GEO vs AEO vs SEO: What Actually Changes in AI Search?

SEO, AEO or GEO?

The question is often framed as though brands must choose one.

That is the wrong starting point.

Consider a bank that ranks well for home-loan interest rates. A prospective customer asks an AI assistant:

Which home loan is suitable for a salaried applicant who expects to prepay within five years?

The response may draw on the bank’s product page, fee schedule, eligibility criteria, FAQs, comparison websites and recent articles.

Ranking remains valuable. But ranking alone does not guarantee that the bank’s information will be selected, correctly qualified, cited or used in the final recommendation.

SEO has not stopped working. The path from discovery to decision has gained more stages.

The more useful question is:

What must an existing search programme add when the search engine can assemble the answer itself?

SEO, AEO and GEO in plain English

There is no universally accepted boundary between SEO, AEO and GEO. The terms frequently overlap, and different platforms and practitioners use them differently.

They are still useful when they describe distinct jobs within the same discovery system.

Search engine optimisation

SEO helps search engines discover, understand, index and rank webpages.

Its traditional focus includes technical accessibility, content quality, relevance, authority, internal linking and page experience.

Typical SEO measures include:

• Rankings
• Organic impressions
• Click-through rate
• Organic traffic
• Conversions

Answer engine optimisation

AEO, or answer engine optimisation, is commonly used to describe the work of making information easy to identify and extract as a direct answer.

Its focus is clarity.

Can a system identify the entity being discussed? Is the answer explicit? Are relevant conditions, definitions and relationships clear?

AEO grew around answer-led search experiences such as featured snippets and voice search, but the same principles of clarity and extractability are increasingly relevant to generative search.

Generative engine optimisation

GEO, or generative engine optimisation, focuses on whether information is retrieved, used, cited and accurately represented inside an AI-generated response.

The term was formalised in the research paper GEO: Generative Engine Optimization, later published at ACM KDD 2024.

The researchers examined how changes to web content affected source visibility within their experimental generative-engine environment. They reported visibility improvements of up to 40% for some techniques, while also finding that effectiveness varied significantly by domain. That figure should therefore not be interpreted as a universal traffic or citation uplift.

These are not three independent marketing channels.

They are overlapping layers of digital visibility.

Is GEO replacing SEO?

No.

Google makes this particularly clear in its official guide to optimising websites for generative AI features.

Google states that existing SEO best practices remain relevant because AI Overviews and AI Mode are rooted in its core Search ranking and quality systems. Its generative experiences use techniques such as retrieval-augmented generation to retrieve relevant webpages from the Search index and ground generated responses in current information.

A page therefore still needs to be accessible, indexable, useful and relevant.

Google continues to emphasise:

• Crawlable pages
• Helpful, original content
• Clear technical structure
• Descriptive internal links
• Important information in textual form
• Strong page experience
• Structured data that agrees with visible content

There is also no special GEO schema or AI markup required to appear in Google’s generative search experiences.

In fact, Google explicitly lists unnecessary AI-specific text files, artificial “chunking” and similar GEO hacks among the practices publishers do not need for Google Search.

Weak content does not become useful because it is labelled “GEO content”.

Nor does an enterprise need to create hundreds of near-identical pages for every possible prompt variation.

The fundamentals remain.

What changes is the environment in which those fundamentals operate.

What actually changes in AI search?

Search demand moves beyond isolated keywords

Traditional keyword research often begins with a compact phrase:

• Home loan interest rate
• Enterprise CMS
• Health insurance plans
• Business current account

An AI prompt can combine the user’s profile, constraints, comparison criteria and desired outcome in one request.

For example:

Which business current account is suitable for a small exporter that needs international payments, accounting integrations and low transaction fees?

Google explains that AI Overviews and AI Mode can use a process known as query fan-out, issuing multiple related searches across subtopics and sources to construct a more complete response. Google’s documentation explains how AI features use query fan-out.

Content planning therefore needs to support the broader decision, not merely the head keyword.

For a business current account, that could include:

• Eligibility
• Onboarding requirements
• Transaction limits
• Foreign-exchange charges
• Accounting integrations
• Service availability
• Support levels
• Comparisons with alternative products

This does not mean creating a separate article for every possible question.

It means ensuring the brand has credible, connected information covering the questions that influence the decision.

Ranking is no longer the only visibility outcome

In traditional search, visibility is usually tied closely to a position on a results page.

In generative search, a source may play several different roles.

It may:

• Appear as a cited source
• Support a factual statement
• Contribute information to a comparison
• Be mentioned without becoming the recommendation
• Be cited while a competitor is ultimately recommended

These outcomes cannot be understood through rank alone.

A brand may perform well in organic search but be absent from AI-generated shortlists. Another may receive citations for educational content but fail to appear for commercially important questions.

AI visibility therefore needs to be evaluated across the questions and decisions that matter to the business.

The unit of optimisation expands beyond one webpage

SEO teams have traditionally focused heavily on the page they want to rank.

That remains important. But AI-generated answers may reconcile information from several owned and third-party sources.

A company website may say that a product is available in 20 markets. An old PDF may say 16. A partner listing may say 12. A press article may repeat an outdated number.

Improving the main product page makes that page clearer.

It does not automatically remove conflicting information elsewhere.

For enterprises, GEO therefore introduces a broader source-management question:

Are important public claims current, qualified and consistent across the sources an AI system may encounter?

