When I first started looking closely at Google AI Mode, I made the same mistake I think many SEO are making right now.

I treated it like another search feature that would eventually have its own checklist.

Add this schema.

Use this type of heading.

Write answers in this number of words.

Create pages for these specific prompts.

Do enough of those things, and you’d somehow “rank in AI Mode.”

But the more I looked into how Google actually describes AI Mode, the less useful that way of thinking became.

Google AI Mode is not simply another search-results layout.

It is designed for questions that involve exploration, reasoning, comparison, and follow-up searches. Instead of matching one query with one set of results, Google can break a question into several related searches, retrieve information from different parts of the web, and use those sources to build a broader response. Google calls this process query fan-out.

That changed the way I think about Google AI Mode SEO.

I no longer ask:

“How do I optimize one page for one AI Mode prompt?”

I ask:

“Is my website technically accessible, genuinely useful, and deep enough to contribute to the different parts of the question Google is trying to answer?”

That is a much better starting point.

And as of 2026, it is also much closer to Google’s own guidance.

What Is Google AI Mode?

Google AI Mode is an AI-powered search experience built for more complex questions.

Traditional Google Search is often straightforward.

You search:

best CRM software

and Google returns a set of results.

With AI Mode, someone can ask something much more specific:

“What CRM would work best for a five-person B2B sales team that needs email automation, does not have a technical administrator, and wants to spend less than $100 per month?”

That question contains several different problems.

Google may need information about:

• CRM pricing;
• team size;
• email automation;
• ease of use;
• technical requirements;
• plan limitations;
• product comparisons.

Instead of making the user perform six separate searches, AI Mode can investigate those parts and bring the information together.

Google says AI Mode is particularly useful for questions involving complex comparisons, deeper exploration, and follow-up questions.

This is why I do not think optimizing for AI Mode should start with changing paragraph length.

The bigger issue is whether your content can satisfy the different information needs behind complicated searches.

Why Google AI Mode SEO Is Different From Normal Keyword SEO

I still use keywords.

I still care about titles, internal links, indexing and search intent.

But AI Mode has made me think beyond the exact keyword.

In traditional SEO, I might find:

best project management software

and create a strong page around that query.

But someone using AI Mode may ask:

“Which project management tool is easiest for a remote team of eight people if we need task tracking, time tracking and client access but do not want something as complicated as Jira?”

Now Google has much more work to do.

It may need to understand:

• tools for remote teams;
• project management software for small teams;
• time-tracking capabilities;
• guest or client access;
• ease of use;
• Jira alternatives;
• pricing.

A page does not necessarily need to contain the user’s exact sentence.

Google’s systems are capable of understanding relevance without exact phrase matching.

The real challenge is whether your website provides enough useful information around the subject.

That is where content depth starts becoming much more important.

Query Fan-Out Is the First Thing I Would Understand

If there is one concept that changed the way I approach AI Mode, it is query fan-out.

Google explains query fan-out as a process where its AI system generates multiple related searches around the original question.

Imagine someone asks:

“How do I fix a lawn full of weeds without damaging the grass?”

Google gives examples of possible fan-out searches such as looking at herbicides, non-chemical weed removal and weed prevention.

This means the page Google eventually finds useful may not perfectly match the original wording.

It may answer one important part of the larger problem.

For me, this creates an important SEO lesson.

AI Mode optimization is not about guessing every fan-out query.

It is about covering your subject well enough that your content remains relevant when Google explores the topic from different directions.

Do Not Create a Page for Every Possible Fan-Out Query

This is where I think AI SEO can go wrong very quickly.

Once people hear that Google runs multiple related queries, the natural reaction is:

“Great. I’ll create a page for every possible query.”

I would not do that.

Imagine creating separate pages for:

• best CRM for five people;
• best CRM for six people;
• best CRM for seven people;
• CRM with email automation;
• CRM with easy email automation;
• easy CRM with email automation;
• affordable easy CRM with email automation.

Technically, you are covering more keywords.

In reality, you may just be creating dozens of weak pages saying almost the same thing.

Google specifically warns against creating large amounts of content around query variations primarily to manipulate rankings or generative AI responses. Its scaled-content policies apply whether that content is produced manually or with AI.

So instead of multiplying pages, I prefer to improve the coverage of the right page.

Crawlability Still Comes Before AI Mode Optimization

The first thing I would check on any site trying to gain AI Mode visibility is surprisingly boring:

Can Google crawl the page?

There is no point developing an advanced AI content strategy if Googlebot cannot properly access the content.

Google’s current documentation says that a page must be indexed and eligible to appear in normal Google Search with a snippet before it can be shown as a supporting link in AI Mode or AI Overviews. There are no separate technical requirements specifically for AI Mode.

So my AI Mode audit still begins with standard technical SEO.

