Does GEO affect measurability and reporting?

Yes. GEO changes how visibility is measured. Because AI answers often do not require a click, the focus shifts from traffic to brand mentions, source citations, and the context in which a brand is referenced. This calls for additional measurement methods alongside traditional KPIs such as rankings and clicks, for example monitoring AI generated answers and brand mentions.

Can you apply GEO without restructuring your entire website?

Yes. In many cases, you can already gain a lot by tightening the structure of existing pages, using clear headings and sections, improving internal linking, and explicitly marking key information with structured data or schema. You do not have to rebuild everything from scratch, but GEO is more than just text. Your site architecture and technical signals also play a role.

Is GEO only relevant for large or well-known brands?

No. Smaller or more specialized organizations can benefit from GEO as well. AI systems do not look only at brand awareness, but also at subject matter expertise and relevance. A niche player with clear, well-structured, and trustworthy content can be selected just as easily, and sometimes even more often, than a big brand.

GEO: zichtbaarheid in AI-antwoorden

GEO in a nutshell

GEO is about visibility within AI-generated answers, not traditional search results. Key points:

AI systems select sources based on clarity, authority, and structure — not on keywords or backlinks.
Less than 10% of the sources AI engines cite appear in Google’s top 10 organic results (eMarketer, 2026).
Brands that aren’t included in AI answers miss a growing share of their potential reach.
GEO doesn’t replace SEO — it extends it into a new type of search experience.

What does GEO mean

Generative Engine Optimization (GEO) describes a digital marketing approach focused on visibility in AI-generated answers. Traditional search engine optimization is about rankings in search results. GEO is about whether your content is selected, interpreted, and presented by AI systems such as ChatGPT, Perplexity, Google Gemini, and Claude.

The reason for this shift lies in how people search. More and more, users ask questions to AI assistants and expect a direct, coherent answer — not a list of ten blue links, but a summary they can use right away. For organizations, this means being discoverable in Google is no longer enough. The question becomes: is your brand mentioned when someone asks an AI assistant for advice?

This page explains what GEO is as a concept, why it matters for organizations that want to stay visible online, and which strategic domains together make up this field. The focus is on understanding the shift, not on applying specific tactics.

What GEO means in the AI search landscape.

GEO emerged as a discipline between 2023 and 2024, when AI-driven search interfaces such as ChatGPT, Bing Chat, and Google’s Search Generative Experience were rolled out at scale (Ramp, 2024). The rise of these systems changed how users find information. Instead of scanning a page of search results, they now often receive a direct answer, assembled from multiple sources.

For marketers and content creators, it became clear that the rules were shifting. Content that performed well in traditional search engines didn’t automatically show up in AI-generated answers. The reason is simple: AI systems work differently. They analyze meaning, context, and credibility to produce a coherent response, rather than ranking pages based on keywords and backlinks (One400, 2026).

GEO is the response to that shift. It describes the strategies and considerations that ensure content is understood, trusted, and used by AI systems when they assemble an answer.

The difference from traditional SEO

The difference between GEO and SEO isn’t so much in the techniques, but in the goal. SEO focuses on improving rankings in search results. GEO focuses on being cited or summarized in AI answers.

Traditional SEO is about:

Keyword rankings
Backlinks and domain authority
Click-through rates from search results

GEO is about:

Being included in AI-generated answers
Being interpreted and summarized correctly
Being recognized as a trustworthy source by AI models

Both disciplines complement each other. A strong SEO foundation supports content discoverability, while GEO ensures that content is also included when AI systems assemble answers (MadHawks, 2025).

The role of answer generation in modern search systems.

I-driven search environments don’t simply return a list of relevant pages. They synthesize information from multiple sources into a single, coherent answer. This changes the relationship between a query and its result.

A traditional search engine sends the user to a website to find the answer there. A generative search engine provides the answer directly. The website isn’t always visited, but it may still be mentioned as a source. This phenomenon, often referred to as zero-click search, has direct implications for how organizations should approach their online presence.

