SEO

AI SEO vs Traditional SEO: What’s the Difference in 2026?

S3Decode September 16, 2026 55 min read

Search has changed faster in the last two years than it did for much of the previous decade. People are no longer relying only on a list of blue links to find information. They can ask Google’s AI Overviews or AI Mode a complex question, use ChatGPT to compare products, or turn to other AI platforms for recommendations and research.

The numbers show how significant that shift has become. BrightEdge found that AI Overviews appeared for about 48% of its tracked queries by February 2026, up from roughly 30% a year earlier. At the same time, traditional organic search still matters: around 52% of tracked queries did not trigger an AI Overview, meaning conventional search results remained the primary experience for those searches.

There is another important change hiding in those numbers. BrightEdge found that only about 17% of sources cited in AI Overviews also appeared in Google’s traditional organic top 10. In other words, ranking on page one and being selected as a source for an AI-generated answer are related, but they are not exactly the same thing.

That creates a new challenge for SEO teams. Traditional SEO has largely focused on helping a page rank for a query. AI SEO goes a step further: can your content be understood, trusted, referenced, and recommended when an AI system generates an answer?

This doesn’t mean businesses should abandon traditional SEO and start chasing every new “GEO hack.” Google continues to use its existing Search systems alongside AI features, and its guidance emphasizes the same fundamentals: technically accessible websites, useful content, clear information, and original value.

The difference is where that visibility appears and how success is measured.

Traditional SEO asks, “How high does my page rank?” AI SEO increasingly asks, “Does my brand or content become part of the answer?”

In this guide, we’ll compare AI SEO vs traditional SEO, look at how search behavior and content optimization are changing, explain whether backlinks and keywords still matter, and show how businesses can build an SEO strategy that works across both traditional search results and AI-powered search experiences.

What's Inside the Article?

AI SEO vs Traditional SEO: What Do They Actually Mean?

Before comparing AI SEO with traditional SEO, it helps to clear up one common misconception: AI SEO is not a replacement for traditional SEO.

Google’s own guidance says the fundamentals of SEO continue to apply to AI Overviews and AI Mode. Pages still need to be crawlable, indexable, useful, and eligible to appear in regular Google Search. Google also says there are no special AI-only technical requirements or schema markup that guarantee inclusion in its generative search features.

The difference is mainly how people search, how information is presented, and what kind of visibility a brand is trying to earn.

What Is Traditional SEO?

Traditional SEO is the process of improving a website so that its pages can appear prominently in conventional search results. For example, someone might search “best project management software for small businesses”

A traditional SEO strategy would focus on creating a useful page targeting that search intent, optimizing the title and headings, improving internal links, making the page technically accessible, and building relevant authority.

Traditional SEO typically involves:

Keyword research
Search intent analysis
On-page optimization
Technical SEO
Internal linking
Content creation
Backlink and digital PR strategies
Page experience
Organic ranking and traffic analysis

These fundamentals are still important. In fact, Google says its AI search experiences are rooted in the same core Search ranking and quality systems used across traditional Search.

What Is AI SEO?

AI SEO focuses on making your website, content, and brand more likely to be understood, surfaced, cited, or recommended within AI-powered search experiences.

For example, someone might search for a conversational prompt: “What are the best project management tools for a 20-person remote marketing team, and which one has the best collaboration features?”

This is much broader than a single keyword. An AI search system may need to understand the user’s requirements, break the question into related subtopics, retrieve information from multiple sources, compare options, and then produce an answer.

Google describes this process as query fan-out, where AI-powered Search can generate multiple related searches across different subtopics and sources before constructing a response. That changes what visibility can look like.

Your website might not simply need to rank for one exact phrase. It may need to provide specific, trustworthy information that can support several parts of a broader answer.

AI SEO vs Traditional SEO: Key Differences

AI SEO and traditional SEO share the same foundation, but they differ in how visibility is achieved and measured. Traditional SEO is heavily focused on ranking pages for searches, while AI SEO adds another layer: making information easy for AI-powered search systems to understand, use, and surface in generated answers.

Google also confirms that its existing SEO fundamentals remain relevant for AI Overviews and AI Mode, so businesses don’t need to treat AI SEO as a completely separate technical discipline.

Factor	Traditional SEO	AI SEO
Primary goal	Rank pages higher in traditional search results	Increase the chance of being surfaced, referenced, or represented in AI-generated answers
Main focus	Rankings, organic traffic and clicks	Visibility across AI-generated answers and traditional results
Search behavior	Often centered around keywords and individual queries	More conversational, contextual and multi-part questions
Keyword strategy	Target primary, secondary and related keywords	Cover the broader topic, entities, questions, relationships and user context
Search intent	Match a page to a specific search intent	Understand the complete context behind a complex question
Content approach	Create optimized pages targeting specific queries	Create comprehensive, useful content that can answer multiple related questions
Content structure	Headings, keywords, internal links and readable sections	Clear explanations, direct answers, supporting evidence and well-connected information
Authority	Domain authority, topical relevance, links and reputation	Topical authority, trustworthy information, original insights and recognizable expertise
Backlinks	Important for authority and rankings	Still useful, but brand reputation, references and credible information can also influence visibility
Visibility	Search result listings, featured snippets, image results and other SERP features	AI Overviews, AI Mode, citations, referenced sources and traditional search results
Competition	Compete for positions in search results	Compete to become a useful source within an AI-generated response
Traffic model	Generally designed to drive clicks from search results to a website	Can generate clicks, but may also create brand visibility without a traditional organic click
Measurement	Rankings, impressions, clicks, CTR and organic traffic	AI visibility, mentions/citations, impressions, brand searches, referral traffic and conversions
Optimization cycle	Optimize pages based on rankings, traffic and search performance	Improve content based on both traditional search performance and how well the brand/content is represented in AI search
Best content type	Keyword-focused landing pages, guides, comparison pages and articles	In-depth guides, original research, comparisons, expert explanations, data and highly specific answers
Technical requirements	Crawlability, indexability, site architecture, performance and structured data where relevant	The same technical SEO foundation; there is no separate AI-only technical requirement from Google
Long-term strategy	Build rankings and organic authority	Build search visibility, topical authority and brand recognition across traditional and AI search

The important part: these strategies are not opposites

The table makes one thing clear: AI SEO does not replace traditional SEO.

A technically weak website cannot compensate for that weakness simply by adding conversational content. Your pages still need to be crawlable, indexable and useful. Google specifically says that SEO best practices continue to apply to its generative AI search features.

The difference is that AI-powered search creates additional ways for your content to become visible.

A traditional SEO strategy might aim to get your article into the top 10 for a target query. An AI-focused strategy asks a broader question: Can the information on this page help answer several related questions that a user might ask?

That is why the strongest approach in 2026 is traditional SEO + AI search optimization.

You need the technical foundation and authority that traditional SEO has always required, while also creating content that is clear, specific, original and useful enough to support more complex search experiences.

What Has Actually Changed in Search?

