GEO SEO: What Generative Engine Optimization is and how it works

1. GEO in numbers: Status of the 2026 study in collaboration with SISTRIX

The following figures are from the 2026 LEAP/SISTRIX study and provide a snapshot of German e-commerce.

2. GEO vs. SEO: What distinguishes GEO from traditional SEO

The term Generative Engine Optimization comes from a research paper by Princeton University, published in 2023 with contributions from Georgia Tech and the Allen Institute for AI (arXiv). The core idea: Generative search engines don't just list pages; they synthesize information from multiple sources into a new answer. This changes the target metric. Classic SEO optimizes for ranking and clicks. GEO optimizes for being mentioned and cited in generated answers.

The terms GEO, AEO, and LLMO are not used consistently in the industry. For this article, we distinguish them as follows:

• GEO stands for optimizing visibility and citation in generative AI answers, such as in AI Overviews, AI Mode, ChatGPT, or Perplexity.
• AEO (Answer Engine Optimization) describes aligning content for direct answers, for example in featured snippets or answer boxes.
• LLMO (Large Language Model Optimization, also called LLM SEO) focuses on the language models themselves, specifically how brands and content are recognized and reproduced by these models.

In practice, all three overlap significantly.

Google doesn't separate these terms either. In May 2026, Google adopted both AEO and GEO in its documentation for website operators as terms for optimizing visibility in AI search features. From Google's perspective, anyone optimizing for generative search is optimizing the search experience, and that is still "just SEO." Google therefore treats GEO as SEO for a new way of presenting search results (Google Search Central).

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3. The participation gap: being relevant and being mentioned are two different things

The fact that an AI response appears doesn't mean your brand is included in it. These are two separate events, and between them lies the participation gap.

A strong organic presence seems to be an important prerequisite for being considered as a source. However, it does not automatically determine whether a site is actually selected. Our study therefore measures both separately: how often an AI Overview appears for a domain's keywords and how often the domain is cited as a source within them. Those who only look at classic visibility miss this gap. It only becomes apparent when you also track whether your brand actually appears in the responses.

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Where AI strikes and where it doesn't: Research vs. Purchase

AI responses currently appear primarily for queries related to research and comparison. This is the phase where users first discover a brand or weigh options against each other: How do two product types differ? What should I look for when buying? For searches with clear purchase intent—when someone already knows what they want and is specifically looking for a product or price—classic search remains the standard for now.

This seems less harmful than it actually is. The loss isn't immediately reflected in revenue. Companies that no longer appear during the research phase don't make it onto the customer's shortlist and won't be searched for specifically later on.

This likely won't remain the case. According to a McKinseysurvey published in March 2026 in Germany, France, and the UK, 63 percent of respondents use AI to compare brands, models, prices, and reviews. However, most still want to make the final purchase decision themselves. Nevertheless, it is foreseeable that the line between research and purchase will continue to blur. In agentic commerce, AI agents compare and purchase on behalf of users. We explain how this works and what companies need to prepare for it on our page about Agentic Commerce.

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4. What this means for content work: two answers instead of one

Informational content—such as guides, comparisons, and buying advice for users in the research phase—must fulfill two roles simultaneously in the future:

1. It must continue to rankso that users who still want to search and compare for themselves will click.
2. It must be citablemeaning it must be factually supported, backed by recognizable expertise, and anchored on a domain that conveys credibility and trustworthiness. This allows an AI to use it as a source component, even if no click occurs.

Transactional pages—the product and category pages where the actual purchase takes place—must continue to be secured with traditional SEO (which we consider part of our Search Intelligence) as long as this area of search still functions primarily in the traditional way. In the research phase, citability is what counts; in the purchasing phase, it is about defending your position. Both rely on the same foundation.

Furthermore, every AI search engine selects its sources based on its own logic. Being visible in one system does not automatically mean you are visible in the next. This is a strong argument for building a robust organic foundation that all systems can draw from, rather than optimizing for a single one.

There is also an opportunity here: companies that start building citability now will gain a competitive advantage over rivals who are still only focused on rankings.

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5. Why a strong organic base remains the foundation

The study shows a clear correlation: domains with high organic visibility are also cited significantly more often in AI answers. A strong organic foundation appears to be a prerequisite for being among the sources an AI selects from. This aligns with Google’s own description that AI features are built upon the existing ranking and quality systems of Search.

Whether and to what extent a lack of citability causes visibility losses cannot be proven from this alone. It is merely striking that industries with similarly frequent AI Overviews are developing differently. Human medicine and books are both among the most affected sectors, yet one maintained its organic visibility while the other lost the most. One possible explanation is that content suitable as a source component is more common in some industries than in others. This is not proven. To see where your company stands compared to your industry, check out our free Growth Maturity Check.

GEO builds on SEO. Learn how to build a solid GEO foundation on our page about GEO and LLMO optimization or in our webinar recording “16.7 percent less visibility on Google. What this means for your online shop.”, including implementation tips via email.

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6. Frequently asked questions about GEO

What is the difference between GEO, AEO, and LLMO, and what is SEO for AI?

These three terms are not used consistently and overlap significantly. In this article, GEO (Generative Engine Optimization) refers to optimizing visibility and citations in generative AI responses, AEO (Answer Engine Optimization) refers to targeting direct answers like featured snippets, and LLMO (Large Language Model Optimization) refers to focusing on the language models themselves. From Google's perspective, optimizing for generative AI search is still SEO. "SEO for AI" is also used, usually meaning the same thing. At LEAP, we group everything—including traditional SEO—under the term Search Intelligence.

Do you need a separate tool or special tactics for GEO?

Not necessarily for Google. Google clarifies that things like dedicated AI text files or special schema for their search are not required.

For measurement, the Generative AI reports have been available worldwide in Google Search Console since August 31, 2026. They show how often your site's URLs appear in AI features like AI Overviews and AI Mode, broken down by page, country, device, and date. The report provides impressions but no click or search query data, and it may not be displayed at all if there are too few AI impressions.

For ChatGPT, Perplexity, and other systems, supplementary, cross-system monitoring remains useful, either manually via your most important search terms or using a tool.

How do you measure citability?

By combining traditional visibility with the frequency of AI responses for your topics and how often you are cited as a source within them. Both should be monitored across multiple AI systems, as they differ in their source selection. For Google, Search Console now provides a baseline of data, while other systems require their own monitoring. It is more meaningful to track your own progress over time than to compare raw citation volumes between different systems.