Which AI Systems Can Be Optimized?
The digital landscape is changing: Generative AI applications such as ChatGPT, Claude, and Perplexity could soon take significant market share away from traditional search engines. For companies, this raises a crucial question: How can we remain visible when users no longer obtain information solely through search engines, but directly from AI-powered answer engines?
The terms Large Language Model Optimization (LLMO), Generative Engine Optimization (GEO), and Answer Engine Optimization (AEO) address precisely this challenge—they describe the logical evolution of SEO in the age of generative AI. LLMO refers to optimization for large language models in general, GEO focuses on optimization for generative AI search systems, and AEO describes targeted optimization for direct answers, such as those provided in AI Overviews or Featured Snippets. In practice, the three disciplines overlap by approximately 90%: The levers are largely identical, while the difference lies in the final format of the answer.
Before discussing the relationship between SEO and AI visibility, we need to clarify a fundamental distinction, as not every AI system can be influenced to the same extent.
From a technical perspective, AI systems operate in two very different ways:
Traditional large language models (LLMs) without web access generate answers exclusively from their training data. Their knowledge is “frozen” at a fixed knowledge cutoff. They do not actively access the web, but instead generate answers based on the statistical patterns they learned during training. Examples include ChatGPT in its traditional mode without web search enabled or Claude without tool use. Traditional SEO measures do not have a short-term impact here—the content would already have needed to be part of the dataset during an earlier training phase for the model to reproduce it.
RAG systems with grounding (Retrieval-Augmented Generation), by contrast, combine a language model with live web search. They break the user’s query down into multiple subqueries, retrieve current web content, and use it as the basis for the generated answer. This category includes Google AI Overviews, Google Gemini with Search, ChatGPT Search, Perplexity, and Microsoft Copilot. These systems can be actively influenced through SEO measures because they access the same content that is indexed by traditional search engines.
This distinction is crucial: All statements regarding the relationship between SEO and AI visibility refer to RAG systems with grounding functionality. With pure LLMs that do not have web access, influence occurs through a different and significantly slower channel—namely, whether and how your content and brand are incorporated into the dataset during the next round of model training. This lever works through brand awareness, consistent mentions on authoritative platforms, and a strong topical presence across the web—not through short-term SEO measures.
The good news is that practically all AI systems currently gaining relevant market share now use grounding. Google AI Overviews and AI Mode are retrieval-based, Perplexity has been designed as retrieval-first from the outset, and ChatGPT automatically activates web search for current or specific questions. This means that the most relevant lever for AI visibility today is, in fact, precisely what traditional SEO has always addressed.
The Key Finding: Grounding-Based AI Systems Preferentially Cite Content That Is Already Visible in Search Engines
For RAG systems with grounding, the question “Is AI visibility related to traditional SEO visibility?” can be answered empirically—and the answer is yes. The correlation varies by platform, but it is measurable:
Google AI Overviews: An Ahrefs analysis of 1.9 million AIO citations found that 76% of the cited pages ranked in Google’s top 10. Over a period of 16 months, BrightEdge documented that the citation overlap between AIO sources and organic rankings increased from 32% to 54%—and even reached 68–75% in YMYL sectors such as healthcare, insurance, and education.
Perplexity: According to Search Engine Land, 60% of Perplexity citations overlap with Google’s top 10 results. In the healthcare sector, the figure is as high as 82%.
ChatGPT with web search enabled: An SE Ranking analysis of 129,000 domains and 216,524 pages identified the number of referring domains as the strongest individual factor. Sites with up to 2,500 referring domains receive an average of 1.6 to 1.8 ChatGPT citations, while sites with more than 350,000 referring domains receive 8.4 citations. When browsing mode is active, 87% of the sources overlap with Bing’s top 10 results.
The logic behind this is simple: A RAG system performs a search query in the background, evaluates the results, and uses them as the basis for the generated answer. Whether the search is initiated by a person or an AI, the mechanisms used to select sources are based on the same fundamental principles: relevance, authority, trust, and clear structure. A strong presence in search engines measurably increases the likelihood of being cited in the AI-generated answers of these grounding-based systems.
However, the Weighting of SEO Disciplines Is Shifting
This is where the picture becomes more nuanced. While the relationship between SEO visibility and AI citations is established, the relative importance of individual levers within the traditional SEO stack is shifting. Three observations are central:
1. The Overlap Between Top 10 Rankings and AIO Citations Is Declining
A follow-up Ahrefs study (863,000 keywords, 4 million AIO URLs, February 2026) shows that the share of AIO citations from Google’s top 10 fell from 76% (July 2025) to 38% (February 2026). Google is increasingly incorporating sources that do not rank on page 1—an effect of so-called query fan-out, in which a single user query is broken down into multiple semantically related subqueries. The consequence: A pure top 10 strategy focused on one primary keyword is no longer sufficient. Topical breadth and depth are becoming more important.
