AI search and Chatbots are changing the way information can be found and displayed. For SEO teams, this signifies a fundamental change: away from the clicks and rankings and towards brand references and presence in responses.
Search engine optimisation has been synonymous with Google optimisation for a long time. The goal was to reach the highest placement possible within organic results to increase reach and clicks. With the introduction of AI search and the increasing usage of chatbots such as ChatGPT, this principle changes fundamentally.
More and more frequently, search systems are replying to questions directly in their generated text without the necessity of clicking on a source. For brands and website operators, a new form of visibility is created: the deciding factor is no longer where a link appears but instead if and how a brand is named in the response. This is where GEO comes into play.
Performance measurement in AI search: from clicks to brand mentions
The goal of search engine optimisation used to be clear: gaining the highest possible placement in the Google search results and the corresponding amount of clicks to one’s website. With the introduction of AI search and chatbots, this standard has changed in an elementary way. These systems aim to answer a user’s question directly in their reply text. External links are most often not necessary anymore.
While Google is still citing sources in their AI Mode, the click rate on these references is minimal. For ChatGPT, this web search is often omitted entirely, which means that no sources are shown.
The performance measurement for AI search orients itself not by the amount or position of links to one’s website, but by whether the brand or products were mentioned in the responses. With the SISTRIX AI Projects, you can pick for which brands and competitors the AI answers should be searched for. In the Research Area, the recognition of entities is centrally done through us. The sources that this is based on are not included in this recognition.
| Conventional SEO (Search Engine Optimisation) | GEO (Generative Engine Optimisation) | |
|---|---|---|
| Primary goal | High rankings in the search results (SERPs) | Direct mention as a source in the AI generated answers |
| Most important metric | Organic clicks, Click-Through-Rate (CTR), Position | Brand visibility, frequency of the references |
| User interaction | User clicks on a link to visit a website | User receives the response directly in the chatbot or AI search |
| Content strategy | Creation of content that caters to specific keywords | Creation of extensive, fact-based content that answers complex questions directly |
| Role of the brand | Brand is the goal behind the click | Brand is turned into a direct authority and source of information within the response |
| Provider environment | Dominated by Google | Diversified market (Google AI, ChatGPT, Perplexity, etc.) |
| Performance measurement | Measurable via ranking tools and web analysis (for example Google Analytics) | More difficult to measure; monitoring of AI responses across different platforms is necessary |
| Long-term effect | Build-up of traffic and authority over one's own domain | Build-up of brand authority and trust directly with the target group |
Why chatbots do not deliver fixed responses
AI chatbots function differently from conventional search engines on their most basic level. For the traditional web search, all documents within the index are sorted by their relevancy for the sought-after keyword. This way, similar results will usually appear for the same search. Large Language Models on the other hand, the basis for chatbots and AI search, work with probabilities. This leads to responses varying much more greatly, even if the question stays the same.
Additionally, the result is dependent on whether the chatbots are generating the response from their own LLM knowledge or if they are using a grounding process, for which a web search is used.
The sources used can also vary from request to request. This behaviour, called unstable citations, leads to responses varying, even if the prompt was identical. This is why it is sensible to gather daily data points and long-term trends, as well as observing average values. By following this process, you can identify fluctuations and draw more reliable conclusions.
Responses from Foundation Models and RAG
AI systems generate their answers in different ways. Foundation Models fall back on the knowledge they learned during the training process. They don’t have any access to current web content and respond exclusively based on their internal knowledge. This leads to consistent, but oftentimes less up-to-date results.
While using RAG, Retrieval Augmented Generation, a targeted web search is carried out ahead of the response. The retrieved information is incorporated into the reply and causes it to be more current and specific. ChatGPT uses both approaches, depending on version and settings, whereas the Google AI Mode always works with RAG and integrates external sources. Foundation models deliver stable, but limited answers. RAG systems react more dynamically, but are also more dependent on the quality of the employed sources.
From a search engine to many AI platforms
With the shift from conventional SEO to AI SEO, the environment of providers changes as well. Previously, Google dominated the search almost entirely. Today, various AI systems and chatbots respond to the users’ questions. In practice, however, especially Google with their AI Mode and OpenAI with ChatGPT play a central role. Both shape how content is found, processed and portrayed.
Risks and limits of AI responses
AI systems are opening up new possibilities, but also carry uncertainties with them. Since answers are generated based on probabilities, they can be incomplete, false or inconsistent content-wise. Even RAG systems will only deliver responses that are as reliable as the sources they use.
For an analysis this means that singular responses should not be assessed by themselves. What is essential is the pattern over time and many prompts. The tools in SISTRIX AI support this exact approach by regularly evaluating data and making long-term trends visible.
In summary
The changeover from Google SEO to AI SEO changes how visibility is measured, assessed and optimised. What counts are no longer clicks, but mentions. The competition is no longer surrounding positions, but presence in the response texts. Brands that understand and continuously observe this new logic can secure visibility early on in this next generation of search.
