SEO for AI: the rules to become the source for AI
“Search engines as we know them are already dead.” It was July 2024 when Ivano Di Biasi wrote these words in the introduction to his book SEO FOR AI. Inventiamo la SEO del futuro (SEO FOR AI. Let’s invent the SEO of the future), and eighteen months later, we can say that he was right: Google and Bing have not disappeared, but they have transformed to adapt to the global spread of Artificial Intelligence, which has changed the way we search for and consume information. Today, virtually all of us interact daily with ChatGPT, Gemini, or Perplexity, expecting an immediate response without having to click on a result. Generative systems read the available content and decide independently whether to use, cite, or ignore it. Indexing is no longer just about access, and visibility is no longer just about position.
This is where the traditional SEO model breaks down: ranking remains a technical condition, but it ceases to be the center of the game. What matters is whether content becomes response material, who is used to construct the response provided to the user, and which brand is recognizable as the source. Within two years, what seemed like a prediction about the transformation of search engines into “invisible infrastructure” has become an operational problem for anyone running a digital business.
The starting point is therefore to understand what SEO for AI really means, the set of concrete choices that determine whether your content will be ignored as background noise, absorbed without identity, or recognized and cited as a source.
What is SEO for AI
SEO for AI is the strategic vision your brand needs to be read, understood, and recognized by AI-powered engines, which have stopped acting as intermediaries between user requests and responses and are starting to behave like authors.
Think of it as “SEO after SEO”: a discipline that encompasses and extends traditional practices to govern what happens when indexing has already worked and generative systems select, connect, and reformulate content on the web to build a unique response. This is what you need to transform your content from simple web pages into nodes of an intelligible knowledge graph—you are no longer just providing documents for humans to read, you are providing structured data and connected entities that machines can use to reason.
The protagonists of this transition are Large Language Models, the LLMs behind ChatGPT and Gemini, which do not just retrieve pages: they read large amounts of text, learn relationships between concepts, brands, and entities, and produce answers by synthesizing what they have understood. Alongside these are real-time response engines, such as Google’s AI Overview or Perplexity’s web search, ChatGPT, and Gemini itself, which search the web, compare multiple sources, and construct a single response on behalf of the user.
In both cases, content ceases to be a destination and becomes informative material. It is not shown to be chosen, it is chosen to build the response. If a system cannot clearly interpret what you say, or does not associate your brand with a sufficient level of reliability, the content remains outside the process even when it is correctly positioned in traditional results.
SEO for AI was created to govern this transition. Today, users ask a question and receive a direct solution instead of a list of links, so your visibility depends on your ability to transform a positioned URL into a source of quotable truth.
The paradigm shift is brutal: classic SEO brought you traffic, SEO for AI brings you presence in the answer. Being in the SERP is (still) necessary to exist, and the conditions for access remain: crawling, indexing, topic coverage, and basic signals continue to determine whether content can be found. But being in the AI response is necessary to count and be visible.
The dual engine of AI: memory and real-time search
The first technical complexity you face is the dual nature of current systems. When you interact with platforms such as Gemini, ChatGPT, or Google AI Overview itself, you are activating two parallel processes.
On the one hand, there is the intelligence of the past, the basic version of Large Language Models. They function like a static encyclopedia: they possess vast knowledge but “frozen” at the date of their last training (Knowledge Cut-Off).
When the system draws on this memory, it does not search the web; it only cites the entities and facts that it has assimilated and “digested” over time. Here, you cannot do immediate technical SEO: if the model cites you, it is because your brand reputation is so solid that it has become part of its “worldview.” Mentions that emerge at this level do not generate direct traffic and do not respond to classic SEO metrics, but they certify that the brand exists as a recognized information subject.
On the other hand, there is the intelligence of the present, the Answer Engines that use the RAG (Retrieval-Augmented Generation) system. These engines do not know the answer, they find it; they read the web in real time, often based on Google’s Index, perform fan-out queries (breaking down the question into multiple sub-searches), scan the results, and synthesize everything into a single text.