2. Brand Mentions and Digital Authority Outperform Pure Backlink Metrics
Ahrefs analyzed 75,000 brands and found that brand mentions correlate with AIO visibility at a Spearman coefficient of 0.664, while traditional backlinks correlate at only 0.218. Brand mentions are therefore about three times as predictive as pure link metrics. An expanded study from December 2025 confirms this pattern across all three major AI platforms: Branded web mentions score 0.664 for AI Overviews, 0.709 for Google AI Mode, and 0.656 for ChatGPT. YouTube mentions show the strongest individual correlation of all (~0.737). This does not mean backlinks have become irrelevant—they continue to influence rankings, which in turn create AIO eligibility. However, they are no longer the dominant lever for AI visibility alone. Brand mentions are also the lever that has a long-term effect on pure LLMs without web access—through the training-data pathway.
3. Platforms Work Differently
An Ahrefs analysis of 15,000 prompts shows that only 12% of ChatGPT, Gemini, and Copilot citations overlap with Google’s top 10. For Perplexity, the figure is around 30%. ChatGPT and Claude frequently favor niche expert publications with topical depth (Investopedia, Wired, GitHub, Coursera, Edmunds) over generalist high-DA domains. A one-size-fits-all strategy across all platforms falls short.
The Four Pillars of SEO in the AI Era
Our recommendation as an agency is not to move away from traditional SEO, but to deliberately rebalance the four core disciplines: structure, technology, content, and Digital Authority Management. These four pillars form the basis of our SEO and GEO strategy—and they work exactly as the current body of research suggests.
Structure
It remains the foundation—and has now been demonstrated algorithmically for the first time. The Google API leak of May 2024 confirmed that Google measures a website’s topical focus through specific signals: siteFocusScore (how strongly a site concentrates on a core topic), siteRadius (how far an individual page deviates from the core topic), and siteAuthority (domain authority, which Google had publicly denied for years but which is documented in the code). These signals are calculated using so-called site2vec embeddings, which map a website profile in semantic space.
The practical consequence is clear: A well-designed URL architecture, topical segmentation (siloing), a clear navigation structure, and consistent internal linking enable search engines and AI systems to understand a website’s topical relevance and depth in the first place. In its 2025 ranking factors update, First Page Sage ranks “Niche Expertise”—defined as at least 10 authoritative pages within a shared topic cluster—as the fourth-strongest factor, with a weighting of around 13%. A Graphite study (332 URLs, 12 domains) also shows that content on domains with high topical authority gains visibility 57% faster, is 62% more likely to receive traffic in the first week, and reaches impression milestones 30% faster.
Especially in the context of query fan-out—where AI systems break a user query down into multiple subqueries—topical authority built through connected topic clusters becomes the decisive factor. A website with a clean structural setup does not merely address a single keyword, but an entire semantic field.
Technology
It remains mandatory. Crawlability, indexability, loading speed, and clean sitemap structures are prerequisites for both search engines and AI crawlers (GPTBot, OAI-SearchBot, PerplexityBot, Google-Extended) to process content at all. Structured data via Schema.org is not a direct ranking factor—Google has confirmed this several times—but it supports machine interpretability and therefore the likelihood of being cited in AI Overviews. A Search Engine Land experiment showed that only the page with correctly implemented schema appeared in an AI Overview.
Content
It remains central, but the format is changing. AI systems prefer so-called answer-first structures: The direct answer to a question appears in the first one or two sentences of a section, followed by context and evidence. An analysis of 1.2 million ChatGPT answers by Kevin Indig showed that 44.2% of all LLM citations come from the first 30% of a text—a pattern with a statistically significant p-value of 0.0 (“ski-ramp distribution”). Specific figures, data, and source references measurably increase the probability of citation: The Princeton GEO paper (Aggarwal et al., 2023) demonstrates that integrating statistics increases AI visibility by around 22%, while quotations increase it by as much as 37%. Alongside traditional keyword research, prompt research is becoming more important: What questions do users ask in AI chatbots? Which subqueries arise from them? These insights feed directly into the content strategy.
