In the first half of 2026, GEO completed a shift many of us had anticipated: moving from a useful marketing acronym to a genuine operational discipline that a company needs to plan for.
The clearest signal of this transition comes from Google. With the publication of its official guide to optimizing for generative AI in Search, the company puts in writing a simple, long-debated principle: visibility in AI experiences rests on a foundation of excellent SEO.
It is the formalization of a methodology. The word GEO stops being a marketing label and becomes a framework with rules, boundaries and best practices recognized at the source.
What Google actually says
The central message of the guide is unambiguous: generative AI features in Search, such as AI Overviews and AI Mode, are built on the same core ranking and quality systems that have governed Search all along.
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 we already know. 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. From Google’s point of view, optimizing for AI search means optimizing for the search experience, and so it remains, to all effects, SEO.
The foundation that holds everything up: content and structure
Google traces the entire guide back to two pillars. They are the same ones a mature SEO practice is built on, now load-bearing for AI visibility.
The first pillar is valuable content, specific and not generic. AI systems compare multiple sources and reward an original point of view, built on direct experience or real expertise. A first-person review, an analysis that comes from your own data, a perspective that does not simply summarize what is already available elsewhere: this is what makes the difference over the long term, more than any technical trick.
The second pillar is technical clarity. The way Search finds and processes pages remains the core of how AI systems access content. Indexability, crawlability, readable semantic HTML, page experience, reducing duplicate content: the foundations stay the same, and today they carry a new 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 states that llms.txt is ignored, that statement holds for Google Search, and the same guide acknowledges that keeping these files still makes sense for other services and systems that use them. AI search, as a discipline, covers more surfaces than Google Search alone.
The operating principle that follows is this. The SEO foundations Google describes are necessary everywhere: they are the baseline condition to be retrieved and cited. Specific tactics, instead, must be calibrated to the surface: what is inert on one engine can play a role on another. Reading each environment correctly, without generalizing a recommendation valid for only one, 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 starting to move: autonomous systems that interpret a goal, plan and carry out actions on the user’s behalf, such as a booking, a product comparison, a purchase.
An agent does not look at the site on a screen. It reads it 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: provide clean signals 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 open up far more for agents to do.
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 now 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, and not as a repertoire of tricks. The direction, now, is written.
Official source: Google Search Central, guide to optimizing for generative AI in Search.
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