Here, citation is purely a technical merit: you win if your content is better structured than others to be read by the machine. The system chooses the sources that best respond to that cluster of questions, which present clear, verifiable, and consistent information. This is where traditional SEO returns to center stage: coverage of intent, depth of content, information structure, and reliability signals determine whether a source is used or discarded. The difference is that the competition no longer takes place among ten visible links, but in a much smaller space, where few sources are used to construct the entire answer.
SEO for AI is the synthesis of these two souls: build reputation to enter the encyclopedia’s memory (LLM) and do technical ranking to be captured by the researcher in real time (RAG).
The logic of probability
This duality radically changes the goal of your work.
For years, SEO gave you a static goal: ranking. Victory was positioning a URL as high as possible in a vertical list to get the click. With generative systems, ranking becomes just a prerequisite.
The real metric is inclusion. When generating the response, the model does not take the “best page” to display in its entirety. It breaks down the user’s question into dozens of simultaneous micro-searches, retrieves various documents, isolates specific fragments, and discards anything that is ambiguous. You no longer compete to be seen, you compete to be used. The system works on a probabilistic basis, calculating which text fragment is most likely to reduce the uncertainty of the final response. If your content is distracting, rhetorical, or technically messy, it increases the “computational effort” and the risk of error, leading the algorithm to discard you in favor of a more concise and structured source.
Algorithmic reputation as risk reduction
There is one last decisive factor: trust.
Generative models have a structural fear of “hallucinations” (inventing facts), the classic limitation they face that leads them to produce inaccurate answers when information is incomplete or contradictory. Every wrong answer costs the platform credibility, and to minimize this risk, they tend to favor sources that demonstrate historical consistency.
The algorithm therefore does not evaluate the quality of the individual article in isolation, but rather the overall algorithmic reputation and reliability of the information subject, your brand. It observes whether you deal with that semantic cluster on a recurring basis, whether your statements are confirmed by other authoritative sources, and whether you maintain a consistent editorial line. Contradictions can weigh more heavily than omissions.
Technically perfect content published on a domain that has no “history” on that topic is often ignored as statistical noise. Conversely, if you have worked on your entity identity, the model assigns you a higher “trust score,” increasing the likelihood that your information will be extracted and presented as factual truth.
“SEO for AI,” from editorial (pre)vision to technical standard
Let’s go back to July 2024 to recall the evolution of these months. While the market was divided between enthusiasts of the new and skeptics (or apocalypticists, who predicted yet another definitive “death of SEO”) and SEO strategies were still based linearly on keywords, SERPs, pages, and clicks, Ivano Di Biasi had already sensed the change. Looking at the first generative responses, he did not limit himself to evaluating them as a marginal extension of the engine, but had already glimpsed a change in architecture.
And SEO for AI contains an early reading of a functional mutation that now defines your daily operations, already signaling the structural crack in the model we took for granted: the answer was beginning to shift away from the results page and back into the flow of a generated conversation.
We are not quoting this text to engage in editorial archaeology or to say “we told you so”; we are talking about it because that book contains the fundamentals of reverse engineering that you are forced to apply today. The dynamics you are now observing—the decline in organic direct traffic, the increase in zero-click responses, the need for absolute thematic authority—are the direct consequences of that change described two years ago.
If an engine is capable of querying the web, retrieving documents, and synthesizing a response, then the critical point ceases to be the results page and becomes the informational material that feeds that synthesis. The prediction of the “death of search engines” has come true in the most technical way possible: Google has not disappeared, but it has changed its business. It has shed its role as the final showcase for the user to take on that of critical infrastructure. Today, the classic search engine operates as an invisible backend, a huge dynamic database whose sole function is to feed Artificial Intelligence systems with fresh, validated data.
The distinction between being seen and being used
The value of the book lies in introducing a distinction that is now law: the difference between the Retrieval phase and the Generation phase. When a user queries an AI system, the old SERP runs in a few milliseconds “behind the scenes” to retrieve the raw documents. In this step, traditional positioning remains a necessary condition: if you are not on the first page or in the Knowledge Graph, the AI does not “see” you. But being seen is no longer enough.