Digital Authority Management
It is becoming the most important discipline in the AI era—and at the same time, it is the only lever that works through both pathways: through grounding in retrieval-based systems and through training data in pure LLMs. Digital Authority Management encompasses far more than traditional link building. It is about systematically building digital authority through:
- High-quality backlinks from directories, chambers, guest articles, specialist blogs, and forums,
- citations and brand mentions in specialist articles, studies, and relevant industry media—they strengthen trust and E-E-A-T signals,
- linkable assets such as white papers, infographics, tools, or original data that AI systems preferentially use as references,
- a multi-platform presence on Reddit, LinkedIn, YouTube, G2, Capterra, Trustpilot, and specialist publications focused on the relevant topic.
The evidence here is clear: Sites that are present on four or more platforms are mentioned 2.8 times more often in ChatGPT answers. A Stacker study (215 stories, 54,702 AI responses, 8 AI platforms) shows an almost linear relationship between Domain Rating and citation rate: Pickups from publishers with high Domain Authority (DR 81+) are cited in AI answers in 43% of cases, while pickups from publishers with DR 62 are cited in only 2% (correlation r=0.99). Quality outperforms quantity more clearly than ever. A Muck Rack analysis of more than one million AI-cited links also confirms that 82% of all AI citations come from earned media—that is, editorial third-party coverage rather than owned or paid content.
Our Agency Position: An Integrated Strategy Instead of a Buzzword Discipline
In practice, we see two common mistakes:
The first is declaring traditional SEO obsolete and reinventing “GEO” as an isolated discipline with its own secret hacks. This may be easy to sell, but it ignores the established evidence: The correlation between organic visibility and AI citations in retrieval-based systems is real and, in some cases, very strong.
The second mistake is the opposite: continuing to define top 10 rankings for individual keywords as the sole KPI and ignoring the structural shift. The correlation exists, but it is weakening. Anyone who still claims in 2026 that “good SEO is enough” overlooks the demonstrable shift toward brand mentions, topical depth, and multi-platform presence—and also underestimates the share of pure LLM usage that is not served through grounding.
Our position is neither of these. We treat SEO and AI visibility as an integrated strategy with clearly weighted levers: a solid structural and technical foundation, answer-optimized content with topical depth, targeted Digital Authority Management through backlinks, citations, and brand mentions, and platform-specific optimization wherever it demonstrably makes a difference.
This is not the fastest concept—but it is the one that stands up to the current body of research.
What Google Officially Clarified on May 15, 2026
Since mid-May 2026, this assessment has no longer been merely our position; it has also been Google’s official position. On May 15, 2026, Google published “Optimizing your website for generative AI features on Google Search,” its first canonical guide to optimizing for AI Overviews and AI Mode. The key sentence reads verbatim: “From Google Search’s perspective, optimizing for generative AI search is optimizing for the search experience, and thus still SEO.” The German translation is equivalent to: From the perspective of Google Search, optimizing for generative AI search means optimizing the search experience—and therefore remains SEO.
The guide includes a dedicated “mythbusting” section in which Google explicitly declares five popular GEO tactics irrelevant: separate llms.txt files, breaking content into miniature chunks (“chunking”), deliberately rewriting content for AI readability, purchasing staged brand mentions, and an obsession with schema without any identifiable benefit. Three independent studies (Spriestersbach, OtterlyAI, ALLMO) had previously shown that the most widely marketed of these measures—llms.txt—is retrieved by AI crawlers in fewer than 0.1% of cases.
For us as an agency, this official clarification is a welcome confirmation—and at the same time, an important distinction. We do not sell GEO hacks because the evidence argues against them. What we do is exactly what Google’s own recommendations actively support: unique, experience-based content, clean structure and technology, and the targeted development of digital authority through authentic third-party coverage. These four pillars—structure, technology, content, and Digital Authority Management—form the basis of our SEO/GEO roadmap.
Platform-specific differentiation remains important: Google’s guide explicitly refers to its own surfaces (AI Overviews, AI Mode). Similar fundamental principles apply to ChatGPT, Perplexity, and Claude, but the requirements are not identical. Independent studies on these platforms point in the same direction, but they are not yet conclusively canonical. Reputable GEO consulting recognizes this distinction—and does not sell universal hacks where platform-specific analysis is required.
Conclusion: SEO Remains the Foundation—but the Weighting Is Shifting
The shift toward AI-powered search is not a short-term change. Visibility is no longer created exclusively through traditional rankings, but also through the probability of being cited as a source in generated answers. The good news: For all relevant AI systems with grounding functionality, this probability is demonstrably related to traditional SEO visibility.
Companies that retain SEO as their foundation while also investing in Digital Authority Management, topical depth, and platform-specific presence can secure decisive competitive advantages. KITICON supports companies in doing so—with an integrated strategy combining structure, technology, content, and Digital Authority Management, based on verifiable data rather than buzzwords.
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