The implication is clear: content that isn’t included in the synthesis effectively doesn’t exist for users who search via AI.

How generative search engines process information

To understand why GEO matters, it helps to know how AI systems select and process sources. This isn’t a technical manual, but a conceptual outline of the mechanisms that determine which information becomes visible.

Generative AI engines are essentially predictors. They generate answers based on patterns learned from vast amounts of text, sometimes supplemented with real-time web access in certain models.

When a user asks a question, the system typically goes through this process:

Question interpretation: the model determines what the user wants to know, including implicit context and intent.
Source selection: the system identifies which sources are relevant and trustworthy for answering the question.
Synthesis: information from multiple sources is merged into a coherent answer.
Presentation: the answer is produced in natural language, often with citations.

The criteria for source selection differ per AI system, but a few patterns show up consistently.

AI models tend to prefer content that is:

Clearly and unambiguously written
Published by sources with demonstrable expertise
Consistent with other authoritative sources
Structured in a way that’s easy for machines to process

Research by First Page Sage (2025) shows that ChatGPT bases 41% of its recommendations on mentions in authoritative lists, 18% on awards and accreditations, and 16% on online reviews. Google Gemini uses similar criteria, but weighs Google Business Profile reviews more heavily for local queries (38%).

One striking finding: fewer than 10% of the sources cited in generative AI engines rank in Google’s top 10 organic results (eMarketer, 2026). This underlines that traditional SEO success is not a guarantee of visibility in AI answers.

The position of brands and organizations in AI-generated answers

For organizations, the question is shifting from “how do we rank higher in Google?” to “are we mentioned when AI gives an answer?” This is more than a technical issue. It touches brand perception, authority, and how potential customers encounter an organization for the first time.

When a user asks an AI assistant for a recommendation, for example for a service provider or a product, they often receive a concise overview with a few options. The brands that appear in that overview have an immediate advantage. The brands that do not appear are simply not considered, regardless of their quality or reputation.

This mechanism is fundamentally different from traditional search results. With a Google search, a user can scroll through multiple pages and choose which websites to visit. With an AI answer, that freedom is more limited. The AI system has already made the selection.

Visibility versus discoverability

In traditional SEO, the focus is often on discoverability, meaning how easily a website can be found via search queries. In the context of GEO, the focus shifts to visibility, meaning whether a brand is included in the answers AI systems generate.

The distinction is subtle but relevant. Discoverability means your website appears in search results, and the user can decide to click. Visibility means your brand is mentioned in the answer the user sees immediately.

An organization can be highly discoverable in Google, yet completely invisible in AI answers. Conversely, a brand with modest SEO rankings can still be cited by AI systems, as long as its content meets the criteria those systems use.

The changing relationship between source and end user

In the traditional search model, there is a direct relationship between the source, the website, and the end user. The user clicks a search result, visits the website, and consumes the content within the context the site provides.

With AI generated answers, that relationship becomes indirect. The AI acts as an intermediary that pulls information from sources, processes it, and presents it to the user. The source may be mentioned, but the user does not have to visit the website to get the answer.

This changes how organizations need to position their content. Content must not only be valuable to the end user, but also understandable and usable for the AI system acting as the intermediary.

What is interesting is that generative search tools are also creating new patterns in website traffic. Research by Siege Media (2025) shows that brands saw an average of 10.7 percent more homepage clicks via generative search tools, even while total site visits declined. This suggests that AI answers can function as a kind of brand introduction, after which interested users may still visit the website.

The relationship between content structure and AI interpretation

The way information is organized largely determines how AI systems understand and apply it. This is not about fixed formats or technical rules, but about how clearly the content is put together and how easily a machine can follow it.

AI models are trained on enormous amounts of text and recognize patterns in how information is presented. Texts with a clear buildup, a strong structure, and logical connections are therefore easier to process than texts that are loosely written or phrased ambiguously.

