The term “Google AI” encompasses more than just a singular technology – it stands for an extensive strategy for the integration of artificial intelligence into nearly all of Google’s products and services. The goal is to fundamentally change the user’s interaction with digital information, away from conventional search and towards an intelligent assistance systems that actively understand, interpret and respond to user questions.
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Central to this strategy is the Google’ Gemini: a large, multimodal language model that was built from the ground up. Whether it’s in web search, Gmail, Google Docs or on Android devices, “Google AI” is employed everywhere.
It is important to distinguish between:
- Google AI as a strategic alignment – the entirety of all measures to integrate AI technology deeply into products and user experiences.
- Gemini as the technical core model – which directly implements this strategy.
- AI Overviews and AI Mode as new search functions – that show how strongly Google’s role changed: from a search supplier to a response giver.
With the relatively quick rollout of AI Mode in mid-2025 Google is clearly showed an ambitious approach to their AI strategy. This new form of search interaction is already replacing conventional search result lists for many users, but it also creates new challenges for companies, publishers and SEOs.
“Google AI” is no longer a future vision but a new reality. Those who want to continue gaining visibility must understand how Google will process, evaluate and display content in the future. The conventional optimisation for ten blue links is no longer sufficient; now it’s all about being more visible as a brand within AI responses.
What can Gemini do?
Gemini is a family of multimodal AI models that were developed by Google DeepMind. It was presented for the first time at the end of 2023. The current version (Nov 2025) is Gemini 2.5 but further releases have already been announced. Gemini 3.0 is schedules for late 2025.
Gemini was developed from the beginning to be able to process multiple types of data. It can analyse a picture, generate text for it and solve a programming task based on this—without having to switch between specialised models. This multimodality is a central difference to many other LLMs that were primarily trained on texts.
Google offers different models for different tasks:
- Gemini Ultra: For highly complex tasks, for example for research or special applications.
- Gemini Pro: Universal model for web use and the Gemini app.
- Gemini Nano: Resource-conserving version for smartphones (such as the Google Pixel).
Gemini is the result of a combination of two AI initiatives: Google Brain and DeepMind. This bundling of resources has lead to a quicker development and higher quality. Special variants like Med-Gemini already showcase how efficient the model is in vertical areas of application—such as medical diagnoses and the interpretation of complex documents.
Evaluating Gemini data with SISTRIX!
With the beta of SISTRIX for AI/Chatbots you can now measure how visible your brand is in large AI models like ChatGPT, Gemini and DeepSeek. We assess if and how often a brand is named, which competitors appear, which topics dominate and sources (if they appear!) Additionally, we determine relevant entities such as places, products or persons and can thus precisely contextualise the context of our conclusion.
The analysis works similarly to Google or Amazon search results: it shows the responses for which a brand is present, which content leads to it being named and where potential for optimisation can be found. This way, a new basis for market and web analyses are created, tailored to the growing importance of AI-based search systems.
AI search from Google – an overview
Conventional search results, with a list of blue links, is increasingly being replaced by Google’s AI Overviews. These AI generated summaries appear above the organic hits and directly offer a compact response to the question. This is complemented by Google’s AI Mode, which enables a conversational interaction—similarly to chatbots, e.g. ChatGPT.
Overview of Functions
- AI Overviews: Immediate summary from multiple sources.
- AI Mode: A type of chat with the Google search.
- Contextualisation: The search remembers earlier questions and takes personalised information into account, if the user consents.
This new search experience has far-reaching consequences: users have to do less research themselves, content is being presented in a pre-structured way and the path to information is drastically reduced.
“AI Mode changes both the presentation and choice of results, but does not fully overturn the world of search. Especially when looking at the possibility of the AI Mode becoming the standard for web search, this is certainly a positive signal.” (Johannes Beus)
Tracking AI Overviews with SISTRIX
Since the past few months, Google has been broadly showing AI Overviews in Germany as well. At this point, these new SERPs are already appearing for about 17% of all keywords. To be able to evaluate the AI Overviews and optimise content accordingly, SISTRIX offers three different features.
- A filter in the keyword table makes it possible to directly find out, for which keywords a domain is listed as a source in the AI Overviews.
- A menu button in the SERP area shows all keywords for which a domain appears in the AI Overview and offers information for the position within the response box.
- The SERP archive has been extended to show the complete content of an AIO, including linked sources and detected entities.
Gemini vs. ChatGPT: Comparing two approaches
While OpenAI retroactively added multimodality after ChatGPT primarily focussed on texts, Gemini was conceived with multimodality in mind from the beginning. This allows for a deeper connection between different types of data.
- Gemini is fully integrated into Google’s world of products—from search to Gmail to Google Cloud.
- ChatGPT on the other hand profits from the integration into Microsoft’s services such as Bing, Word or Excel.
Gemini’s main advantage lies in its connection to Google search. The model can integrate current information from the web and reduce hallucinations, at least theoretically. In practice, both systems only react to a prompt and then provides a response based on probabilities, not facts. Additionally, not everything that is online is correct: if more half-true AI texts on the web are the basis of AI responses, the possibility of receiving incorrect answers increases.
For SEO, this means that in the future one must positively elevate one’s content from the mass of AI generated texts and deliver even more transparency regarding used sources and data that not only humans but also AI search engines can rely on.
Effects on conventional search and SEO
The new search logic changes the user behaviour significantly:
- Information is directly presented in the search results.
- Users click less frequently on organic results.
- The importance of conventional SEO factors shifts from a click in the direction of a citation.
For content creators this means: the competition surrounding visibility gets more severe, since Google is becoming a direct competitor with their own AI responses. At the same time, the necessity to configure content in such a way that it is cited as a trustworthy source in the AI Overviews emerges.
Google is moving away from a Search Engine towards being an Answer Engine—with direct effects on traffic, monetisation and the informational structure of the web. For many vendors, their mentions will not suffice if they are only named in the footnotes and barely get any clicks.
Gemini in all Google products
The Gemini model is already embedded in almost all products of the company:
- Google Search: AI Overviews and AI Mode are directly based on Gemini.
- Gemini App: Available for Android and iOS as a personal assistant.
- NotebookLM: Uses Gemini to extract structured information from one’s own documents and convert texts automatically into audio formats.
- Google Workspace: Gemini helps with writing, summarising and analysing in Gmail, Docs, Sheets, etc.
- Pixel Smartphones: Gemini Nano operates locally and facilitates, for example, summaries of voice memos directly on the device.
- Google Cloud: Developers can build applications using the Gemini API.
Even experimental tools such as “Project Mariner” – a browser agent that can visit websites, compare products or make purchases by itself – are driven by Gemini.
Gemini is changing the future of search
Gemini isn’t just a language model, it’s the central infrastructure for Google’s future products. AI Search, Gmail, Docs, smartphones and cloud services are increasingly based on this model. Google is changing from a search engine to a universal problem solving tool.
With this transformation, new opportunities are arising for users, companies and developers. At the same time, the amount of challenges is also increasing: data protection, transparency, fairness and the question who actually owns the digital knowledge of the world wide web.
For SEO this means that a change in perspective is needed. Whoever wants to stay visible in the future as well has to create content that isn’t just readable but will be judged as trustworthy and useful by machines. Conventional search will lose significance as quick responses result in instant user satisfaction and less clicks to a website. However, conventional SEO also works to help sites and brands become more visible in AI responses.
Additionally, people will always search for products or solutions for which AI responses simply do not suffice. This means it will still be worth investing in SEO and accepting growing complexity needed to rise to this challenge.
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