I check:

• robots.txt;
• noindex directives;
• canonical tags;
• server responses;
• internal links;
• JavaScript rendering;
• duplicate URLs;
• crawl accessibility.

It is easy to get distracted by AI terminology and skip these basics.

But AI Mode still depends on information Google can discover and retrieve.

Check What Googlebot Can Actually See

This is particularly important on JavaScript-heavy websites.

Google can render JavaScript, but Google itself notes that JavaScript SEO is more complex than working with straightforward HTML.

If important product details, comparisons or article text only appear after complicated client-side actions, I want to know whether Google can reliably access them.

I use Search Console’s URL Inspection tools and look at the rendered page when needed.

I want to verify that Google can see:

• the main content;
• headings;
• important links;
• product details;
• images;
• supporting information.

Do not assume Google sees exactly what you see in your browser.

Check.

Indexing Is the Real Gateway Into AI Mode

The next step is indexing.

A page being crawlable does not automatically mean Google has indexed it.

And being indexed does not guarantee that it will appear in AI Mode.

Google makes this distinction clear: meeting the requirements makes a page eligible, but Google does not guarantee crawling, indexing or serving.

That is important because I would never diagnose weak AI Mode visibility before understanding normal indexing.

If an important page is not indexed, I check things like:

• whether the content is unique enough;
• whether Google discovered the page;
• whether internal links point to it;
• whether another URL is being selected as canonical;
• whether the page is duplicating another article;
• whether the content is substantial enough to deserve indexing.

AI Mode does not create a shortcut around poor indexing.

Internal Linking Becomes More Important With Deep Topics

One thing I pay much more attention to now is internal linking.

If AI Mode can explore a topic through several related searches, then I want my site structure to make those relationships obvious.

Suppose my main page is:

Complete Guide to Local SEO

That page might naturally connect with deeper pages about:

• Google Business Profile optimization;
• local citations;
• local keyword research;
• reviews;
• local landing pages;
• technical SEO;
• map visibility.

I do not necessarily want to force all of that information into one enormous article.

Instead, I want a useful content network.

The main page gives the broader explanation.

Supporting pages go deeper.

Internal links connect them naturally.

Google specifically recommends making important content easy to discover through internal links as part of optimization for its AI features.

That is why I see internal linking as more than a ranking tactic.

It is part of helping Google understand how my information fits together.

Content Depth Matters, but Length Is Not the Goal

AI Mode has made people talk a lot about “deep content.”

I agree with the idea, but I think it is often misunderstood.

Depth does not mean writing 6,000 words because competitors wrote 4,000.

It means answering the topic properly.

Some subjects can be covered extremely well in 800 words.

Others genuinely need 3,000.

For me, content depth means asking:

What would someone need to understand before they could confidently move forward?

Take a topic like:

How to choose accounting software for a small business

A shallow page may explain what accounting software is and then list five products.

A deeper page might help someone understand:

• what features actually matter;
• invoicing requirements;
• bank integrations;
• inventory needs;
• payroll considerations;
• multi-user access;
• pricing;
• reporting;
• business size;
• situations where each option makes sense.

That does not make the page better because it is longer.

It makes it better because it helps with more of the decision.

I Try to Cover the Next Question Before the Reader Has to Ask It

This is one of the biggest changes I have made in the way I structure content.

When I finish explaining something, I ask:

What would I ask next if I were the reader?

If I explain what Google AI Mode is, the natural next questions may be:

How does Google find sources?

What is query fan-out?

Do normal rankings matter?

How do I know if my site appears?

Do I need special schema?

Those questions guide the article structure.

This approach works naturally with conversational search because users can ask follow-up questions inside AI Mode.

Google designed AI Mode specifically to support those deeper follow-up interactions.

So instead of treating each query as completely separate, I try to build pages around the journey of understanding.

Original Information Has Become More Valuable

This is something I feel more strongly about in 2026 than I did a few years ago.

The web has no shortage of summaries.

If I search almost any major SEO topic, I can find dozens of articles repeating similar advice.

AI can summarize that information extremely well.

So I ask:

Why would Google need my page?

That question can be uncomfortable.

But it is useful.

If my article only repeats things Google can already retrieve from a hundred stronger sites, I have created very little reason for my page to become a source.

Google’s latest generative AI guidance specifically encourages unique, valuable and non-commodity content rather than simply recreating widely available information.

For me, that means adding more:

• first-hand experience;
• original screenshots;
• real examples;
• expert insight;
• calculations;
• original comparisons;
• research;
• case studies;
• data;
• observations.

This is one of the strongest ways I know to make content harder to replace.

I Would Rather Publish One Strong Example Than Ten Generic Tips

Suppose I am writing about indexing problems.

I could say:

Improve internal linking.

That is technically correct.