Search hasn’t simply added an AI-generated paragraph above the traditional results. The more important change is that Google can now handle a search as a multi-step research problem rather than treating every query as an isolated keyword.

Google says AI Mode queries have more than doubled every quarter since launch, and AI Overviews now have more than 2.5 billion monthly active users. Google also says AI Mode has surpassed 1 billion monthly active users.

For SEO, that changes what a useful page looks like.

1. Search queries are becoming more specific and contextual

Take a search such as:

“What is the best CRM for a 50-person B2B SaaS company that is moving away from Salesforce and wants strong automation without enterprise-level pricing?”

An SEO professional would immediately recognize that this is not simply a “best CRM” query.

The search contains several decision factors: company size, business model, migration from an existing platform, automation requirements and pricing expectations.

Google’s AI Mode is designed for exactly these types of complex questions. Google says it can break a complicated request into multiple related searches across different subtopics and sources, a process it calls query fan-out.

That has a direct impact on content strategy.

A page that only has a paragraph about “the best CRM software” gives very little depth for such a recommendation. A stronger resource would compare CRM platforms based on implementation requirements, user limits, automation, integrations, migration considerations, reporting and pricing at different team sizes. The difference is subtle but important.

2. A ranking position is no longer the complete visibility picture

Traditional SEO reporting has trained marketers to think heavily in terms of positions. If a page moves from position 11 to position 4, that is normally considered a clear improvement.

That still matters. However, AI search introduces another form of visibility: being selected as a source for an AI-generated response.

BrightEdge’s 2026 analysis found that only around 17% of sources cited in AI Overviews also appeared in Google’s traditional organic top 10 for its tracked queries. At the same time, around 52% of the tracked queries did not trigger an AI Overview, showing why conventional rankings remain important.

This creates an interesting situation for SEO teams.

Imagine a technical website has an article explaining a very specific issue with a Java deployment environment. The article ranks seventh for a broad Java-related query, but contains a particularly useful explanation of a version mismatch, complete with the relevant error message, affected JDK versions and a tested solution.

A broader technology publication might rank higher for the general topic. Yet the more specific technical page can still be useful when an AI system needs evidence for that particular part of an answer.

That does not mean “rankings don’t matter.” It means that ranking and being referenced are now two different performance questions.

3. The best content is becoming more useful at the decision stage

Consider the difference between these two articles:

“10 Best AI Coding Tools in 2026”

and

“Cursor vs GitHub Copilot vs Claude Code: Which Is Better for Large Codebases?”

The first article can attract broad informational searches. The second is much closer to an actual technology decision.

An expert-level comparison would need to examine things such as repository context, agentic coding capabilities, IDE integration, multi-file changes, debugging, terminal access, model selection, usage limits, privacy and enterprise controls.

A reader deciding between these products does not simply need ten product descriptions. They need to understand why one tool would be preferable under a particular development workflow.

That distinction is increasingly important in AI search because recommendation-style questions are often more detailed than traditional keyword searches.

Google has specifically reported that AI Mode users are asking questions that are longer and more complex than traditional searches. For content teams, this means a strong comparison page should contain the evidence required to support a recommendation, not just the keywords associated with the products.

4. Original research can give a page information that competitors cannot easily reproduce

There is another major change that is easy to overlook. AI makes it extremely easy to create another article summarizing information that already exists.

Suppose 50 websites publish articles about AI adoption in small businesses. Most of them might repeat the same statistics, predictions and vendor claims.

Now imagine one company publishes a survey of 2,000 small businesses showing how many actually use AI, what they spend each month, which departments use it most, and what prevents wider adoption.

That research becomes a fundamentally different SEO asset. Other websites can cite it. Journalists can reference it. Industry analysts can discuss it. An AI-generated answer looking for current evidence about small-business AI adoption has a reason to consider it.

This is why Google’s guidance for generative AI search places emphasis on unique, valuable and non-commodity content rather than content created primarily by rearranging information that already exists.

For an SEO strategist, the question therefore changes from:

“What article can we create for this keyword?”

to a more useful question:

“What information can we provide that would make our page worth referencing?”

That might be original research, first-hand testing, proprietary data, expert commentary, product testing, industry surveys or a genuinely useful analysis.

5. Search journeys are becoming longer, but not necessarily through more Google searches

Consider someone evaluating project-management software.

They might begin with a broad question about the best platforms for a professional-services company. After seeing an AI-generated response, they can ask a follow-up about resource planning, then compare two specific products, then ask about migration and finally investigate pricing.

Google has deliberately built AI Mode around this kind of continued exploration. Its current Search experience connects AI Overviews and AI Mode so users can move from an initial answer into deeper follow-up questions while continuing to access links to the web.

For publishers, this makes topic depth more important.

A website that has only one generic article about project management software has limited coverage.

A site that also publishes detailed comparisons, implementation guides, pricing analysis, migration content, industry-specific use cases and alternatives has much more information available across the user’s research journey.

This is where topical authority becomes more meaningful than simply publishing dozens of articles around closely related keywords.

6. Brand and entity understanding matter more in recommendation searches

Consider a question such as:

“Is Semgrep better than Snyk for a development team that wants code security checks inside the developer workflow?”

To answer that well, a search system needs to understand both companies as entities and the relationships between their products, capabilities, target users and use cases.

A page that mentions “Semgrep” twenty times does not necessarily provide that understanding.

A strong comparison should explain what each platform actually does, which environments it supports, how its security workflow operates, where it differs from competitors and which type of development team is likely to benefit from each approach.

This is one reason modern SEO cannot be reduced to keyword placement.

Search engines need enough context to understand the subject, the relationships between entities and the reason one option may be more appropriate than another.

7. Evidence becomes more important when content is used for recommendations

Consider two statements: “This is the fastest AI coding tool.” and “In our test of 30 identical coding tasks, this tool completed 21 without manual intervention, compared with 17 for the second-best tool.”

The second statement is much more useful because the reader can understand what was measured. The same principle applies to SEO research. If an article claims that a particular platform is cheaper, the content should show the relevant plans and explain what is included.

If it claims that a tool is better for enterprises, it should explain why.If it claims that a technology is faster, it should identify the benchmark or testing methodology.

This type of evidence gives content more substance than simply repeating vendor marketing copy. It also makes the page more useful when search systems need to evaluate competing information.

8. The SERP is no longer the only competitive landscape

Traditional competitive research usually starts by examining the first page of Google. That remains essential, but AI search requires another layer of analysis.

Suppose you are trying to rank an article about AI data security tools. You should still examine the pages ranking for the main keyword. But you can also study which sources appear in AI-generated answers for related questions, what claims those sources support, which original statistics they provide and which topics repeatedly appear in the generated responses.

This produces a more sophisticated competitive analysis.

You are no longer studying only who ranks. You are studying which information sources are useful enough to appear across different parts of the search experience.

Google’s own documentation makes an important point here: AI Overviews and AI Mode are still rooted in Google’s Search systems, and websites need to meet the normal requirements for crawling, indexing and appearing in Search. There is no separate technical “AI SEO” markup that guarantees inclusion.

So, what actually changed?

