In the first half of 2026, GEO made the leap we had been expecting for a while now. From a label good for marketing slides to an operational discipline, one to plan for inside the organization like any other function.
The clearest signal comes from Google itself. With its official guide to optimizing for generative AI in Search, the company puts in writing what we have argued for some time now. Visibility in AI experiences is built on a foundation of excellent SEO.
It is the formalization of a method. GEO stops being a label and becomes a framework with rules, boundaries and practices recognized at the source.
What Google actually says
The central message of the guide leaves no room for interpretation. AI Overviews and AI Mode run on the same ranking and quality systems that have always governed Search.
Two mechanisms explain why.
The first is RAG, Retrieval-Augmented Generation. To build a reliable answer, the model retrieves relevant, up-to-date pages from the Search index, using the same ranking systems it has always used. And the quality of that retrieval depends on the SEO quality of the page.
The second is query fan-out, the breaking down of the user’s question into several related queries, generated simultaneously to retrieve richer results. Here too, what enters the process is content that is already indexed and well structured.
The consequence is direct. For Google, optimizing for AI search means optimizing for the search experience. It remains, in every sense, SEO.
The foundation that holds everything up: content and structure
Google traces the entire guide back to two pillars, the same ones a mature SEO practice is built on. Today, though, they are also the foundation of AI visibility.
The first pillar is valuable content, specific rather than generic. AI systems compare multiple sources and reward an original point of view built on direct experience or real expertise, such as a first-person review, an analysis that comes from your own data, or a perspective that does not simply summarize what is already available elsewhere. That is what makes the difference over the long run, more than any technical trick.
The second pillar is technical clarity. The way Search finds and processes pages remains the backbone of how AI systems access content. Indexability, crawlability, readable semantic HTML, page experience, less duplicate content. The foundations are the same ones as always. Today they are simply carrying a heavier load.
In short: GEO that works comes from SEO that works. Those who have done good work on the basics start with an advantage.
The myths Google debunks, and the reading that matters
The most discussed part of the guide is where Google lists the practices that, for Search, can be ignored.
Among them, llms.txt files and “special” markup designed for machines, which Google Search does not use. Splitting content into blocks, unnecessary because the systems understand multiple topics on a single page. Rewriting content meant only for AI. Chasing inauthentic “mentions.” An excessive focus on structured data, useful for overall SEO but not required for AI search.
Here a precise reading is needed, because this is where many stop at the headline.
Google is talking about its own ecosystem. When it says llms.txt is ignored, that holds for Google Search. The same guide acknowledges that keeping these files still makes sense for other services that do read them. AI search, as a discipline, covers more surfaces than Google Search alone.
The operating principle is this. The SEO foundations Google describes are necessary everywhere, the baseline condition for being retrieved and cited. Specific tactics, instead, must be calibrated to the surface, because what is inert on one engine can carry weight on another. Reading each environment for what it is, without stretching a recommendation valid for one across all of them: that is exactly the work that separates serious GEO from a collection of tricks.
The frontier: the site has a new visitor
There is a section of the documentation that looks further ahead than all the others, the one dedicated to agentic experiences.
The website has a new kind of visitor. Alongside people and crawlers, AI agents are now on the move, autonomous systems that interpret a goal, plan and act on the user’s behalf. They book, compare products, buy.
An agent does not look at the site on a screen. It reads the site through machine-readable representations, the screenshot interpreted by a vision model, the DOM HTML, the accessibility tree that distills roles, names and states of interactive elements. The quality of those representations determines how well the agent can operate.
The design principle is clear. Clean signals are needed across all these channels: semantic HTML, stable layouts, unambiguous interactive elements, a structure an autonomous system can traverse without ambiguity. Add to this emerging protocols such as the Universal Commerce Protocol, which will give agents significantly more room to act.
It is the next operational level. For most Italian sites it is still ground to be claimed, and this is where the advantage of the coming months will be decided.
The common thread
The publication of these guidelines changes the way we can talk about GEO. There is, at last, an official source that defines its boundaries and traces AI visibility back to a recognizable foundation.
The common thread remains one. Visibility in AI systems is won on solid SEO foundations, content built on real expertise, and a technical structure designed for people and machines alike.
The teams that will win this phase are those that treat GEO as measurable, multi-surface engineering, oriented toward results. The others will keep chasing isolated tricks. The direction, now, is written.
Official source: Google Search Central, guide to optimizing for generative AI in Search.
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