GEO 2026: why we need to stop optimizing “for AI” and start optimizing engine by engine

Generative Engine Optimization, or GEO, is often presented as the new SEO: produce clearer, more sourced, more structured content, in order to be cited by ChatGPT, Perplexity, Google AI Overviews, Gemini or Claude.

True, but incomplete.

The real breakthrough of 2026 isn’t just that AI engines respond instead of web pages. It’s that they don’t all search for the same thing, don’t reformulate queries in the same way, don’t cite the same sources and don’t react in the same way depending on the language of the prompt.

In other words: there’s no such thing as a universal GEO strategy. There are GEO strategies by engine, by language and by search intent.

The problem: many brands still treat GEO as enriched SEO

Most current GEO guides repeat three useful but generic tips: produce expert content, add reliable sources, structure your pages with clear answers. Semrush, for example, defines AI search optimization as the practice of making content frequently referenced and featured by systems such as ChatGPT, Google AI Overviews and Perplexity. (Semrush)

These recommendations are necessary. But they are no longer enough.

Why? Because an AI engine doesn’t always simply read the user’s prompt and search the web for that exact phrase. It can reformulate, chop up, enrich or hijack the initial query before going on to retrieve sources.

Profound tracked 10,000 prompts on ChatGPT, Copilot and Perplexity for 14 days and observed clear differences in search behavior: the engines don’t launch the same searches from the same user question. (Profound) Its Research Hub even sums up the problem bluntly: ChatGPT shares only around 13% lexical overlap with the original prompt, and almost never launches the same query twice.

This is a major change for SEO, content and brand teams. Optimizing a page solely around the keyword typed by the user becomes insufficient when AI transforms this keyword into several intermediate queries.

What AI engines “see” is not necessarily what the user writes

In Google, classic SEO was based on a relatively stable principle: a typed query, a results page, ranking signals.

In AI search, the path is more fragmented:

1. the user asks a question ;
2. the motor interprets the intention;
3. it can generate several internal requests;
4. it retrieves documents ;
5. it synthesizes an answer;
6. he may choose a few visible quotations.

The decisive point is between stage 2 and stage 4. This is where the engine decides what it’s really looking for.

Perplexity, for example, is often closer to a source-oriented answer engine, with a visible, citation-based search logic. ChatGPT, according to Profound’s observations, seems to rewrite and broaden the initial query. This is not a cosmetic difference: it changes the content likely to be cited.

A brand that only optimizes the “best small business CRM software” page may miss out on intermediate queries such as :

• “CRM comparison for small business sales teams
• “best pipeline management tools for B2B startups”.
• “HubSpot alternatives for small sales teams”.
• “CRM pricing and implementation time comparison

Winning content isn’t just content that repeats the right keyword. It’s the one that covers the ecosystem of reformulations that an AI engine is likely to generate.

ChatGPT, Perplexity and Google AI Overviews don’t quote the same web

The second common mistake is to believe that a citation obtained on one AI engine automatically means visibility on the others.

Profound has already shown, for example, in an analysis of 100,000 prompts comparing ChatGPT and Perplexity, that the two models largely cited different sources: only 11% of the domains cited appeared in both environments, meaning that almost 89% of citations came from different corners of the web depending on the model queried.

This is probably one of the most important statistics for understanding GEO.

It means that a brand can be highly visible in ChatGPT and almost absent in Perplexity. Or it may be cited by Google AI Overviews on certain informational queries, but never by ChatGPT on comparative queries. Google position tracking doesn’t show this fragmentation. Nor does organic traffic, as AI responses can create awareness without immediate clicks.

GEO must therefore become a multi-surface measurement discipline. It is no longer enough to ask: “Are we well ranked?” We must ask:

• Are we quoted?
• By which engine?
• On what intentions?
• In what language?
• With which competitors?
• From what types of sources?
• Are we mentioned without a link, quoted with a link, or absent?

Language changes quotes too

Third underestimated angle: the language of the prompt.

Profound analyzed 3.25 billion citations across 7 models and 14 countries. Its conclusion is clear: the language of the query strongly modifies the sources cited, especially social citations, and Google AI Overviews and ChatGPT react differently to non-English-speaking prompts.

This is a strategic point for international brands.

Many companies still translate their content as if multilingualism were a simple localization problem. In GEO, this is not enough. A question asked in French, German or Spanish can trigger a different corpus of sources, a different weighting of local sites, and an increased or reduced presence of forums, media, directories, comparators or community content.

This means that a B2B brand that dominates AI citations in English may remain invisible in French. Conversely, a local player may be cited in Google AI Overviews France without existing in ChatGPT in English.

The international GEO strategy should not be “translate English pages”. It must be “rebuilding citation authority in each language”.

GEO becomes a proof architecture discipline

In traditional SEO, many teams have learned to produce long, complete pages optimized around a cluster of keywords. In GEO, the focus shifts to extractability.

An AI engine must be able to quickly extract a reliable, contextualized and attributable assertion. This favors certain formats:

• short definitions ;
• explicit comparisons ;
• criteria tables ;
• transparent methodologies ;
• dated figures ;
• Targeted FAQs;
• credible author pages ;
• external sources ;
• customer evidence ;
• specific use cases ;
• updated content.

It’s not just a question of HTML structure. It’s a question of “recoverable proof”.

High-performance GEO content should help the engine to answer micro-questions such as :

• Which is the best choice for this use case?
• Which brand specializes in this segment?
• Which solution is the most reliable for this industry?
• What sources confirm this assertion?
• Is this information recent?
• Does this site have an independent authority or is it just self-declared?