But it is generic.

A stronger section might show:

I found that several important pages on the site were only linked from the XML sitemap and had no contextual internal links from indexed content. After identifying them, I connected them from relevant category and article pages.

Now the reader understands what the problem looks like.

That kind of explanation is more useful than another bullet point.

This is how I think about depth for AI Mode.

Not more words.

More substance.

Clear Structure Still Helps

I do not believe Google needs every article written in a special “AI format.”

Google explicitly says site owners do not need to artificially chunk content into tiny blocks for generative search.

But clear structure still matters.

I try to use headings that tell the reader what the section actually covers.

For example:

Weak heading:
More Information

Better heading:
How Query Fan-Out Changes Keyword Research

The second heading immediately establishes context.

I also use:

• paragraphs that stay focused;
• descriptive H2 and H3 headings;
• tables when comparisons need them;
• lists when they genuinely simplify something;
• examples after complex explanations.

I do this for people first.

If it also makes the page easier for search systems to interpret, that is a useful side effect.

Important Information Should Still Exist as Text

I like visual content.

But I do not want critical information trapped inside an infographic.

Google’s AI-feature guidance recommends making important content available in textual form, while supporting that content with relevant images and video where useful.

So if I create a comparison infographic, I still explain the comparison in the article.

If I show a chart, I explain what it means.

If I create a video tutorial, I make sure the page still has enough context to understand the topic.

Text, images and video should support each other.

Images May Matter Even More as Search Becomes Multimodal

This is another part of AI Mode SEO I would not ignore.

Search is becoming increasingly multimodal.

Users can search with text, images and cameras instead of relying entirely on typed queries.

Google expanded Search Console reporting in September 2026 to provide insights into multimodal searches involving experiences such as Lens, Circle to Search and image uploads, including reporting inside its generative AI performance views.

That reinforces something I already consider good SEO practice:

Use original, useful visual content when the subject benefits from it.

For example:

• product photography;
• diagrams;
• comparison graphics;
• screenshots;
• charts;
• process illustrations;
• demonstrations.

Again, the goal is not to upload images because “AI likes images.”

The image should contribute information.

Do I Need Special Schema for Google AI Mode?

No.

This is one area where I would keep things simple.

Google currently says there is no special schema.org markup required to appear in AI Mode or AI Overviews.

I still use structured data where it is genuinely appropriate.

If it is a product page, Product structured data may make sense.

If it is an article, relevant Article markup may make sense.

If it is a local business, the appropriate business information matters.

But I do not add random markup expecting it to unlock AI Mode visibility.

Schema should accurately represent the page.

It is supporting information, not a shortcut.

You Do Not Need llms.txt for Google AI Mode

Another idea I would not prioritize specifically for Google Search is llms.txt.

Google’s current generative AI optimization guidance says that site owners do not need additional AI text files or machine-readable files for visibility in its generative search features.

That does not mean nobody on the internet will ever use llms.txt.

It simply means I would not treat it as a Google AI Mode ranking requirement.

If I had one hour to improve a page, I would rather spend that time fixing weak content, improving internal links or adding original information.

Page Experience Still Matters After the Click

AI Mode does not eliminate the website visit.

Google includes links specifically so people can explore supporting sources.

Once they click, the normal website experience matters again.

I still care about:

• mobile usability;
• loading performance;
• intrusive pop-ups;
• excessive advertising;
• confusing layouts;
• readability.

Google’s page-experience guidance continues to emphasize an overall good experience rather than one individual metric.

This matters even more if AI Mode gives the user several different sources to choose from.

Your page needs to justify their click.

Give People a Reason to Visit the Source

This is a bigger strategic issue.

Imagine AI Mode summarizes your entire article perfectly.

Why should someone click your link?

If the whole value of your page is:

“Here are five basic tips everyone already knows,”

there may not be much reason.

So I try to create pages where the summary is only the beginning.

The actual page can offer:

• complete examples;
• interactive tools;
• calculators;
• templates;
• downloadable resources;
• detailed case studies;
• original data;
• comparisons;
• full demonstrations.

AI Mode may tell someone that your resource exists.

Your page should give them a reason to explore it.

How I Measure Google AI Mode Visibility in 2026

This is one of the most important changes this year.

Until recently, measuring AI visibility involved a lot of manual checking and guesswork.

That is no longer the whole story.

Google introduced dedicated Generative AI performance reports in Search Console in June 2026 and completed the worldwide rollout by August 31, 2026. These reports include visibility from generative AI features such as AI Overviews and AI Mode.

The reporting can show:

• impressions;
• URLs that appeared;
• countries;
• devices;
• performance over time.

AI Mode activity is also part of the broader Search Console performance totals.

That gives me a much better way to approach optimization.

Instead of asking:

“Did I optimize this article for AI?”