The fundamentals of SEO have not disappeared. Technical SEO still matters. Search intent still matters. Internal linking, crawlability, indexability, useful content and authority still matter.

What has changed is the complexity of the search experience.

A page is no longer competing only for a position against another page. Depending on the query, it may also compete to become the source of a fact, the evidence behind a recommendation, the explanation of a technical issue, the basis for a product comparison or the reference supporting part of an AI-generated response. That is the real shift behind AI SEO.

Traditional SEO remains the foundation, but modern search visibility increasingly depends on how much useful information your website can contribute to the entire research process.

Does Traditional SEO Still Matter for AI Search?

Yes. In fact, treating traditional SEO as outdated is one of the biggest mistakes a business can make in 2026.

Google’s current guidance is clear: the same fundamental SEO practices that help pages appear in Search also apply to its generative AI features. Websites still need to be crawlable, indexable, technically accessible and useful to users. Google has also explicitly said there is no special markup or separate technical requirement that guarantees inclusion in AI Overviews or AI Mode.

The difference is that traditional SEO now forms the foundation, rather than the entire strategy.

Technical SEO is still the entry point

Before Google can understand whether a page is useful, it needs to be able to access and process that page.

A page blocked from crawling, accidentally marked noindex, poorly rendered, buried behind technical problems or unavailable to Googlebot has little chance of becoming a useful source in either traditional Search or AI-generated results.

The same principle applies to content that is technically accessible but difficult to interpret.

Clear page structure, descriptive titles, meaningful headings, internal links, canonicalization and accessible content still help search engines understand what a page is about.

This matters even more when a page covers a complicated subject.

For example, an article comparing enterprise AI platforms might contain pricing, features, security controls, integrations and use cases. If all of that information is buried in poorly structured content, it becomes harder for both users and search systems to understand the relationships between those details.

Technical SEO therefore hasn’t become less important because of AI. It has become the infrastructure that allows AI search to use your content in the first place.

Keyword research still matters, but keywords are no longer the whole strategy

Keywords remain useful because they reveal what people are searching for. The problem is relying on them as the complete definition of search intent.

Consider a company targeting:

“AI customer service software”

Traditional keyword research might lead to supporting terms such as:

AI customer service tools
AI customer support software
AI chatbot for customer service
best AI customer service platforms
AI support automation

Those terms are useful, but they don’t tell the entire story.

A buyer may also want to know whether the software integrates with Salesforce, how it handles human escalation, whether it can use company knowledge bases, how pricing changes with ticket volume, what happens when the AI gives an incorrect answer and whether customer conversations can be reviewed by support teams.

Those questions may not all have significant individual search volume. Yet they can be extremely important to the person making the purchasing decision.

That is why modern keyword research should be treated as market research rather than a list of phrases to insert into an article.

On-page SEO still helps establish what a page is about

Titles, headings, descriptive copy, internal links and well-organized content continue to provide important context.

Suppose you publish an article called:

“Salesforce Alternatives for Enterprise Teams.”

A weak version might mention Salesforce, HubSpot, Microsoft Dynamics and other competitors repeatedly without explaining how they differ.

A stronger page could organize the comparison around enterprise requirements such as customization, integrations, reporting, workflow automation, data governance, implementation complexity and total cost of ownership.

The second page gives search engines substantially more context about the topic.

It also gives AI systems more useful material to work with when a user asks a related question.

The goal, therefore, is not to remove traditional on-page SEO. It is to use it to make genuinely useful information easier to understand.

Internal linking becomes more strategic

Internal links have always helped search engines discover and understand pages. In an AI-heavy search environment, they can also help establish relationships between topics across a website.

Imagine a website covering cybersecurity.

Its main guide about application security might link to detailed resources covering:

SAST tools
SCA tools
API security
cloud security
vulnerability management
Semgrep alternatives
Snyk alternatives
developer security workflows

That structure tells a much stronger topical story than publishing isolated articles that happen to contain similar keywords.

It also gives users a path to investigate a subject in greater depth.

The important point is not to create hundreds of pages simply because AI search can generate many variations of a query. Google specifically warns against creating large amounts of low-value, unoriginal content for the purpose of manipulating Search.

Internal linking should connect genuinely useful resources, not manufacture artificial topical depth.

Backlinks still matter, but they are only one part of authority

Links remain an important part of SEO, particularly when they come from relevant and trustworthy websites.

However, AI search makes it useful to think about authority more broadly.

Imagine two websites publishing an article about enterprise AI security.

One has thousands of low-quality backlinks but publishes generic summaries copied from existing sources.

The other has fewer links but is known for publishing original security research, detailed technical analysis and expert commentary.

The second site may have stronger informational value even though it has fewer backlinks.

This does not mean backlinks have stopped working. It means link quantity should not be confused with overall authority.

A modern SEO strategy should build authority through relevant links, original research, expert contributions, digital PR, useful data and content that other publications genuinely want to reference.

Structured data has a role, but it is not an AI visibility hack

Structured data can help Google understand specific types of content and make pages eligible for certain search features. It remains useful where Google supports the relevant markup.

What it does not do is magically make a page appear in AI Overviews. This distinction is important because the growth of AI search has created a market for claims about special schemas, AI-specific markup and technical shortcuts.

Google’s current documentation specifically emphasizes that SEO best practices remain relevant for its generative AI features and warns against common misconceptions around so-called AEO and GEO hacks.

For most businesses, the better investment is not finding a secret AI schema. It is making the underlying page accurate, accessible, original and genuinely useful.

Traditional SEO gives AI SEO something to work with

This is the most important relationship to understand. AI SEO does not sit on top of a broken website.

If your site has poor technical SEO, weak content, unclear site architecture and no authority, adding conversational headings or writing content around AI-style questions will not suddenly make the site authoritative.

The stronger approach is cumulative.

Technical SEO makes the content accessible.
Content strategy gives the site useful information.
Internal linking establishes relationships between resources.
Digital PR and relevant links strengthen authority.
Original research adds information that competitors may not have.
Clear structure makes that information easier to interpret.

AI search then becomes another environment in which that content can be discovered, selected and referenced.

Google’s current documentation reinforces this point: its generative AI search experiences continue to rely on core Search systems, while AI Mode and AI Overviews can use information from multiple web sources.

How AI Search Evaluates Content Differently

Getting a page indexed is only the starting point. The harder question is whether the content contains enough useful information for a search system to rely on when answering a user’s question.

This is where AI search changes the content equation.

Traditional SEO often focuses on whether a page is relevant to a query and competitive enough to rank. AI search adds another layer: can the page provide a clear, reliable and useful piece of information that can support an answer?

Google’s current guidance for generative AI search emphasizes unique, valuable, non-commodity content and says SEO best practices continue to apply. It also warns against creating large amounts of unoriginal content simply to manipulate Search or generative AI responses.

AI search needs more than topical relevance

A page can be completely relevant to a topic and still provide very little useful information. For example, imagine an article targeting “Salesforce alternatives.”

