GEO — Generative Engine Optimization — is the new discipline that optimizes companies’ visibility on AI response engines (ChatGPT, Perplexity, Gemini). Like SEO…
A new entry point into the customer journey
Since 2023, a structural change has taken place in information search behavior. A growing fraction of users – professionals and individuals alike – no longer enter queries into Google. She asks direct questions to conversational artificial intelligence systems: ChatGPT, Perplexity, Gemini, Claude.
The difference with the classic search engine is fundamental. Google returns a list of ranked links. AI response engines generate a direct recommendation, formulated in natural language, citing two or three sources — and ignoring all others.
This change creates a new market: that of visibility on AI response engines. A market which did not exist three years ago, and which is today almost virgin.
What SEO has built in ten years, GEO must rebuild
SEO — Search Engine Optimization — is a mature industry today. Thousands of agencies, tools worth several hundred million euros, dedicated teams in each company of significant size.
However, in 2003, SEO was only practiced by a handful of specialists. Companies that invested early have built lasting competitive advantages. Those who waited had to pay much more to catch up.
Generative Engine Optimization follows the same trajectory — but at an accelerated speed. The reasons are structural: the adoption of conversational AI is much faster than that of search engines. ChatGPT reached 100 million users in two months. It took Google several years.
A technical infrastructure to be rebuilt from scratch
GEO is not an extension of SEO. It is a distinct discipline, with its own technical levers.
AI response engines select their sources according to radically different criteria than Google. PageRank, backlinks, loading times — these classic SEO indicators have little influence on AI citeability. What matters is the semantic structure of the data, the consistency of mentions across third-party sources, and the ability of the content to precisely respond to a search intent formulated in natural language.
Concretely, three levels of intervention define a GEO strategy:
The first is technical. The crawlers of large AI platforms — GPTBot for OpenAI, ClaudeBot for Anthropic, PerplexityBot — are blocked by default on the majority of websites. This situation results from configurations inherited from classic SEO, which did not distinguish search engine robots from new AI indexing agents. Without explicit unblocking, a site’s content cannot be read or cited by these systems.
The second is semantic. LLMs seek to identify structured entities. The implementation of adapted Schema.org schemas — LegalService for law firms, LocalBusiness for businesses, Organization for businesses — allows the models to precisely associate an entity with an area of expertise and a geographic area.
The third is editorial. AI-quotable content has a specific intent, comes from a source with measurable authority signals, and is structured so that it can be extracted and rephrased. This is what academic research calls the EEAT principle — Experience, Expertise, Authoritativeness, Trustworthiness — applied no longer to Google algorithms, but to LLM selection mechanisms.
A market in formation, positions still available
Data from the Seenby Observatory — covering several hundred players audited in the French legal sector — illustrate the extent of the accumulated delay. 82% of Parisian law firms are today absent from the responses generated by the main AI platforms on requests directly linked to their activity. Zero firms out of ten have explicitly authorized GPTBot in their robots.txt file.
These figures are not specific to the legal sector. They reflect the general state of a market which has not yet become aware of the paradigm shift.
This structural delay is precisely what creates the opportunity. In a mature SEO market, gaining visibility on a competitive keyword takes years and is expensive. In the GEO, the positions have not yet been taken. Players who invest today are building a first-mover authority that their competitors will have to pay much more to acquire tomorrow.
AI Reputation: the forgotten dimension of AI visibility
Visibility is only half the problem. The other half — less obvious, potentially more critical — is what we can call AI Reputation.
Appearing in an AI response is not enough if the content generated is inaccurate, ambiguous or unfavorable. LLMs can confuse namesake entities, aggregate negative signals present in their training data, or simply describe a company too vaguely to be credible. This phenomenon affects 67% of organizations with a minimal AI presence, according to data from the Seenby Observatory.
AI Reputation management constitutes a new market segment in its own right, distinct from classic e-reputation. It cannot be managed with responses to Google reviews. It requires intervention on the sources that AI consults to construct their representation of an entity — specialized directories, reference publications, consistency of structured data.
A limited window for action
The history of SEO teaches one thing: visibility markets crystallize quickly. Once positions are taken and algorithms have incorporated the authority signals of established players, the cost of entry for newcomers explodes.
In GEO, this crystallization has not yet taken place. The models continue to evolve, the selection criteria are not yet fixed, and the majority of economic players have not yet initiated a structured AI visibility strategy.
The window is open. It won’t stay that way indefinitely.