Immediately afterwards, the generation phase kicks in: the model reads the retrieved documents, extracts their meaning, and synthesizes a response. This is where the real selection takes place. If your content is positioned but semantically poor, AI scans it and discards it in favor of a source that, perhaps positioned lower down, offers a more coherent and “machine-readable” data structure. In this new scheme, you compete to pass the cognitive filter of the model, not (only) the statistical filter of the ranking algorithm.
During 2025, this approach became part of everyday practice: generative responses are no longer an experiment and have become a stable interface. Synthesis takes up space, the information click shrinks, and the choice of sources takes place before the user can intervene. This is where the core of the book proves to be operational. Two elements become central. The first is the selection of the source: systems favor content that can be isolated, verified, and integrated without ambiguity. The second is the role of the engine as infrastructure: no longer the final destination, but an information layer on which other systems work.
It is no longer a question of appearing higher up, but of being used as a source. The SERP ceases to be the place where the user decides; it becomes an intermediate level, an information pool from which other systems draw, compare, and reassemble content. In this setup, positioning remains necessary, but it loses its role as the final result. It serves to enter the information perimeter, not to guarantee visibility. Visibility is played out elsewhere, when information is selected to construct a response.
The new optimization work
The latest data confirms that ChatGPT handles an estimated 4% to 12% of Google’s traffic, or between 600 million and 1.7 billion queries per day. Added to this is the impact of AI Overview and AI Mode, which now intervene in more than one in ten searches globally, intercepting the informational and decision-making queries that previously drove traffic to the site.
And so your job as an SEO specialist also changes: no longer trying to manipulate an index to climb the rankings, but working with a linguistic model to provide it with the data it lacks.
You need to evolve into a semantic architect: stop writing for keywords and start designing for entities. Create content that unequivocally defines who the subject is, what they do, and how they relate to other concepts in the field. Manage the knowledge graph so that when AI calculates its vectors of meaning, your brand is mathematically the most likely and reliable answer to deliver. Google has become the circulatory system that keeps generative information alive: being excluded from it means oblivion, but overseeing it requires treating content not as text for humans, but as structured data for machines.
The souls of visibility today: SEO for AI, GEO, and AEO
To navigate this new scenario with confidence, you must first clear your mind and avoid the confusion generated by the market, which tends to invent new acronyms and abbreviations to sell old services.
The structure of visibility today is hierarchical, and each component has a specific role. At the base is classic SEO, which builds the irreplaceable foundations and guarantees technical existence. Above this is SEO for AI, which is not just “another channel” but the strategic discipline that governs the fate of content after indexing and which, in order to act effectively, is divided into two operational arms: GEO, which builds brand identity, and AEO, which ensures the performance of the response for the response.
You cannot choose one at the expense of the others, because the response generation process stops if even one link in this chain is missing. Working only on traditional SEO leads to content that is findable but easily replaceable. Working only on identity leads to recognized but little-used brands. Working only on selection leads to episodic presences, without continuity and without strong attribution. SEO for AI holds these levels together and makes them consistent, because today visibility does not depend on a single factor, but on how access, recognisability and use combine in the same system.
Classic SEO: a condition of access, not success
It all still starts here, with traditional SEO, which continues to perform an essential filtering function: making content accessible and interpretable for crawlers. Without a solid technical architecture, without proper scanning, without thematic coverage, without minimum signs of reliability, and without indexing, no content enters the scope of an AI system.
Crawling, indexing, site structure, internal linking, and editorial consistency remain the basic requirements for existing within the informational perimeter of the web—even Google has said so.
The breaking point comes later. Until yesterday, optimization work on these aspects was “enough” to determine the final ranking, but today it only serves to allow entry into the field. SEO determines whether you exist for search engines, but it does not decide whether you will be used when the response is no longer a list of links. Consider it your access condition: it ensures that your documents are in the database, but it does not ensure that they will be chosen by the generative algorithm to build the final summary.