A basic article might introduce HubSpot, Microsoft Dynamics, Zoho and Pipedrive, followed by short descriptions copied from each company’s positioning.

The page is relevant. It contains the right entities. It may even target the keyword effectively. But it doesn’t answer the questions that actually determine which alternative is appropriate.

A stronger article would explain differences in implementation complexity, customization, automation, reporting, integrations, user limits, pricing structure and the types of businesses each platform is designed for.

That additional information gives the page much more informational value.

The important distinction is between mentioning a topic and helping someone understand a topic.

AI search makes that distinction increasingly important because a generated answer may need specific facts, comparisons, explanations or evidence rather than simply a page that contains the target phrase.

Original information has a stronger advantage

One of the biggest problems created by generative AI is the ease with which websites can reproduce the same information.

Suppose hundreds of websites publish articles about AI coding assistants.

Most of them may discuss the same products, features and publicly available pricing. If every article simply summarizes the same information, there is little reason for an AI system or a human reader to prefer one page over another.

Now consider a publisher that actually tests the tools. It might compare how different coding assistants handle large repositories, debugging tasks, multi-file edits, code refactoring and test generation. It could document the methodology and explain where each tool performed well or poorly.

That article contains information that is much harder to reproduce through simple summarization. Google’s guidance specifically encourages site owners to provide information that is unique and valuable rather than commodity content that adds little beyond what is already available.

This is why original research, first-hand experience, testing, proprietary data and expert analysis can become important competitive advantages in AI search.

Evidence can make a page more useful than a longer article

More words do not automatically create better content. Consider an article claiming that one email marketing platform is better for ecommerce businesses than another.

Writing 3,000 words about both platforms does not necessarily make the comparison useful.

A stronger article might show actual pricing differences, explain automation capabilities, compare ecommerce integrations, identify limitations and include examples of the workflows each platform supports. The second article may be shorter, but it contains more decision-making information.

This distinction matters because AI-generated answers increasingly have to choose which information to include from multiple sources.

A page that provides a precise explanation or supporting evidence can be more useful than a much longer page filled with general observations.

Specificity matters when answering complex questions

Consider the difference between these statements: “Semgrep is a security tool for developers.” and “Semgrep provides static analysis and security scanning capabilities that can be integrated into developer workflows, making it relevant for teams that want security checks earlier in the software development lifecycle.”

The second statement gives substantially more context. It identifies the product category, the function, the audience and the use case. For AI search, this type of specificity can help establish relationships between entities and concepts. The same principle applies across industries.

A page about cloud cost optimization should not simply repeat that companies can “save money in the cloud.” It should explain mechanisms such as rightsizing, unused resource detection, committed-use discounts, workload scheduling and storage optimization.

A page about AI customer support should distinguish between chatbots, AI agents, retrieval-based systems, human escalation and automated workflow execution.

The goal is to give the subject enough context that both the reader and search system can understand what the page actually contributes.

Content structure still matters

AI search does not mean that traditional content formatting has become irrelevant. Clear headings, descriptive titles, concise explanations, tables where comparisons are useful and logical internal links can make complex information easier to interpret.

Suppose an article compares five enterprise observability platforms. A single 2,500-word block of text forces both the reader and search system to work harder to identify which platform offers which capability.

A well-structured page can separate the information into meaningful sections such as:

Section	Information provided
Platform overview	What the product actually does
Core capabilities	Monitoring, tracing, logs and analytics
Integrations	Cloud, DevOps and development environments
Pricing	Pricing model and important limitations
Enterprise features	Security, governance and administration
Best use case	Where the platform makes the most sense
Limitations	Situations where another option may be better

The same structure that helps a human understand a comparison can also make the underlying information easier for search systems to interpret.

AI search can use multiple sources for one answer

AI Overviews and AI Mode can draw from multiple web sources rather than relying on a single page to answer an entire question. Google describes these experiences as using information from a variety of sources, including web content.

That creates an interesting opportunity for specialized pages.

Your website does not necessarily need to publish the definitive article covering an entire industry.

It may be valuable because it provides one particularly strong piece of information.

For example, one publication might provide original pricing research. Another might publish a detailed technical comparison. A software company might provide documentation for a specific implementation problem. An analyst might publish an industry survey.

Each source can contribute something different.

This means SEO teams should start thinking about information contribution, not just article completeness.

Ask what your page can contribute that another source does not explain as clearly.

Commodity content becomes harder to differentiate

This is particularly important for websites using AI to accelerate content production.

AI can help with research, outlines, editing and content workflows. The problem begins when the output simply combines information that already exists without adding anything new.

Google’s spam policies explicitly identify scaled content abuse as a problem when large amounts of unoriginal content are created primarily to manipulate Search, regardless of whether generative AI was used to create it.

That creates a useful test for publishers:

If five competitors can produce essentially the same article by asking an AI tool the same question, what makes your version worth choosing?

The answer might be original research, stronger examples, expert commentary, first-hand testing, better data, clearer comparisons or a perspective based on genuine experience.

If the answer is simply “better wording,” the competitive advantage is probably weak.

The difference is information quality, not AI-friendly formatting

There is a growing tendency to treat AI SEO as a formatting exercise.

Add more questions.

Use conversational headings.

Write short paragraphs.

Add an FAQ.

Repeat the target topic in different forms.

These techniques can improve readability, but none of them automatically makes content valuable to AI search.

The more important question is whether the page contains information worth retrieving.

A technically perfect article with no original insight is still weak.

A well-researched article containing useful data, clear explanations and strong evidence has a much stronger reason to be referenced.

That is why Google’s current guidance does not present a separate set of secret optimization techniques for AI search. Instead, it continues to emphasize established SEO fundamentals alongside unique and valuable content.

The new content standard

Traditional SEO encouraged publishers to ask:

“Can this page rank for the target keyword?”

AI search makes another question equally important:

“If a search system needs reliable information about this subject, what does this page provide that is genuinely useful?”

The strongest pages in 2026 are increasingly built around that second question.

They don’t abandon keywords, technical SEO or rankings. They use those fundamentals while adding something harder to replicate: useful information, evidence, experience, context and original insight.

That is the content standard AI search is pushing SEO toward.

Biggest Content Differences Between Traditional SEO and AI SEO

The biggest mistake in AI SEO is assuming that traditional content simply needs to be rewritten in a more conversational style.

It doesn’t.

The real change is what the content needs to prove.

Traditional SEO often succeeds when a page clearly matches a search query, satisfies search intent, and provides enough depth to compete with other results. AI search adds another layer: the system needs to identify useful information inside that page and determine whether it is worth using in an answer.

Google’s own guidance now explicitly recommends creating non-commodity content for generative AI search experiences. It also continues to emphasize established SEO practices rather than a separate set of technical tricks for AI visibility.

That changes how content should be researched, written, and maintained.

From keyword coverage to information coverage

A traditional article might target:

“Best CRM for small businesses”

The page may then compare 10 CRM platforms, include the primary keyword in the title and headings, and explain the major features of each product.

That can still work.