I can start asking:

“Which of my pages are actually appearing?”

What I Look for in Search Console

Once pages begin gaining generative AI visibility, I would compare them.

I would look for patterns.

Do those pages:

• cover topics more deeply?
• have stronger internal links?
• contain unique information?
• include original images?
• answer more specific questions?
• already perform well organically?
• belong to a particular topic cluster?

Then I would compare them with pages getting little or no visibility.

This is more useful than following a generic AI Mode checklist.

Your own data may eventually tell you more about your site than another SEO theory does.

My Practical Google AI Mode SEO Process

If I were optimizing a website specifically with AI Mode in mind, I would work through it in this order.

1. Fix crawlability

Make sure Googlebot can reach important content.

Check robots.txt, server issues, JavaScript and internal links.

2. Fix indexing

Identify which important pages are missing from Google’s index and understand why.

3. Clean up duplication

Avoid having several pages fighting to answer almost the same question.

4. Map the real topic

Start with the main query and identify the related questions someone would naturally ask.

5. Build depth

Improve the page until it covers the decision or problem properly.

Do not add words simply to make it longer.

6. Add original value

Bring in experience, examples, screenshots, comparisons, data or something else competitors cannot easily reproduce.

7. Strengthen internal links

Connect the page with useful supporting content.

8. Improve visual content

Use images, video and diagrams where they genuinely help.

9. Keep structured information accurate

Use appropriate structured data, Merchant Center information or Business Profile data where relevant.

10. Measure the result

Use Search Console’s generative AI reporting to see which URLs actually gain visibility.

Then improve based on evidence.

That is much closer to how I think sustainable AI Mode optimization should work.

What I Would Not Do for Google AI Mode

There are several tactics I would avoid turning into a strategy.

I would not create hundreds of near-duplicate pages for guessed fan-out queries.

I would not stuff every article with questions.

I would not add unnecessary schema.

I would not rewrite every paragraph into a rigid 40-word block.

I would not assume llms.txt will improve Google visibility.

I would not publish huge amounts of generic AI content simply to cover more prompts.

I would not buy fake mentions around the web.

And I definitely would not ignore normal SEO because someone told me “SEO is dead.”

Google’s own documentation continues to describe traditional SEO fundamentals as the foundation for generative AI visibility.

The technology changed.

The need to create something useful did not.

The Biggest Lesson I Have Learned About Google AI Mode SEO

The biggest lesson for me is that AI Mode encourages me to think wider than the keyword but deeper than the summary.

Traditional keyword thinking sometimes pushes us toward exact phrases.

AI hype sometimes pushes us toward shallow answer blocks.

I do not think either extreme is ideal.

Instead, I want to understand the broader problem behind the search.

Then I want to create the strongest resource I realistically can around that problem.

Google can handle query variations.

It can run related searches.

It can connect information from different sources.

My job is not to predict every internal query.

My job is to make sure that when Google explores the subject, my site contains something genuinely useful to find.

Conclusion 

Google AI Mode SEO in 2026 is not about discovering a hidden optimization trick.

It starts with the same foundation I would expect from a healthy website:

Make the content crawlable.

Get important pages indexed.

Build a clear site structure.

Use internal links intelligently.

Answer the topic properly.

Then go further.

Think about the questions behind the query.

Think about the follow-up questions.

Add original information.

Create useful visual content.

Give people a reason to visit the source rather than only reading the summary.

And finally, measure what Google is actually showing through Search Console.

The biggest shift is not technical.

It is how I think about the search itself.

With AI Mode, Google may explore several parts of a problem before building its response.

So I do not want to create a page that only matches a keyword.

I want to create a resource that remains useful when the question gets deeper.

That, more than any AI SEO trick, is how I approach Google AI Mode in 2026.

Frequently Asked Questions

How do I optimize my website for Google AI Mode?

Start with normal SEO fundamentals. Make sure your pages are crawlable, indexed and internally linked. Then focus on satisfying the broader topic, answering related questions, adding original information and giving users a strong page experience.

Does Google AI Mode use normal search rankings?

Google says AI Mode is rooted in its core Search ranking and quality systems and retrieves information from Google’s Search index. However, AI Mode can also use query fan-out to search related subtopics, so the links shown do not necessarily mirror a traditional results page.

What is query fan-out?

Query fan-out is a technique where Google generates multiple related searches around the original question. It then retrieves relevant information from those searches to help build a more complete AI-powered response.

Should I create pages for every fan-out query?

No. I would focus on strong topic coverage rather than creating dozens of thin pages for small query variations. Google warns against scaled content created mainly to manipulate Search or generative AI responses.

Does content need to be long to appear in AI Mode?

There is no official word-count requirement. I focus on covering the topic as deeply as necessary without adding unnecessary sections simply to increase length.