This is why artificial “best-of” content or pages created solely to manipulate AI responses are becoming risky. In fact, Google has just updated its anti-spam policy to explicitly include attempts to manipulate generative responses in Search, including AI Overviews and AI Mode.

The signal is important: sustainable GEO cannot be based on manipulation. It must be based on verifiability.

A good GEO strategy must be segmented by engine

The practical consequence is simple: we need to stop building a single “AI search” roadmap.

A serious strategy should distinguish at least three logics.

1. For ChatGPT: optimize the semantic universe, not just the query

If ChatGPT strongly reformulates queries, the challenge is to cover neighboring intentions. This means working on pillar pages, comparisons, background content, decision guides and third-party evidence.

The aim is not just to appear on “best tool X”. It’s to be present in the alternative formulations that the model might generate when it interprets the request.

2. For Perplexity: optimize direct quotation and freshness

Perplexity works more like a citation response engine. Up-to-date, clearly sourced, easily quotable content aligned with a precise query therefore has an advantage.

Here, content must be more “retrieval-ready”: explicit titles, short sections, visible figures, update dates, clean comparisons, accessible and unambiguous pages.

3. For Google AI Overviews: combining classic SEO, authority and synthetic response

Google AI Overviews remains strongly linked to the Google Search ecosystem. A GEO strategy for Google cannot therefore ignore traditional SEO: indexability, domain authority, internal linking, E-E-A-T, structured data, editorial quality and anti-spam compliance.

But we need to add another layer: formatting content so that it can be synthesized into a short answer.

Platform	Search behavior	Loyalty to initial prompt	Use of social sources	What this means for GEO
ChatGPT	Strongly rewrites internal queries, explores adjacent formulations and acts as a searcher rather than a literal engine	Low lexical fidelity	Low to moderate, with heavy reliance on Reddit in social citations	Work with a broad semantic field, varied natural formulations, and pages capable of responding to multiple reformulations of the same intent
Perplexity	Remains as close as possible to the operation of a classic search engine	Strong loyalty to prompt	Higher than ChatGPT, but less aggressive than Google AI Overviews	Focus on very explicit coverage of queries, titles close to expected wording and clear answer-oriented structure
Microsoft Copilot	Compresses prompts into shorter, more standardized requests	Intermediate fidelity	Low	Optimize clear, compact, well-marked content, with easy-to-extract entities and attributes
Google AI Overviews	Surface very aggressive on social quotes	Not documented here on fanout	Very high	Think GEO by market and language, with a real strategy for presence on social platforms according to target zone
Google AI Mode	AI-enhanced Google environment, best seen here from a citation angle	Not documented here on query rewriting	Very high	Reinforce classic SEO signals, while adapting the strategy of proof, freshness and external presence by language
Gemini	Less documented here on fanout, with a lower relative share of social citations	Not documented here	Low	Don’t over-invest in social platforms alone; keep a solid base of reliable, clear and extractable content
Claude	Little documented here on search mechanics, but present in citation comparisons	Not documented here	Weak	Promote clarity, credibility of evidence and clean editorial structure

 

The real GEO KPI: response share, not just citations

The market is beginning to structure itself around tools, agencies and tracking platforms. First Page Sage, for example, has published a ranking of GEO 2026 agencies, a sign that the discipline is becoming a market in its own right.

But the central KPI shouldn’t just be “number of citations”. A quotation can be marginal, negative, drowned out by ten competitors or placed with little commercial intent.

The right indicator is response share: the frequency and quality with which a brand is included in responses that are useful to its business.

This involves following :

• strategic prompts ;
• engine-generated reformulations ;
• visible quotes ;
• mentions without link ;
• competitors present ;
• the tone of the response;
• language ;
• the country ;
• the freshness of springs;
• the presence of third-party content that validates the brand.

To put it plainly: GEO is not just about content. It’s about algorithmic reputation.

What to do now

Brands should start with simple mapping.

First, identify the 50 to 100 questions their prospects would actually ask ChatGPT, Perplexity or Google AI Overviews. Not just SEO keywords, but conversational questions: “which tool to choose”, “which agency is reliable”, “which alternative to”, “how much does it cost”, “what are the risks”, “best for an industrial SME”, and so on.

Then test these questions by search engine and language. The aim is to see where the brand appears, where competitors appear, and which sources are cited.

Then build three types of content:

1. proprietary content, on the brand’s website, designed to be extracted ;
2. third-party content, via press, partners, serious directories, comparisons, studies and communities;
3. localized content, truly adapted to target languages and markets.

Finally, measure over time. AI responses are volatile. Quotes change. Models evolve. Indexes move. A GEO strategy cannot be a static quarterly audit; it must become a continuous monitoring system.

Conclusion: GEO isn’t “the new SEO”, it’s the new layer of brand visibility.

SEO was about gaining a position on a results page.

GEO is looking to win a place in an answer.

This difference changes everything. In a results page, the user compares links. In an AI response, the engine pre-compares for him. To be absent from this synthesis is sometimes to be absent from the decision.

So the big lesson of 2026 is this: you can’t just optimize for keywords. We need to optimize for engines that reformulate, select, quote and synthesize differently depending on the context.

Brands that understand this fragmentation will have an advantage. They will no longer ask, “How can I be visible in AI?”

They will ask: “How can I be quoted by the right engine, in the right language, on the right intention, with the right proof?”