However, a stronger AI-search-focused page would cover the actual decisions behind the query:

Which CRM works for a 10-person company?
Which one is better for a B2B sales process?
Which CRM has strong automation without requiring technical expertise?
Which platforms make Salesforce migration easier?
Which options have transparent pricing?
Which products support multiple sales pipelines?
Which CRM works well with existing marketing automation tools?
What are the limitations of each platform?

The difference is subtle but important. The first approach primarily covers the topic. The second covers the decision-making process around the topic.

That gives search systems more useful information to retrieve when users ask increasingly specific follow-up questions.

Evidence becomes more valuable than word count

Long-form content is not automatically better content. A 4,000-word article containing generic product descriptions may provide less useful information than a 1,800-word comparison containing accurate pricing, implementation details, limitations, testing results, and clearly explained differences.

Consider an article comparing AI coding tools.

A weak comparison might say: “Tool A is great for developers, while Tool B is better for advanced coding.”

A stronger comparison could explain how the products performed across specific workflows such as large-codebase navigation, multi-file changes, debugging, test generation, refactoring, terminal usage, model selection, privacy controls, and autonomous task execution.

The second page gives the reader something to evaluate. It also gives search systems specific facts and relationships to work with.

This is particularly important as Google says its AI search experiences can use information from multiple web sources to generate responses. Pages need to be accessible and crawlable if their content is going to be considered as a web source.

Original information creates a stronger reason to cite you

There is another important shift. If 100 websites publish essentially the same description of a product, another summary adds very little.

Original information changes that equation.

For example, instead of publishing another article titled “Best AI Meeting Assistants,” a site could conduct its own evaluation covering:

Evaluation area	What the research could measure
Meeting transcription	Accuracy across different accents and audio quality
Speaker identification	How reliably multiple speakers are separated
Action items	Whether tasks are captured correctly
Summaries	Accuracy compared with the actual meeting
Integrations	CRM, project management and calendar support
Processing time	How quickly recordings become available
Pricing	Cost at different team sizes
Limitations	Features missing from lower-tier plans

That research creates information other websites may reference.

It also gives the page a reason to exist beyond repeating information that is already available across dozens of competing pages.

Specificity matters more than generic completeness

AI search does not make generic content useless overnight. But generic content becomes easier to replace.

Compare these two statements:

Generic:

Semgrep is a security tool that helps developers find vulnerabilities.

More useful:

Semgrep provides code analysis that can identify security and code-quality issues during development. Its value depends on how a development team wants to integrate scanning into its workflow, including the languages, rules, CI/CD process, and security controls it already uses.

The second explanation gives more context.

It establishes relationships between the product, its category, its users, its capabilities, and the workflow in which it operates.

That type of contextual information is more useful when a user asks a narrower question such as:

“Which static analysis tools work well for a development team that wants security checks inside CI/CD?”

The goal is not to predict the exact wording an AI system will receive.

The goal is to make the page informationally complete enough to remain useful when the question changes.

Tables become more useful when they contain decision-making information

Tables have always been useful for comparison content, but AI SEO makes the quality of the information inside those tables more important.

A table containing only:

Tool	Rating	Price
Tool A	4.5/5	$20
Tool B	4.4/5	$25

doesn’t provide much context.

A better comparison might include:

Tool	Best for	Key strength	Main limitation	Starting price
Tool A	Small engineering teams	Fast setup	Limited enterprise controls	$20
Tool B	Enterprise teams	Governance and security	More complex setup	$25
Tool C	Security-focused teams	Deep code analysis	Steeper learning curve	$30

Now the table helps answer why someone would choose one option over another.

That is the type of information that makes comparison content genuinely useful rather than simply longer.

Content needs clearer relationships between facts

Another important difference is the relationship between pieces of information.

A traditional article might separately describe:

the product,
its features,
its pricing,
its target audience,
and its integrations.

A stronger page connects those facts.

For example:

Product A is aimed at larger engineering teams, supports advanced access controls, and integrates with enterprise development workflows. That makes it more suitable for organizations where governance is important, even though its higher-tier pricing and implementation requirements may make it less attractive to small teams.

That single paragraph connects audience, feature set, implementation, pricing, and use case.

Those relationships are often more valuable than simply adding another 500 words.

The new content test

Before publishing an article in 2026, don’t ask only:

“Can this page rank for the keyword?”

Ask a second question:

“What information does this page contain that another search result would not provide as well?”

That could be:

original research,
first-hand testing,
accurate comparisons,
proprietary data,
expert analysis,
documented limitations,
current pricing,
implementation experience,
unique examples,
or a clearer explanation of a complicated decision.

This is also consistent with Google’s current direction. Its generative-AI search guidance specifically calls out non-commodity content, while its spam policies warn against producing large amounts of unoriginal content simply to manipulate search visibility.

The implication for publishers is straightforward: AI SEO is not about making ordinary content look more conversational. It is about making the underlying information more useful, specific, defensible, and worth retrieving.

Backlinks vs Mentions vs Citations: What Matters in AI SEO

Backlinks have been one of the foundations of SEO for years.

But AI search introduces another layer of visibility: your brand, product, research, or website can become part of an AI-generated answer even when the traditional SEO signals involved are not identical to those used for ranking a page in a conventional results list.

That does not mean backlinks are dead.

It means SEO professionals need to stop treating every external reference as the same type of signal.

A backlink, a brand mention, and an AI citation serve different purposes.

What is the difference?

Signal	What it means	Traditional SEO value	AI SEO relevance
Backlink	Another website links to your page	Can contribute to authority and discovery	Can help establish relationships and authority, but does not guarantee AI citation
Brand mention	Another source discusses your company, product, or expertise	Can support brand visibility and reputation	Helps create broader context around an entity
AI citation	An AI search experience references your page as a source	May generate visibility and traffic	Directly connects your content with an AI-generated answer
Expert reference	A publication attributes knowledge or commentary to you	Builds credibility and visibility	Can strengthen the association between expertise and a topic
Original research reference	Other sources cite your data or findings	Can attract links and authority	Gives AI systems distinctive information to retrieve

The important point is that these signals overlap, but they are not interchangeable.

Backlinks still matter

AI SEO should not become an excuse to abandon link building.

A relevant editorial backlink can still help a website with discovery, authority, referral traffic, and conventional organic visibility.

For example, suppose an independent cybersecurity publication publishes original research about the cost of security vulnerabilities.

A major technology publication references that research and links to the original study.

That backlink can send referral traffic and provide a conventional SEO benefit.

But the bigger opportunity is the information surrounding the link.

The cybersecurity publication is now associated with:

original research,
a specific subject,
a specific dataset,
and an external publication willing to reference its work.

That is considerably more valuable than acquiring a random link from an unrelated website.

A mention can be valuable even without a link

Imagine a technology publication writes:

“S3Decode’s analysis of AI coding tools found that…”

but does not link to S3Decode.

From a traditional link-building perspective, that may look disappointing.

From a brand perspective, it is still meaningful.

The company is being associated with a particular piece of expertise.

This is why digital PR should not always be judged by a simple links acquired metric.

