AI search engines will compete with Google, Yahoo, Bing, etc. for our search queries. AI-based search engines combine AI thinking with information on the web. This results in a search engine that can answer questions more specifically, with greater precision, and without lengthy research. In addition to the answer to the search query, there will also be a source citation, with links to the websites used to answer the question.
This model is currently being launched by OpenAI on a trial basis with SearchGPT.
„SearchGPT could displace Google's monopoly in the search engine market, meaning SEO as we know it will be fundamentally changed.“
How does AI search differ from traditional search?
The probably biggest difference between the two search engine systems is the formulation of questions and finding answers. In conventional search engines, questions are posed clearly, concisely, and with a heavy emphasis on keywords in order to find the best search results. The search results can then be clicked through until the desired answer is found.
AI search, on the other hand, works primarily through context, meaning it understands a search query whether it's formulated in keywords or long detailed sentences and provides a correspondingly detailed answer to the question.
How it works and technology
The AI search function combines various technologies to answer questions as precisely as possible. An important component of the search is natural language processing, also known as NLP. It enables the AI to understand and interpret search queries in natural language and, based on this, retrieve the necessary information for the answer. For this purpose, web content (texts, images, videos) is captured and stored in a structured manner. To understand the meaning of terms and their relationships to each other, the context and semantic relationships of the content are analysed.
Another key concept is the Retrieval-Augmented Generation (RAG). This retrieval extension allows the AI to access external data sources, find relevant information, and incorporate it into its responses. RAG combines information retrieval with text generation to further improve search results.
Another advantage of the AI search engine is the user-friendly and individual user experience, which is continuously improved based on stored user interactions. Furthermore, this method is particularly user-friendly as it enables accessibility.
From the context, NLP-based search engines manage to „understand“ what the user contextually wants to achieve.
The search result itself, unlike conventional search systems, is not listed but presented as a summary.
Core functions of AI search engines
Conversational search
AI search engines enable conversational interaction. The relevance of keywords recedes into the background, which is why the enforcement of this method will also have a significant impact on SEO and content strategy. Instead of focusing on keywords, NLP-based search engines understand the context and nuances of the query. Since the context is also maintained for follow-up questions, a continuous dialogue with the search engine is thus possible.
Intelligent Prioritisation of Results
AI search engines use specific algorithms to identify and rank relevant information. On the one hand, the frequency of search terms is analysed, but factors such as position within the text and the quality of linked pages are also taken into account. These algorithms „learn“ from interactions with search queries and continuously improve their systems based on the insights gathered.
Integration of visual and interactive elements
In addition to factual information, AI also searches the internet for visual representations such as photos, videos, or graphics. This function makes it possible to make complex information easier to understand and to ensure all possible search results. This core function contributes positively to the user-friendly search experience.
AI search engines vs. traditional search engines
The intelligent search engine is capable of understanding not just the search term, but also the context, using NLP techniques. This means you don't have to send a separate search query for each topic, but can directly request all the information you need. Based on this question-text, the AI search engine generates a summarised answer text from multiple sources.
Traditional search engines cannot answer such complex questions. Here, a list of relevant websites is displayed. The user reads through the websites until they find the desired answers.
The different response formats play a significant role in which search engine users tend to favour. The NPL-based search engine proves to be more efficient as less effort is required to arrive at an answer. On the other hand, users can engage in an interactive dialogue and receive targeted answers to follow-up questions, as the AI recognises the context of the previous question. The new search capability also offers a personalised experience, as the answers are tailored to the current user.
While traditional search engines deliver results faster, it can be debated whether the time isn't ultimately the same. With traditional search, additional time is required to find the desired answer, whereas with AI search engines, it is displayed directly.
SEO for AI search engines
SEO as we know it will change with NPL-based search engines. While links will be displayed with answers, the probability that users will click on them is low. After all, they will have already received all the important information and, in many cases, will no longer need to visit the website. This is advantageous for the user, but less so for businesses and website owners – this function potentially leads to a decrease in organic traffic and click-through rates.
Existing SEO strategies must also be revised and adapted to the capabilities of AI search engines to understand the context and intent behind search queries.
This means the focus is shifting away from keywords and towards contextual orientation. Texts need to be adapted to a natural and conversational language. But not all keywords are losing relevance; long-tail keywords, for example, are gaining importance as they better reflect natural language control. Furthermore, despite long-tail keywords, SEO will need to concentrate more on the semantic meaning and context of content in the future.
Technical SEO aspects
Even in technical SEO, there are requirements that need to be taken into account regarding AI search engines. The implementation of structured data helps content to be better understood and interpreted.
Conclusion
AI search engines are changing our search behaviour by combining traditional search engines with AI and natural language processing. Instead of a list of links, they provide precise, context-based answers. This allows for a more efficient and user-friendly search experience.
If this model becomes the definitive one, SEO as we know it will also be fundamentally changed: The focus will shift away from keywords and more towards the context of the query. For users, AI search offers a faster and more personalised answer. Companies, on the other hand, will need to adapt their SEO strategies to remain relevant.