SEO for AI comes into play precisely from this point onwards: it does not replace traditional SEO and does not make it obsolete. It governs what happens after SEO has done its job. If SEO determines whether you can be retrieved, SEO for AI determines whether you can be used. It is the transition from visibility as an event to visibility as a function.
GEO to work on identity, AEO on response
GEO and AEO fit into this scheme, operating on different and non-interchangeable levels.
GEO, or Generative Engine Optimization, is the component of SEO for AI that works on the ecosystem and attribution and serves to define an entity. Its purpose is to ensure that AI models (particularly LLMs, the intelligence of the past) know exactly who you are, what you do, and what the boundaries of your expertise are. It does not decide on its own whether a source is selected in a response, but it reduces the ambiguity that prevents the system from recognizing it as legitimate.
If a system needs to use information about your industry, it must first be able to link it to your entity without ambiguity, and GEO makes the brand “decodeable,” preventing it from being read as generic or contradictory. Working with GEO means curating the Knowledge Graph and the semantic consistency of the brand so that the model automatically associates you with topics relevant to your niche. When it works, the brand is not re-read from scratch every time; the information you publish and the information circulating on the web converge towards the same image. If you don’t do this optimization, however, you risk having excellent content that AI reads but does not attribute to your brand because it does not “trust” the source, losing the battle for reputation.
AEO comes into play later. It concerns the specific response, the moment when the system has to decide what content to use, how to summarize it, and whether to attribute it. It depends on SEO, because without access there is no recovery. It depends on GEO, because without a clear identity, selection becomes unstable. It doesn’t build reputation, it puts it to the test.
AEO optimizes the structure of the individual response for RAG engines (the intelligence of the “present”) : it works to make the text atomic, scannable, and unambiguous, facilitating the task of the algorithm that has to extract a fragment to compose the summary. While GEO is a long-term effort on reputation, AEO is a surgical effort on the page to win the immediate competition on search intent.
Why confusing these levels leads to wrong strategies
The most serious mistake you can make today is to treat these acronyms as simple umbrella labels. Everything becomes GEO, everything becomes AEO, everything is presented as “optimization for AI.” The result is operational confusion that leads to unbalanced interventions.
If you focus only on GEO (identity) without caring about AEO (response structure), you will have an authoritative but mute brand, unable to enter into direct responses. Conversely, if you only work on AEO and neglect GEO, your content will be technically perfect but lacking the “trust” necessary to be selected by the model. And if you only work on traditional SEO, you remain visible but replaceable.
SEO for AI serves precisely to avoid this short circuit; it is the ability to orchestrate these three levels: use classic SEO to get in, GEO to get recognized, and AEO to get chosen. It is the strategic level that coordinates different roles in the same system, because today visibility is not played out in one place, but along the entire path that leads from question to answer.
What does it really mean to optimize for AI?
Ivano said it plainly in his book: today, you don’t optimize to “rank higher on Google,” you optimize so that an AI system can use your content as a source and, when needed, attribute and link to it.
It’s not “using AI to do SEO.” . It is understanding that content is retrieved, broken down, compared, and synthesized, and that the game is played on how that content holds up during these phases.
You need an operational shift, which is also based on the work of the copywriter, who plays a fundamental role in the visibility strategies of every company, because it is (also) through writing that we can make content appealing to AI LLM models and no longer just to Google, responding to the various needs of users. This does not detract from classic SEO optimization for search engines, except for a greater focus on transforming data and insights about audience intentions into information.
It is a change in the function of content. You have to design what you publish knowing that it will not only be read, but used by systems that respond on behalf of the user, compare multiple sources, and seek to reduce ambiguity and error.
When the response is no longer a list of links but a generated summary, content ceases to be a final destination and becomes informative raw material. In this scenario, it is not what attracts clicks that wins, but what can be retrieved, integrated, and reworked without losing accuracy and attribution.
Optimizing for AI therefore means governing the moment when information is selected to construct a response, not just when it is found.