If a company is repeatedly mentioned by relevant publications, analysts, podcasts, communities, or industry sources in connection with a specific subject, it can develop a much stronger presence around that topic.

That does not mean every unlinked mention automatically improves rankings or guarantees AI visibility. There is no reliable formula that says a certain number of mentions produces a citation.

The better way to think about mentions is as part of the broader information environment surrounding an entity.

AI citations are a different kind of visibility

An AI citation is closer to the actual search experience.

Google says AI Overviews and AI Mode can generate previews of topics and queries using a variety of sources, including web sources. That means a page can potentially become one of the sources used to support an AI-generated response.

Consider a user asking:

“What are the best AI coding tools for a development team working on a large legacy codebase?”

An AI answer might combine information from several pages.

One source could provide pricing.

Another could explain repository context.

Another could contain independent testing.

Another could discuss enterprise security.

Your website does not necessarily need to provide every piece of information to be useful.

It may be selected because it provides one particularly useful part of the answer.

That changes how publishers should think about content.

Don’t optimize only for being “the best source”

A common AI SEO mistake is trying to create one enormous article that answers everything.

That is not always necessary.

A better strategy is to create strong individual resources that have clear information value.

For example, a software publication could have separate resources covering:

AI coding assistant pricing

A detailed comparison of plans, usage limits, and billing models.

AI coding assistant security

An analysis of data handling, privacy controls, enterprise policies, and code exposure risks.

AI coding assistant testing

A documented test comparing how different tools handle debugging, refactoring, test generation, and large repositories.

AI coding assistant comparison

A broader comparison that connects the findings from those individual resources.

Each page serves a different information need.

Together, they create a stronger knowledge base.

The quality of the referring source matters

Not all backlinks or mentions carry the same contextual value.

A link from a website about enterprise software to an enterprise software comparison is naturally more relevant than a link from an unrelated website.

For example:

Relevant

A developer publication references your benchmark of AI coding assistants.

Weakly relevant

A generic directory lists your website among hundreds of technology businesses.

Potentially problematic

A network of low-quality sites publishes nearly identical articles containing keyword-rich links to your pages.

The third approach may create the appearance of link volume without creating meaningful authority.

Google’s current policies continue to address attempts to manipulate search through low-value third-party content, including content published on established sites primarily to exploit their existing reputation.

For AI SEO, this distinction becomes even more important because the objective should be to build credible associations, not just accumulate URLs.

Context can matter more than anchor text

Traditional link building often focuses heavily on anchor text.

For example:

best AI coding tools

might be considered a desirable anchor for a page targeting that topic.

But consider a more natural editorial reference:

“The publication tested seven AI coding assistants across large repositories and found significant differences in debugging performance.”

The second reference communicates far more context.

It tells readers what the publication actually did.

That surrounding context can be more useful for understanding the relationship between the source and the subject than forcing an exact-match keyword into every backlink.

This is another reason modern link building should prioritize editorial relevance over anchor-text manipulation.

Original research can connect all three

The strongest content assets can generate backlinks, mentions, and citations at the same time.

Suppose an SEO company publishes a study analyzing 10,000 AI search queries across different industries.

The study contains:

the methodology,
the dataset,
industry breakdowns,
changes over time,
screenshots,
limitations,
and downloadable findings.

Google is also giving users more control over sources

There is another development worth watching.

Google now has a Preferred Sources feature that allows users to select websites they want to see more prominently. Google says selected sites can receive a preferred badge in AI Mode and AI Overviews for those users.

This is an important reminder that search visibility is increasingly about the relationship between users, publishers, and sources.

A publication that people repeatedly trust may have opportunities that are difficult to create through technical optimization alone.

For publishers, that makes brand building part of the SEO strategy rather than something completely separate from it.

What should you prioritize?

The practical approach is not to choose between backlinks, mentions, and citations.

Build the assets that can naturally produce all three.

Priority	What to build	Why it matters
1	Original research	Creates information others have a reason to reference
2	Expert analysis	Establishes topical expertise
3	First-hand testing	Differentiates content from generic summaries
4	Strong comparison pages	Helps users make specific decisions
5	Digital PR	Puts expertise and research in front of relevant audiences
6	Relevant editorial backlinks	Supports authority, discovery, and referral traffic
7	Brand mentions	Expands recognition and contextual associations
8	AI citation tracking	Shows whether content is appearing as a source in AI search

The goal is not to manufacture an “AI citation signal.”

There is no legitimate shortcut where publishing a certain number of guest posts, adding a particular schema type, or acquiring a fixed number of mentions guarantees inclusion in AI answers.

Google’s current guidance specifically pushes back against the idea that there is a special technical optimization required for generative AI search. Its documentation continues to emphasize established SEO fundamentals and useful, non-commodity content.

The more durable strategy is therefore straightforward: create information worth citing, build a recognizable reputation around that information, and earn relevant references from the web.

That approach benefits traditional search and AI search at the same time.

How to Optimize a Page for Both Google and AI Search

The biggest mistake in AI SEO is treating Google Search and AI search as two completely separate channels.

They are not.

A page that is technically accessible, useful, well-structured, relevant to the query, and supported by credible information can work across both environments. Google’s current guidance explicitly says established SEO best practices continue to matter for its generative AI features, while also emphasizing the importance of original, non-commodity content.

The practical approach is therefore not to create one page for rankings and another page for AI visibility. Instead, build a page that gives search systems more useful information to understand, evaluate, and potentially surface.

Start With the Decision the Searcher Is Trying to Make

Before optimizing a page, identify the decision behind the query.

For example, someone searching for “best AI coding tools” may not simply want a list of products. They may be trying to determine:

Which tools work well with large codebases?
Which ones can make multi-file changes?
Which are better for developers who use VS Code?
Which provide autonomous coding agents?
Which are affordable for individual developers?
Which offer enterprise security controls?
Which tools are better for debugging rather than code generation?

A page that answers only the original keyword is therefore much weaker than one that addresses the surrounding decision.

This is particularly important for AI search because complex questions can involve several related information needs before the system produces a final response.

Build the Page Around Information Coverage, Not Keyword Density

Traditional on-page optimization often starts with a primary keyword and then works through secondary keywords.

That process is still useful, but it should not determine the entire page.

Consider a page targeting “Salesforce alternatives.”

A weak version might mention Salesforce, HubSpot, Zoho CRM, Pipedrive, and Microsoft Dynamics 365 several times and give each product a short description.

A stronger page would examine the actual reasons someone is considering a move:

Decision factor	What the page should explain
Pricing	Entry-level pricing, higher-tier costs, and important limitations
Migration	Difficulty of moving data, workflows, and integrations
Customization	How much control users have over fields, workflows, and automation
Automation	Native automation capabilities and major limitations
Reporting	Reporting depth and suitability for different teams
Integrations	Important business applications and APIs
Ease of use	Learning curve and administration requirements
Enterprise use	Security, governance, permissions, and scalability
Best fit	Which type of company should consider each alternative
Switching risk	Where migration or implementation can become difficult

Now the page contains information that helps someone make a decision rather than simply satisfying a keyword.