Those sources may include:

• Product pages
• Regional websites
• PDFs and brochures
• Help centres
• Newsroom articles
• Business listings
• Partner pages
• Reviews
• Independent publications

This is where AI visibility begins to overlap with content governance, brand management, PR and corporate communications.

For a deeper examination of this issue, see Your Brand Is Visible in AI. But Is It Accurate? on the Publive AXP blog.

Claims need evidence and context

Generative systems do not merely match keywords. They need information that can support an answer.

Compare these statements:

Our platform is the best solution for growing enterprises.

versus:

The platform supports 15 regional websites from one governed content environment, with role-based approvals and version history.

The second statement is more useful because it identifies a specific capability and the context in which it applies.

This is also consistent with the original GEO research, which found that adding credible citations, quotations and statistics could improve visibility in its experimental environment, although the effectiveness varied by topic and optimisation method.

For important product and brand claims, teams should make clear:

• What is being claimed
• What the claim applies to
• Which conditions or exclusions matter
• What evidence supports it
• When the information was last reviewed
• Whether a newer source supersedes an older one

The goal is not to make content mechanical or overloaded with statistics.

It is to make commercially important information explicit, defensible and difficult to misinterpret.

Measurement expands beyond traffic

Rankings, impressions, clicks and conversions remain essential.

They do not, however, show whether the brand is being used inside an AI-generated answer.

Relevant additional measures can include:

• Brand mentions
• Citation frequency
• Cited pages
• Recommendation frequency
• Competitive inclusion
• Factual accuracy
• Sources shaping the answer
• AI referral traffic
• Conversions or pipeline influenced by AI referrals

The measurement layer is now beginning to appear in mainstream webmaster platforms.

Microsoft introduced AI Performance in Bing Webmaster Tools in February 2026. The report shows citation activity, cited URLs and grounding queries across supported Microsoft AI experiences.

Microsoft also makes an important distinction: citation counts indicate that a page has been referenced, but do not by themselves represent ranking, authority or the role that page played in an individual answer.

Google similarly recommends using its generative AI performance reporting in Search Console to understand how content performs within Google’s generative search experiences.

Independent prompt testing can still add useful context because platform reports do not explain every recommendation, competitive comparison or factual error.

But AI visibility is becoming a measurable extension of search performance rather than an abstract brand exercise.

GEO is not a collection of AI-search hacks

The rise of GEO has produced a growing list of supposed shortcuts:

• Create a separate page for every possible prompt
• Rewrite every paragraph into tiny “AI-friendly” chunks
• Add special markup that guarantees citation
• Generate large quantities of long-tail content
• Manufacture brand mentions across third-party websites
• Add an AI-specific file and assume the job is done

These practices confuse optimisation with manipulation.

Google’s generative AI search guidance advises publishers to prioritise useful, original, non-commodity content rather than GEO hacks. It says publishers do not need unnecessary AI text files or special AI markup for Google Search, and warns against creating pages at scale primarily to manipulate search or generative responses.

Different AI platforms use different retrieval systems, crawlers and source-selection mechanisms, so Google’s guidance should not be treated as a universal description of every AI platform.

The broader principle, however, is useful:

Build information that is accessible, original, relevant, well-supported and easy to interpret.

What should enterprises do next?

The practical response is not to replace the SEO team with a GEO team.

It is to extend the existing search programme.

Preserve the SEO foundation

Continue investing in technical accessibility, indexability, internal linking, original content, authority and page experience.

Without this foundation, there is less reliable information for search and AI systems to retrieve.

Map the questions behind the buying decision

Move beyond a list of target keywords.

Identify the questions customers ask when evaluating eligibility, pricing, risk, implementation, comparison and suitability.

Then determine whether the website contains enough information to answer those questions credibly.

Make important information explicit

Review priority pages for vague claims, missing qualifiers and unsupported assertions.

Ensure that important facts, comparisons and limitations appear in clear textual content rather than existing only in images, interactive components or downloadable documents.

Review the wider public source set

Identify commercially or legally significant claims and check where they appear across webpages, PDFs, listings and third-party sources.

Resolve outdated information wherever the organisation has control. Where it does not, establish which external corrections, PR actions or partner updates are required.

Add AI-specific measurement

Track rankings and organic performance alongside AI mentions, citations, recommendations, accuracy and referral behaviour.

Use a stable set of commercially relevant questions so that changes can be compared over time rather than relying on occasional anecdotal prompts.

These should become part of ongoing search, content and governance operations, not a one-time GEO exercise.

So, is GEO replacing SEO?

No.

SEO makes content discoverable and eligible.

AEO helps make that information clear enough to be extracted and used as an answer.

GEO extends the problem further. It asks whether that information can be retrieved, trusted, cited and correctly represented when an AI system constructs an answer from multiple sources.

That distinction matters.

A brand can rank and still not be cited.

It can be cited and still not be recommended.

It can be recommended and still be represented using outdated or contradictory information.

So the progression is no longer simply:

Publish → Rank → Click

Increasingly, brands also need to think about:

Access → Understand → Cite → Recommend → Refer

SEO remains part of that journey. It simply no longer describes the whole journey.

SEO, AEO and GEO belong in the same system

For enterprise teams, the shift is not from SEO to GEO.

It is from optimising only for a ranked page to managing the wider path between a customer’s question, the public information available about the brand and the answer an AI system ultimately produces.

That means retaining what already works in SEO while extending the programme into machine readability, answer readiness, public-information consistency and AI-specific measurement.

The objective is no longer only to rank when someone searches.

It is to remain a reliable part of the answer when the search engine responds.