GEO vs. SEO vs. AEO: Differences and Commonalities

Three acronyms compete for the attention of B2B marketing leaders: SEO, AEO, and GEO. They sound like three separate disciplines but describe the same fundamental problem from three perspectives. How does a company become visible when people search for answers? This article clarifies the definitions, shows the common technical foundation, explains the different citation mechanisms, and assesses which levers truly matter in the DACH region and specifically in Austria.

Why the distinction between SEO, AEO, and GEO is relevant

Search behavior is measurably shifting. In Germany, half of internet users now use an AI chat at least occasionally instead of classic internet search, as a survey by Bitkom Research shows (DACH survey, Germany). This figure is the most important evidence for the German-speaking region that AI search is no longer a US niche phenomenon.

At the same time, click behavior in classic search is changing. A US analysis by Pew Research documents that users with an AI overview click on a classic search result in only 8 percent of visits, compared to 15 percent without an AI overview (international survey, USA). On the link within the AI answer, only 1 percent of users click (international survey, USA).

This creates a dual task for B2B companies. They must remain present in classic rankings while simultaneously being cited in AI answers. Those who clearly distinguish the three disciplines avoid two costly mistakes: dismissing GEO as mere buzzword or prematurely declaring classic search dead.

Definitions: What distinguishes SEO, AEO, and GEO

The three terms describe optimization for three different output formats of the same search engines.

• SEO (Search Engine Optimization): Optimization for classic search engine rankings, i.e., the organic blue links on the search results page. The goal is the highest possible position that brings qualified traffic to one's own website.
• AEO (Answer Engine Optimization): Optimization for direct answers, such as in Featured Snippets, the Knowledge Panel, or voice search. Here, what counts is not the position in a list but the one precise answer that the search engine displays directly.
• GEO (Generative Engine Optimization): Optimization for citations in AI-generated syntheses. Systems like ChatGPT, Perplexity, Google AI Overviews, or Gemini combine multiple sources into one answer. GEO aims to appear as a cited source in this synthesis.

In practice, additional acronyms circulate as synonyms or subsets. LLMO (Large Language Model Optimization) and AI Optimization essentially mean the same as GEO: visibility in AI systems. SISTRIX founder Johannes Beus calls this acronym proliferation a deceptive package with primarily sales-driven motives. Google Search Liaison Danny Sullivan formulates the counterposition succinctly: Good SEO is good GEO. Both statements are compatible. GEO describes a real shift in output format but does not require an entirely new methodology.

Commonalities: The technical foundation remains identical

The most important and most frequently overlooked point: GEO does not replace SEO but builds upon it. The foundations are the same for all three disciplines.

• Crawlability: What a search engine cannot crawl, an AI system cannot cite either. A technically sound, accessible website is the prerequisite for any form of visibility.
• Structured data and schema: Schema markup helps both classic search engines and AI systems correctly classify content. Machine-readable markup is an advantage in all three disciplines.
• E-E-A-T: Experience, Expertise, Authoritativeness, and Trustworthiness are the quality characteristics by which good content is measured. AI systems prefer trustworthy sources for similar reasons as classic rankings.
• High-quality content: Content that answers a question precisely and completely works in classic search, as a direct answer, and as a cited AI source.

Google itself confirms this continuity. According to Search Engine Land, Google employee Gary Illyes stated that normal SEO is sufficient for appearing in AI Overviews and no separate GEO or LLMO is necessary (international source). Anyone with a solid SEO foundation does not start at zero with GEO.

Differences in citation mechanism

As similar as the foundation is, the mechanisms through which visibility emerges are quite different. This is the actual core of the distinction.

• Classic SEO (Blue Links): The search engine lists ten or more results; the user chooses. Relevance, backlinks, technical signals, and user behavior count. Traffic flows directly to the website.
• AEO (Featured Snippets and Voice): The search engine extracts a single answer from one source and displays it prominently. What counts is whether content is precise, clearly structured, and directly formulated as an answer.
• GEO (synthesized AI answers): The AI system processes multiple sources and generates a new, summarized answer with source citations. What counts is whether a brand or content is present, clearly formulated, and trustworthy in the consulted sources.

The decisive difference lies in the click. In classic search, the click is the goal. In AI answers, it often becomes unnecessary because the answer is already in the interface. This zero-click development is not new but is accelerating. Already in 2024, according to a SparkToro analysis, 59.7 percent of EU Google searches and 58.5 percent of US searches ended without a click (international survey, EU and USA). AI Overviews amplify this effect. An Ahrefs analysis of 300,000 keywords shows that the presence of an AI Overview correlates with a 58 percent lower click-through rate for the top-ranked result (international survey).

GEO-specific optimization levers from practice

Precisely because the mechanism is different, GEO develops its own levers that go beyond classic SEO. The scientific foundation is provided by the GEO study by Aggarwal et al., which shows that GEO methods can increase a source's visibility in generative answers by up to 40 percent (international research). From study and practice, the following levers have become established.

• Front-loading and answer islands: The core answer belongs at the beginning of a section. AI systems preferentially extract clearly delineated, self-contained answer blocks. A paragraph that directly answers the question is more likely to be cited than an answer that only emerges from context.
• Statistics and citations in content: Documented figures and verifiable sources increase the likelihood of being cited. AI systems prefer statements that can be verified.
• Source citations and authority: Those who cite cleanly themselves and document their statements signal trustworthiness. This affects AI systems similarly to human readers.
• Schema for AI: Structured data makes content machine-readable and facilitates correct classification in a synthesis.

A separate chapter is warranted for llms.txt, a proposed file intended to signal to AI crawlers which content they may use. A reality check is needed here. Google does not support llms.txt and does not plan to according to Gary Illyes, as Search Engine Land reports (international source). Anyone implementing llms.txt should understand it as an optional signal for individual systems, not as a reliable lever for Google.