That distinction matters for both traditional search and AI-generated answers.

Make Important Information Easy to Extract

AI search does not mean that every page needs to be rewritten as a collection of short questions and answers.

In fact, excessive formatting can make content worse.

Instead, make important information unambiguous.

If a product has a free plan but limits users to three projects, state that clearly. If enterprise pricing is custom, say so. If a feature is available only on a higher tier, identify the tier.

For example:

Best for large engineering teams: Tool A
Best for individual developers: Tool B
Best for autonomous coding: Tool C
Best for privacy-sensitive teams: Tool D

The value comes from the reasoning and evidence behind those statements, not the formatting itself.

A useful comparison table can then reinforce the decision:

Tool	Best for	Large codebases	Autonomous tasks	Enterprise controls	Starting price
Tool A	Enterprise teams	Strong	Yes	Strong	Verify current pricing
Tool B	Individual developers	Strong	Limited	Limited	Verify current pricing
Tool C	Agentic development	Strong	Strong	Varies	Verify current pricing

The exact fields should change according to the topic. A page about accounting software needs different decision factors from a page about AI coding tools.

Put Evidence Where the Claim Is Made

One of the most effective improvements is to stop separating claims from their supporting evidence.

Instead of writing:

Tool X is one of the fastest AI coding platforms available.

Explain what supports that assessment.

For example:

In our testing, Tool X completed a multi-file refactoring task across a large repository while preserving the existing test structure. However, it required manual intervention when the requested change crossed several unrelated modules.

That gives the reader something much more useful than a generic superlative.

The same principle applies to pricing, performance, security, integrations, benchmarks, product limitations, and feature comparisons.

AI search can potentially use a specific factual detail from a page without using the entire page. Google describes AI Overviews and AI Mode as drawing on multiple sources, including web sources.

That makes specific, reliable information increasingly valuable.

Strengthen the Page’s Entity Context

A page should make it obvious what companies, products, technologies, categories, and people it is discussing. Suppose you publish:

“Semgrep vs Snyk: Which Is Better for Enterprise Application Security?”

The page should establish the relationships between:

Semgrep and application security
Snyk and developer security
static analysis
software composition analysis
code security
dependency scanning
developer workflows
enterprise security teams
CI/CD pipelines

This does not mean stuffing every related entity into the article. It means explaining the subject deeply enough that the relationships are naturally clear. That creates a much stronger information model than repeating “Semgrep vs Snyk” dozens of times.

Use Internal Links to Build a Connected Topic

A strong page should not exist in isolation. If your site has separate resources covering:

AI coding tools
GitHub Copilot alternatives
AI coding tools under $20
AI bug detection tools
AI developer security
software development trends

Then the relevant pages should connect naturally. The purpose is not simply to increase the number of internal links. The goal is to help users move from a broad question to a more specific one while helping search engines understand how the site’s content fits together.

For example, a broad AI coding tools article can link to a detailed AI coding tools under $20 comparison when pricing becomes part of the decision. That creates a stronger topical structure than publishing dozens of loosely related articles targeting minor keyword variations.

Make the Page Technically Accessible

AI optimization does not remove technical SEO requirements.

Google’s current documentation continues to emphasize that content must be accessible to Googlebot if you want Google to crawl and index it. AI Overviews and AI Mode can use web sources, so blocking or otherwise preventing important content from being crawled can undermine visibility.

At a minimum, check:

Technical area	What to verify
Crawling	Important pages can be crawled
Indexing	Pages intended for search are indexable
Canonicals	The preferred version of the page is clear
Rendering	Important content is available when Google renders the page
Internal links	Important pages are discoverable
Mobile experience	Content works properly on mobile
Page speed	Avoid unnecessary technical overhead
Structured data	Use relevant supported markup correctly
Content controls	Make sure important content is not unintentionally restricted

There is also no need to chase every new AI-specific technical trend. Google currently states that llms.txt is not needed for Google Search and does not positively or negatively affect Search visibility or rankings.

That is a useful reminder: spend more time improving the information on the page than looking for an artificial AI-search shortcut.

Add Original Information Whenever You Can

This is one of the clearest ways to make a page more defensible. Imagine two articles targeting:

“Best AI coding tools in 2026.”

Article A collects information from vendor websites and produces a 5,000-word list. Article B tests eight tools using the same repository and evaluates:

codebase navigation
multi-file editing
debugging
test generation
refactoring
terminal commands
context handling
agent autonomy
error recovery
developer intervention

Article B has created something the first article does not have: first-hand information. The same principle works outside software.

A marketing website can publish survey data.
A cybersecurity publication can conduct product testing.
A SaaS company can publish anonymized benchmark data.
An SEO publication can analyze thousands of search results.
A developer can document performance across real repositories.

Google’s current generative-AI guidance specifically highlights the value of non-commodity content.

Optimize for Both the Search Result and the Source

A useful way to evaluate a page is to ask two separate questions.

Question one:
Would this page deserve to rank for the search query?

Question two:
If an AI system were answering a related question, does this page contain a fact, comparison, test, explanation, or piece of evidence worth using?

The strongest pages can answer both. That means you still need strong titles, headings, internal linking, crawlability, search intent alignment, and useful content. But you also need enough depth and originality to give the page a reason to be referenced.

The goal is not to “write for AI” by making every paragraph conversational. The goal is to make the page useful enough that both a searcher and a search system have a reason to trust it.

A Practical Optimization Checklist

Before publishing an important page, review it against these questions:

Area	Question
Search intent	Does the page solve the actual problem behind the query?
Information depth	Does it cover the important decision factors?
Originality	Does it contain information that competitors do not have?
Evidence	Are important claims supported by facts, testing, or reliable sources?
Specificity	Are limitations and tradeoffs explained clearly?
Entities	Are the relevant products, companies, technologies, and relationships clear?
Structure	Can readers quickly find important information?
Internal linking	Does the page connect naturally to related resources?
Technical SEO	Can Google crawl, render, and index the page?
Trust	Is the author or publisher qualified to make these claims?
Freshness	Are pricing, features, statistics, and product details still accurate?
AI usefulness	Does the page contain information an AI-generated answer could genuinely benefit from?

This is what optimizing for both search environments looks like in practice. It is not a separate “AI version” of SEO. It is a higher standard for the same page.

Which SEO Strategy Should You Invest In?

The answer is not traditional SEO or AI SEO. For most businesses, choosing one means leaving part of modern search visibility on the table.

Traditional SEO still provides the technical and content foundation required for search engines to discover, understand and rank a website. At the same time, AI search is changing how users explore complex topics and how information is presented to them.

Google’s own documentation reinforces this point. Its 2026 guidance on generative AI features emphasizes that established SEO practices remain relevant, while also encouraging publishers to create non-commodity content that provides genuine value. Google has also clarified that its spam policies apply to attempts to manipulate generative AI responses, not just traditional rankings.

The practical question, therefore, is not whether you should abandon traditional SEO. It is where you should put additional effort beyond traditional SEO.