How a response is created: retrieval, comparison, synthesis
When a user asks a complex question, the process is not linear. The system activates an implicit breakdown of the request, which, as mentioned, is called a fan-out query, in which the question is translated into several sub-questions, each of which searches for specific information.
The retrieved documents are not evaluated as whole pages, but as sets of statements. The system compares what it finds, verifies consistency between sources, and selects only those passages that can be combined without contradicting each other. At this stage, it is not how complete a page is that matters, but how clear each individual block is in its statements.
Synthesis is the real selection point. Here, preference is given to content that allows clear, verifiable, and consistent information to be extracted. Citation, when it occurs, is a consequence of this process: it only happens if the information is recognized as reliable and attributable. Much of the content that works in SERPs never makes it into the answers precisely because it passes retrieval but fails synthesis.
Optimized content stands up to extraction
Content optimized for AI is content that withstands subtraction. If you extract a paragraph, that paragraph continues to work: it does not lose meaning, it does not become ambiguous, it does not require implicit references.
This is the basis of content chunking: retrieval systems do not work on the page as an indivisible unit, but break it down into autonomous blocks of information, or chunks, each of which is evaluated individually—and must therefore contain a statement that is understandable, verifiable, and coherent even outside its original context.
When a text needs the entire narrative flow to be understood, it becomes fragile. This informational fragility is one of the main causes of technical exclusion. Not because the content is incorrect, but because it forces the system to interpret rather than extract.
Be careful, though: this does not mean writing short, juxtaposed, artificial, and contrived sentences; it does not mean artificially breaking up the text, but constructing it knowing that it will be fragmented. It is a change in design approach, which concerns the structure of information, the separation of levels, and terminological consistency. Form also matters, because elements such as titles, lists, tables, and semantic markup help the system recognize the nature of the data and reduce ambiguity during extraction. They do not guarantee selection, but they lower the cost.
What are keywords used for today?
In this transformation, keywords do not disappear but remain our operational “foothold,” even if they now support access rather than success.
They are useful because, in the retrieval phase, the intent is translated into operational queries—strings, combinations of terms, entities, and constraints that must “match” documents in the index or retrieval. When AI activates the Query fan-out, it translates the user’s discursive prompt into a series of technical queries (keywords) to be launched against Google’s index or its own vector database.
In practice, AI acts like a user performing dozens of traditional searches in a fraction of a second. If AI breaks down the intent into queries such as “price of service X,” “reliability reviews X,” and “alternatives to X,” and your content is not optimized to intercept these specific keywords, the retrieval system will not see you. For this reason, knowing and managing keywords is still the practical basis of the work, because they are topic mapping tools, serving to cover all the branches of the fan-out and ensure that the content enters the retrieval flow. The final selection in the generated response does not depend on the presence of a single string, but on the quality and completeness of the information retrieved thanks to that semantic oversight.
However, the application changes radically: you no longer do keyword research to optimize a page for a single high-volume keyword. You work on a broader front. You have to predict the entire range of keywords into which AI will break down the topic and make sure your content covers them all. The keyword is no longer the goal of positioning, but is the technical hook that is essential for the fan-out process to hook your data.
How the criterion for success is changing
Optimizing for AI also means accepting that the criterion for success no longer coincides with clicks. Rankings, impressions, and traffic remain useful signals, but they no longer explain what is happening on their own.
Content can generate fewer direct visits and at the same time become central to the construction of responses, being used, reworked, or cited repeatedly. Visibility shifts from user behavior to system decision.
The result is no longer the click event, but stable presence as an information source. It is a form of exposure that is less obvious in the short term, but more structural, because it is based on the repeated use of information, not on occasional choice.
Optimizing does not mean controlling everything
There is one last point that really defines what it means to optimize for AI: not everything is immediate, and not everything can be corrected in the short term.
The structure of the content immediately affects the possibility of being used in responses. Identity, continuity, and reputation act over longer periods of time. Some associations are built progressively, others settle, and still others are not quickly reversible.