Several characteristics influence how interpretable content is:

Semantic clarity: Sentences that express exactly what is meant, without ambiguity.
Logical structure: An organization where the relationship between sections is clearly signposted.
Context richness: Enough background information to understand meaning and relevance.
Consistency: Uniform terminology and a consistent style throughout the text.

Generative engines tend to prefer content with depth, clarity, and practical usefulness (MadHawks, 2025). They can distinguish between content written mainly to rank and content that is genuinely useful to the user.

This does not mean all content needs to be rewritten for AI. It does mean that content that is vague, fragmented, or overly promotional will likely be ignored when AI systems select sources for their answers (One400, 2026).

The implication for organizations is that content quality, measured in clarity and usability, directly affects the likelihood of being included in AI generated answers.

Geographic context and language in AI search results

ocation, language, and regional context influence how AI systems assemble answers. This matters for organizations operating in specific markets or offering local services.

AI models try to generate answers that match the questioner’s context. When someone in the Netherlands asks a question about a local topic, the system will attempt to select sources that are relevant to the Dutch context. In practice, this does not always work perfectly, especially when the amount of high quality information available in Dutch is more limited than in English.

For organizations active in the Dutch market, this means Dutch language content plays a role in visibility within AI answers for Dutch speaking users. At the same time, international sources may be included when local information is missing or considered less authoritative.

How AI weighs local and international sources.

The tension between local relevance and global information sources is a recurring theme in GEO. AI systems can pull information from around the world, but they still have to decide which sources are most relevant for a specific question in a specific context.

For local commercial queries, such as searching for a service provider in a particular region, AI systems typically apply different criteria than they do for general informational questions. Google Gemini, for example, weighs local business reviews more heavily for this type of query (First Page Sage, 2025).

This creates a complex situation for organizations. On one hand, visibility in international, authoritative sources can matter. On the other hand, local presence and reputation play a major role for specific types of searches.

The strategic question is not so much how to “solve” this, but how an organization thinks about the different contexts in which its content may be found and used.

GEO as part of a broader digital strategy

GEO does not stand on its own. It is part of a wider mix of disciplines and channels that together shape how visible an organization is online. If you treat GEO separately from your other marketing activities, you miss opportunities.

That is because AI systems pull information from many different sources. They do not look only at your website, but also at mentions in trusted media, reviews on external platforms, social media, and other signals that indicate the reputation and expertise of your brand.

This means GEO visibility is influenced by activities that have traditionally fallen under other disciplines.

PR and media relations. Mentions in authoritative publications.
Reputation management. Reviews and ratings on external platforms.
Content marketing. The quality and depth of your owned content.
Brand strategy. Consistency and recognizability in brand messaging.

Research shows that when ChatGPT makes recommendations, it also takes into account how a company is discussed online. Think of coverage in the news, on social media, and on forums. Is the tone mostly positive, or mostly negative? (First Page Sage, 2025).

This makes it clear that GEO visibility is not only about your website, but also about how people experience and talk about your brand.

For organizations, this means the question “how do we become visible in AI answers?” cannot be separated from the question “how do we build authority and trust in our field?” The two are inseparable.

The shift toward generative search is not a complete reset of everything organizations know about digital marketing. It is an expansion of the playing field, where existing principles around quality, authority, and relevance are applied in a new context (One400, 2026).

GEO (Generative Engine Optimization)

Table of content

GEO in a nutshell

What does GEO mean

What GEO means in the AI search landscape.

The difference from traditional SEO

The role of answer generation in modern search systems.

How generative search engines process information

The position of brands and organizations in AI-generated answers

Visibility versus discoverability

The changing relationship between source and end user

The relationship between content structure and AI interpretation

Several characteristics influence how interpretable content is:

Geographic context and language in AI search results

How AI weighs local and international sources.

GEO as part of a broader digital strategy

Bronnen

Table of content

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Lisanne Groot

Dave Kwakman

Jeffrey Hakvoort

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Frequently Asked Questions

Does GEO affect measurability and reporting?

Can you apply GEO without restructuring your entire website?

Is GEO only relevant for large or well-known brands?

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