If You Are a New Website

A new website should not start by chasing AI citations. Build the fundamentals first.

That means creating a technically sound website, choosing a focused topic area, researching genuine search demand, publishing useful content, developing internal links and earning relevant external references.

AI visibility becomes much easier to pursue once the website has something worth understanding and referencing.

For a new technology website, for example, a better first-year strategy could involve building a strong cluster around AI tools, software, development and digital marketing rather than publishing hundreds of unrelated AI-generated articles.

If You Already Have Organic Traffic

Established websites have a different opportunity. Instead of starting from zero, analyze which existing pages already receive organic visibility and determine where they could become substantially more useful.

Look for pages that:

Rank but receive few clicks
Rank for broad topics but lack depth
Receive traffic but have weak conversions
Contain outdated pricing or product information
Have strong rankings but little original information
Cover a topic without meaningful comparisons
Are frequently competing against stronger specialist resources

These pages can often produce a larger return than publishing another 50 new articles.

If You Are a SaaS or Technology Company

For software companies, AI SEO should be closely connected to the buying journey.

Don’t only target broad terms such as:

“CRM software”

or:

“project management software.”

Build resources around the questions buyers actually investigate before making a decision.

For example:

Salesforce alternatives
CRM migration
CRM pricing comparison
CRM for B2B SaaS
CRM integrations
CRM implementation
CRM security
CRM automation
CRM reporting
CRM alternatives for small teams

The same principle applies to almost any software category.

The more complex the buying decision, the more opportunities exist to create genuinely useful decision-support content.

If You Are a Publisher or Content Website

Publishers have an additional responsibility. AI SEO does not mean accepting every guest post, sponsored article or contributed article simply because someone wants a link.

Google’s site reputation policy continues to address situations where third-party content is placed on established websites primarily to exploit their ranking signals. Google clarified its enforcement approach again in August 2026.

A publisher should therefore protect its topical relevance. A technology publication becomes more valuable when its content consistently demonstrates expertise in technology, software, AI, development, business technology and related subjects.

It becomes less valuable when unrelated industries are added simply because advertisers are willing to pay for placement. Editorial standards are therefore part of SEO strategy.

A Practical 90-Day AI SEO Strategy

You do not need to rebuild an entire SEO program overnight.

A 90-day approach is enough to establish a useful baseline.

Days 1–30: Fix the Foundation

Start by auditing the existing website.

Review:

Crawlability
Indexing
Canonicals
Internal links
Duplicate pages
Page titles
Headings
Search intent
Content quality
Existing backlinks
Organic landing pages
Conversion paths

Then identify your most commercially important topics. Do not begin with hundreds of keywords. Choose a manageable set of topic areas where the business has genuine expertise or a realistic opportunity to develop it.

Days 31–60: Upgrade the Content

Next, improve the pages that already have potential.

For each priority page, ask:

Does this page simply describe the topic, or does it help someone make a decision?

Add missing information such as:

Comparisons
Pricing
Limitations
Use cases
Implementation details
Security considerations
First-hand testing
Expert commentary
Original research
Relevant examples
Updated statistics

Where possible, replace generic claims with evidence. Instead of saying a product is “powerful,” explain what it can actually do. Instead of calling a tool “best,” define the criteria used to make that judgment. Instead of writing another generic industry overview, add information competitors cannot easily reproduce.

Days 61–90: Build Authority and Measure Visibility

The final phase should focus on distribution. Promote your strongest resources through:

Digital PR
Expert contributions
Industry publications
Relevant guest articles
Original research
Expert interviews
Partnerships
Community participation
Data-led content

At the same time, establish your measurement baseline. Track conventional organic performance alongside AI visibility, brand searches, referrals, engagement and conversions.

This allows you to identify which topics are producing genuine business value rather than simply generating impressions.

What Will Matter Most in AI SEO?

There is no single AI SEO tactic that will replace everything that came before it. The strongest signals are likely to come from the combination of several fundamentals:

Area	What matters
Technical SEO	Crawlable, indexable and accessible content
Search intent	Understanding the real problem behind the query
Content depth	Covering the information required to make a decision
Originality	Providing information competitors do not have
Evidence	Supporting important claims with credible information
Entity clarity	Making relationships between companies, products, people and concepts clear
Expertise	Demonstrating genuine knowledge and first-hand experience
Authority	Earning relevant references and recognition
Internal linking	Connecting related information naturally
Freshness	Updating information that changes over time
Brand	Building recognizable expertise within a topic
Measurement	Tracking search, AI visibility and business outcomes

None of these is a magic switch. Together, however, they create the type of website that can perform across multiple search experiences.

FAQs

Is AI SEO replacing traditional SEO?

No. AI SEO builds on traditional SEO rather than replacing it.

Technical SEO, crawlability, indexing, search intent, useful content, internal linking and authority remain important. AI search adds another visibility layer where content may be selected as a source for an AI-generated response.

Is AI SEO just AEO or GEO?

Not exactly.

AEO, GEO and AI SEO are often used interchangeably in the industry, but the terminology is not standardized.

The more useful approach is to focus on the underlying objective: making your website’s information easy to understand, useful, trustworthy and relevant to modern search experiences.

Google’s own guidance also warns against treating AEO/GEO as a collection of secret optimization tricks. Its current generative AI guidance emphasizes established SEO practices and non-commodity content instead.

Do I need special schema markup for AI Overviews?

There is no special schema markup that guarantees inclusion in AI Overviews or AI Mode.

Use structured data when it genuinely describes supported content and follows Google’s guidelines, but don’t treat schema as an AI visibility shortcut.

Does llms.txt improve AI SEO?

Google says llms.txt is not needed for Google Search and does not positively or negatively affect Google Search visibility or rankings. Other AI systems may choose to use such files, but publishing one should not be treated as a Google SEO requirement.

Are backlinks still important for AI SEO?

Yes, but backlinks should be viewed as part of a broader authority and distribution strategy.

Relevant editorial references, original research, expert coverage and digital PR can all help establish a brand’s presence within its subject area.

However, no legitimate link-building tactic guarantees that a page will be cited in an AI answer.

Should I create separate pages for AI search?

Usually, no.

Create pages around distinct information needs rather than creating one version for traditional search and another version designed to “sound good” to AI systems.

A strong page can serve both audiences.

Can AI-generated content rank in Google?

AI-generated content is not automatically disqualified from search.

The bigger issue is whether the content provides genuine value.

Google’s spam policies specifically identify scaled content abuse where large amounts of unoriginal content are produced primarily to manipulate rankings, including content generated with AI when it provides little or no value to users.

The question is therefore not simply “Was AI used?”

It is “What value did the publisher add?”

How do I know if my website is appearing in AI search?

Use Google’s Search Console reporting where available, monitor the queries and topics that matter to your business, and analyze which pages are gaining visibility.

Google has also introduced broader reporting around generative AI search features, while AI Mode data is included in overall Search Console totals.

You should also monitor branded searches, referral traffic, engagement and conversions because AI visibility may influence discovery without producing an immediate organic click.