Optimizing, therefore, does not mean forcing a result, but knowing which levers you are pulling now and which ones you are preparing over time. Without this distinction, optimization becomes expectation. With this distinction, it becomes strategy.
How to do SEO for AI today: on-page interventions
One critical issue remains: how should pages be constructed so that they can go through the process of retrieval, selection, and synthesis without losing meaning?
Doing SEO for AI does not mean changing the tone of writing or adapting language to a machine, but accepting that content will not be queried, broken down, and recombined—it will be used.
Not everything that is well written is automatically usable. A text can be correct, complete, and positioned, but unusable because it forces the system to interpret instead of extract. On the contrary, what works is what becomes informative raw material, and SEO for AI serves precisely to “ensure” that what you publish is retrieved, interpreted correctly, and integrated into a response without distortion. This changes the way content is designed, even before how it is written.
The idea is to work on content as if it were to be read in two ways at the same time: by a person who wants to understand, and by an engine that needs to extract, compare, and reuse specific parts of the text. Optimization is not a final “touch-up,” but rather a sequence of practical interventions on titles, information blocks, syntax, and markup, with a clear goal: to reduce ambiguity and make information reusable without loss of meaning.
Underlying this is a broad and difficult task: you must clarify who is speaking and why they are reliable—an anonymous or neutral text is more easily discarded by AI security filters. You must make explicit the experience, role, and context of the author or brand. Clearly separate facts from opinions: AI must be able to distinguish between objective data and your interpretation. Content that declares its level of responsibility and source (E-E-A-T) is much more likely to be included than generic text, even if the information is correct.
Preventive fan-out analysis
SEO for AI begins before you open the text editor, in the design phase, to try to anticipate the engine’s work by answering three operational questions: what is the main intent, into which sub-questions will the AI break down this intent, and which of these deserve a separate block.
In practice, you should not start with the keyword, but with the information problem, breaking it down into a series of implicit FAQs. If a sub-question is relevant to the topic (e.g., “how much does it cost,” “how does it work,” “is it safe”), it must have a dedicated title and a separate text block. If you allow an important answer to remain diluted within a generic paragraph, that information does not exist for the AI because it is not labeled.
Headings, therefore, become operational labels, because they are the most direct way to tell the engine what a block contains. If you use h2 or h3 headings as “reading breaks” (Introduction, Considerations, An important aspect), you are creating sections that only work for those who read the entire page in sequence. A system that breaks down the request into sub-questions needs informative labels: titles that already tell you what question you are answering.
In practice, each title must explicitly contain the topic or question. If you are talking about a service or product, titles such as “Costs” are too generic; “How much does service X cost” or “Prices and what is included in plan X” immediately declare the scope. If you are writing a guide, ‘Requirements’ is not as helpful as “Requirements for doing Y in 2026” or “What you need to get started with Y.” The rule is simple: a title must be able to stand alone, outside the page, without losing its meaning.
Writing for extraction: chunking and the inverted pyramid
The second step is autonomy control, which is the practical version of chunking—not simply “breaking up,” but building blocks that resist fragmentation.
Reread each section as if it had been copied and pasted elsewhere. If you find sentences within that block that depend on what comes before, you have a weak point. Typical forms are references (“as mentioned above”), additions based on the unspoken (“at this point”), and implicit subjects (“it is useful because…” without saying what). An engine can extract the block without the previous paragraph: if your text needs context to avoid ambiguity, it loses value as a response. It is a matter of cleaning up the syntax for machine readability.
The correction is concrete: put the subject back in the key sentence, close the perimeter of the block, and make the reference explicit. There is no need to weigh everything down; it is only necessary to do so in passages where the meaning could “detach” from its referent.
Within the chunk, the writing must follow the rule of the inverted pyramid, because AI looks for the ground truth (the certain fact) at the beginning of the block. The first sentence under each H2 or H3 heading must contain the direct answer to the question in the heading—not a premise, not a hook, not a detour, preambles, or historical contextualization: write a line that immediately closes the question in the heading, give the data (subject + verb + answer). Only after providing the factual truth can you add nuances or examples.