Brand mentions instead of backlinks: The changed off-site signal

Perhaps the biggest strategic difference concerns off-site signals. In classic SEO, backlinks are the central currency. For AI visibility, the weight shifts toward brand mentions, including unlinked ones.

An Ahrefs study of 75,000 brands provides clear correlation data. According to it, web mentions of a brand correlate at 0.664 significantly more strongly with visibility in AI Overviews than backlinks at 0.218 (international survey, Spearman correlation). The three strongest factors are all off-site signals: web mentions at 0.664, branded anchor texts at 0.527, and brand search volume at 0.392 (international survey).

For practice, this means a shift in priorities. Instead of focusing exclusively on link building, reviews, expert articles, industry directories, and consistent brand mentions gain importance. A brand that is frequently and consistently mentioned in the relevant environment has better chances of appearing in AI answers. Important: correlation is not causation. The data show a strong relationship but do not prove a direct ranking factor.

Target systems overview

GEO is not a uniform discipline but addresses multiple systems with different reach and functionality.

• Google AI Overviews: The AI summary directly in Google Search reaches, according to Google, 2 billion monthly users (international source, USA). Here, classic SEO as a foundation counts particularly strongly.
• Google AI Mode: Google's dialogue-oriented search entry reaches over 100 million monthly active users (international source, USA and India) and is initially available in the USA and India.
• ChatGPT: OpenAI's system reaches, according to Sam Altman, 800 million weekly users (international source). With the search function, ChatGPT becomes a serious search entry point.
• Perplexity, Gemini, and Claude: These systems cite sources with varying transparency. Perplexity displays sources prominently, Gemini is closely integrated with the Google ecosystem, Claude is frequently used in professional contexts.

Optimization differs by system gradually, not fundamentally. Those who focus on clear structure, documented statements, and strong brand signals cover the essential requirements of all systems.

Business value: Why AI referral traffic is above-average valuable

A common objection is: AI traffic is still too small to be worthwhile. The volume is indeed still limited, but the value per visitor is above average.

A Semrush study based on over 500 topics in digital marketing shows that an AI search visitor is 4.4 times as valuable as a classic organic search visitor measured by conversion rate (international survey). The same study predicts that AI search could overtake classic search for these topics by early 2028 (international survey).

Growth is also substantial. An Adobe Analytics analysis for US retail documents a 1,200 percent increase in traffic from generative AI sources between July 2024 and February 2025 (international survey, USA). These visitors also behave more valuably: according to Adobe, they show 8 percent higher engagement, 12 percent more page views per visit, and a 23 percent lower bounce rate (international survey, USA). For B2B lead generation, this means: fewer but more purchase-ready visitors can deliver a higher contribution than a larger, less qualified volume.

Measurement and KPIs: From ranking to share of voice

The different mechanisms require different metrics. Those who measure GEO with classic SEO metrics see only part of the picture.

• Classic SEO KPIs: Keyword rankings, organic traffic, click-through rate, and conversions from organic traffic remain the foundation. Data sources are Google Search Console and web analytics.
• GEO KPIs: Share of voice in AI answers, i.e., how often a brand is mentioned in relevant syntheses. Added to this are citation and mention tracking as well as analysis of AI referral traffic and its conversion.

The tool landscape is evolving rapidly. Providers like Ahrefs with Brand Radar or Semrush with AI visibility functions capture brand mentions and visibility in AI systems. It is important to cleanly segment AI referral traffic in web analytics, for example by tracking references from AI sources as a separate channel. Only this way can the business contribution be proven rather than assumed.

Common mistakes with GEO, AEO, and SEO

In the transition to AI search, certain mistakes recur that cost visibility and budget.

• Understanding GEO as a replacement for SEO: Those who neglect the technical foundation because they rely on AI remove the basis for GEO. Without crawlability, no citation.
• Using manipulative GEO tactics: Attempts to manipulate AI systems with hidden instructions or artificial content are risky. Google consistently penalizes manipulative practices, and AI systems are developing similar defense mechanisms.
• Relying entirely on llms.txt: A file that the dominant search provider ignores does not replace a strategy.
• Not measuring AI visibility: Without share-of-voice and citation tracking, success remains invisible and unmanageable.
• Abandoning classic search: It remains the largest channel. AI answers predominantly access content that is already well optimized.

DACH and Austria perspective

For the German-speaking B2B market, two factors are decisive: market maturity and language.

Market maturity is established. In Austria, according to DataReportal, there are 8.69 million internet users with a penetration of 95.3 percent (Austria survey, January 2025). The high penetration means that virtually the entire relevant target group is reachable. Combined with the Bitkom finding that half of internet users already use AI chats (DACH survey, Germany), the German-speaking market is ready for GEO.

The language factor is its own lever. AI systems respond in the language of the query and preferentially draw on linguistically matching sources. German-language, technically precise content with clear DACH relevance thus has a structural advantage for German-language prompts. For local B2B GEO strategies, this means: consistently publish in German, clearly name regional entities like locations and industry references, and build brand mentions in the German-speaking professional environment.

Further reading: On the path to Search Everywhere

The medium-term trend does not lead to three separate disciplines but to an integrated strategy. Machine-readable websites become standard equipment because AI agents become a new user group. In agentic commerce, software agents research and act on behalf of humans. They read structured data, compare offers, and make pre-selections.

For companies, SEO, AEO, and GEO thus merge into a common task: to be present and citable everywhere answers are generated. Sensible next steps are a technical inventory of crawlability, expansion of structured data, enrichment of content with documented statistics and clear answer blocks, and establishment of mention tracking. The common foundation remains: high-quality, trustworthy content that answers a question better than others.