.

Search Console Insights introduced query groups, which cluster related search queries into themes. You can see top groups, groups trending up or down, and drill into the individual queries inside a group.

These groups are not the same as Google’s internal query fan-out.

Do not confuse the two.

But they are useful because they show how different real user searches relate to the same subject.

Suppose a page receives impressions for:

• LinkedIn profile not showing in Google
• why LinkedIn profile not indexed
• LinkedIn public profile Google
• how long LinkedIn takes to appear on Google

You may have originally targeted only:

LinkedIn profile SEO

The Search Console data tells you what readers actually want to know.

Those questions can become new sections, updates, or supporting articles.

Turn One Keyword Into a Fan-Out Map

Here is a simple example.

Seed query:

social media marketing for local businesses

A basic keyword tool might return close variations.

A fan-out approach could produce this map:

Strategy

• which social networks matter for local businesses?
• how often should a local business post?
• organic social vs paid social

Budget

• how much should a small local business spend?
• can local social marketing work without ads?

Content

• what should local businesses post?
• how to create local content ideas
• customer reviews as social content

Measurement

• what metrics matter?
• how to track calls or store visits
• how to use UTM parameters

Platform-specific

• Facebook for local businesses
• Instagram local SEO
• TikTok for local services

Problems

• low engagement
• no followers
• posts getting views but no leads

Now you have a topic model rather than a pile of keyword variations.

A Commercial Keyword Can Fan Out Too

Query fan-out is not limited to informational searches.

Take a commercial phrase such as buy LinkedIn account.

A useful research map might include:

• what does an aged LinkedIn account mean?
• phone verified vs identity verified
• what recovery access is included?
• what restrictions can exist?
• how does account age affect visibility?
• what are LinkedIn’s account-transfer rules?
• what should a buyer check before payment?

These are hypothesized research questions, not a claim that Google uses those exact internal fan-out queries.

That wording matters.

The point is to understand what information sits around the commercial decision.

A product page can then answer the important questions directly or link to useful supporting guides.

Do Not Create a Page for Every Query

This is where query fan-out can go wrong.

You find 80 related questions and decide to create 80 articles.

That is usually a bad idea.

Google’s current guidance specifically warns against creating separate content for every possible search variation or fan-out query primarily to manipulate rankings or generative AI responses. It also says its systems can understand relevant content even when the page does not contain an exact keyword match.

A better rule:

Same intent = usually same page.

For example:

• what is query fan-out?
• query fan-out meaning
• how does query fan-out work?

These probably belong on one page.

But:

• how to track AI search traffic in GA4

is a different task and may deserve its own guide.

The goal is not maximum page count.

It is complete topic coverage without duplication.

Group Queries by Search Intent

Once you have 30–50 possible fan-out questions, group them.

A basic spreadsheet can use these columns:

| Query | Intent | Topic | Existing Page | New Page Needed? | Priority |
|—|—|—|—|—|
| what is query fan-out | Informational | Definition | Main guide | No | High |
| query fan-out example | Informational | Examples | Main guide | No | High |
| query fan-out vs keyword expansion | Comparison | Comparison | Main guide | No | Medium |
| track AI search traffic GA4 | How-to | Analytics | None | Yes | High |
| AI Mode SEO checklist | How-to | Optimization | Existing guide | Maybe | Medium |

This forces an editorial decision.

Without grouping, keyword research quickly becomes a publishing backlog full of duplicate ideas.

Prioritize the Queries That Change the Answer

Not every fan-out query deserves equal attention.

Prioritize questions that:

• change the recommendation
• expose a risk
• influence a buying decision
• require a separate process
• reveal a common misunderstanding
• have evidence of real search demand
• match your site’s expertise

Skip questions that only rephrase something you already explain.

For example:

What is query fan-out?

and:

What does query fan-out mean?

do not require separate sections.

But:

Can I see Google’s exact fan-out queries?

does.

The answer changes how the reader approaches the whole research process.

Use AI Tools for Expansion, Not Validation

AI tools are useful for brainstorming.

Give one model a seed query and ask:

What information would a person need to fully answer this question?

Then ask it to group the answer into:

• definitions
• comparisons
• constraints
• risks
• process questions
• follow-ups

This can save time.

But do not treat the output as search-volume data.

AI can invent plausible questions that nobody searches.

Validation still needs real evidence from search results, Search Console, customer questions, forums, sales conversations, or keyword data.

That separation keeps the process useful.

How to Use Fan-Out Research in Your Content

Once the research is complete, you have three main options.

Expand an existing page when the new query has the same intent.

Create a supporting article when the query requires a separate task or detailed answer.

Ignore it when it adds little value.

That last option matters.

Good keyword research is partly about deciding what not to publish.

Google’s current guidance for generative AI search still comes back to standard SEO fundamentals: indexed pages, crawlable content, internal links, clear technical structure, and useful information made for readers. There is no separate shortcut for AI search.

A Simple Query Fan-Out Research Workflow

For practical work, use this sequence:

1. Pick one real seed query.
2. Write down the decision or problem behind it.
3. Expand it across definitions, comparisons, constraints, risks, process questions, and follow-ups.
4. Search those ideas manually.
5. Check Search Console for related queries if you already have traffic.
6. Group queries by intent.
7. Map same-intent queries to one page.
8. Create new pages only when the user needs a meaningfully different answer.
9. Add internal links between related guides.
10. Measure which pages gain traditional and generative-search visibility.

Repeat the process after you collect real search data.

Your first fan-out map is a hypothesis.

User behavior tells you which parts were worth covering.

Final Takeaway

Query fan-out does not make traditional keyword research obsolete.

It fixes one of its weaknesses.

Instead of asking only, “What other phrases contain this keyword?”, ask:

“What else must be understood before this question can be answered properly?”

That question reveals comparisons, risks, constraints, follow-ups, and subtopics that simple keyword matching can miss.

You will not see Google’s exact hidden fan-out queries. You do not need to.

Build a reasonable map, validate it against real search behavior, combine queries with the same intent, and publish the pages that genuinely deserve to exist.