If you hide the point after three paragraphs, you are increasing friction. If you state it immediately, you make it easier to extract and verify. Here, the content ceases to be “narrative” in the classic sense and becomes informative in the strongest sense: substance first, argumentation second. This is a rule of copywriting that is also useful for people, but it becomes even more important for search engines because they often extract, compare, and synthesize information starting from the initial portions.
The new approach to keyword management
You no longer work to make a single keyword win; you use queries as a tool to design coverage and ensure retrieval.
Operationally, you need keywords to predict which modules will be searched for when AI breaks down the main question. If the topic is software, the predictable sub-questions are price, alternatives, integrations, security, limitations, and use cases. If the topic is strategy, the sub-questions become steps, mistakes, prerequisites, tools, and applied examples. Each of these must have a “home” in the text: an h2/h3 and a block that really answers the question, without thinking about density or length as potential “winning” criteria. Synonyms and semantic variations are only useful if they clarify the concept, not to cover search quotas.
The result is not a page full of keywords, but a text that covers the spectrum of questions that will be generated. You are optimizing a set of blocks, each technically linked to a specific search query, and the keyword opens up access.
Use HTML as a constraint of truth
The last intervention concerns the form in which you present the information, and you need to learn how to use HTML tags logically. When you are presenting comparable data (features, differences, prices, requirements), a table makes it easier to extract and reduces the ambiguity of the relationship between values and categories—in other words, use the <table> tag: for an answer engine, data entered in a <td> cell crossed with a <th> header is secure information that is easy to extract and display. When you are describing a procedure, an ordered list clarifies the sequence and dependencies between steps; when you are enumerating discrete elements, a list better separates the units. Therefore, always use ordered (<ol>) or unordered (<ul>) lists for step-by-step procedures, or the <li> tag to unambiguously indicate a discrete element in a list.
This is the power of structured data, which in this context refers not only to Schema.org markup (invisible to the user), but also to the HTML structure of the page (visible and rendered). Before training or querying models, systems parse the page to eliminate design “garbage” and retain the information essence. At this stage, an HTML table is worth its weight in gold because it contains a perfect set of “key-value” pairs (header-cell) that are vectorized while keeping the relationship between the header and the data intact.
Schema.org markup is read by the parser in a similar way—as if it were an additional data table—but it serves a different function, mainly to define identity. AI finds key-value pairs (“Brand: Name,” “City: New York”) in the JSON-LD code and uses them to build the vector association between your entity and your industry. So, the operational hierarchy is clear: use visible HTML tables to provide factual answers (the “what”) and use Schema.org to define who you are (the “who”), allowing the system to associate your authority with your content. Both are “embedded,” but if the information crucial to the user is buried in code or in discursive text rather than in an explicit table, you are wasting an opportunity for immediate extraction.
And don’t forget to use bold (<strong>) not to emphasize tone of voice, but to mark entities and factual data (names, dates, prices), helping the model weigh the relevance of terms within the block.
It’s not about “decorating” the page, but about choosing the container that makes the logical nature of the information clearer. With the same text quality, the structure can make the difference between a block that is taken and one that is skipped because it is too discursive or difficult to segment.
The operational checklist: 8 crash tests for your content
Do not publish anything unless you have submitted the text to this audit. These questions help you understand whether what you have written is compatible with the way AI engines select and use sources. If the content fails any of these tests, it is structurally fragile and risks exclusion.
- The fan-out (coverage) test
- The question: If AI breaks this topic down into 5 sub-questions, do I have an answer for all of them?
- The action: Make sure you haven’t written a wall of generic text. If you’re talking about a product, you need to have specific blocks for price, operation, alternatives, and safety. If the block is missing, the hook for that micro-query is missing.
- The labeling test (retrieval)
- The question: Are my H2/H3 titles understandable without reading the content that follows?
- The action: Eliminate creative or “blind” titles (“A nasty surprise,” “What to consider”). Rewrite them by including the entity and the explicit question (“How much does X cost,” “The risks of Y”). The title must function as a database row.
- The atomization test (independence)
- The question: if I cut this paragraph and paste it into WhatsApp to a colleague, does it make sense?
- The action: look for logical dependencies. If the text begins with “As we said,” “Furthermore,” or “However,” or if the subject is implied, rewrite it. Each block must be a self-sufficient island.
- The truth test (inverted pyramid)
- The question: Is the factual answer (the data) in the first sentence after the title?
- The action: Move the data (ground truth) to the beginning. If the user has to read three lines of introduction to find the number, date, or name they are looking for, you have failed. Subject + verb + data right away. Explanations come after.
- The ambiguity test (syntax and consistency)
- The question: Do I always use the same name to define the same thing?
- The action: eliminate unnecessary stylistic synonyms and maintain strict terminology for the names of products, functions, or key concepts (if it’s called “Tool X,” don’t call it “Instrument”). For the rest of the text, maintain flow, but remember to repeat the explicit subject at the beginning of each new paragraph, because the AI may read that block without having seen the previous one.
- The distinction test (facts vs. opinions)
- The question: Can AI distinguish my opinion from technical data?
- The action: Separate facts from opinions visually or logically. Do not mix technical data (fact) with your judgment (opinion) in the same sentence. AI favors what it can verify and attribute with certainty.
- The code test (format)
- The question: Am I using discursive text to explain something that could be in a table?
- The action: if you see numbers, comparisons, or lists of characteristics “drowned” in sentences, convert them to HTML (<table> or <ul>). You are transforming a probable opinion into certain and extractable data.
- The identity test (responsibility)
- The question: Is it crystal clear who is speaking and why are they a valid source?
- The action: Avoid an impersonal or passive tone. Make direct experience explicit (“We tested,” “Our data shows”). Source selection depends on the recognizability of the author or brand behind the data.
How to measure the success of SEO for AI today
If you measure SEO for AI by looking only at rankings and clicks, you are looking at the wrong channel. Here you need to measure three things: presence as a source, role in the response, and stability over time (with the same intent, as the wording varies).
If you have structured your content to be “eaten” by an LLM following fan-out and atomization protocols, you cannot measure the result with a yardstick calibrated to ten blue links. Here, you are not observing the behavior of a user scrolling through a list, but the outcome of an invisible algorithmic choice—which information the system decides to use and which it discards. The result is not a click, but inclusion in a synthesis process.
Traditional metrics therefore cease to be central, giving way to the analysis of selection patterns. The goal is not to know if you “appear” once, but if you are consistently chosen as a source when the response is constructed.
- Presence in the response: single event vs. stable behavior
The first distinction to be made is clear: an occasional appearance in an AI response is not a metric, it is a statistical event that may depend on a lucky formulation or a temporary context. SEO for AI is only measured when presence becomes repeated behavior. Since it is not possible to know the actual queries of users, measurement shifts to controlled variation. We work by constructing families of prompts that express the same intent with different formulations. If, by changing the way the question is asked, the response continues to draw on the same sources, it means that the content has passed the selection phase and is considered reliable for that topic. The metric is not the specific presence, but the resistance to change in formulation.
- Informative role: central or ancillary source
Not all occurrences have the same weight. Being cited as the main source (ground truth) is not the same as being an ancillary source, nor is it simply being incorporated into the summary without clear attribution. Measuring SEO for AI means understanding what role you play in constructing the answer: are you the basis on which the summary is built, or are you one of many interchangeable pieces? This can be observed by analyzing the structure of the responses. When your content disappears as soon as the angle of the question changes, you are not yet winning the selection. If, on the other hand, your data (the price, the technical definition) remains constant even when the rest of the response changes, you have become a structural source.
- Fan-out coverage and information delegation
Every AI response comes from breaking down the intent into operational sub-questions, and this breakdown still occurs through queries asked on the index. This is why keywords are still needed to map the fan-out. The measurement goes from identifying which sub-questions are covered by your content and which are delegated to other domains. If an answer draws on multiple sources, the question to ask yourself is not “am I present?”, but “which parts of the problem am I excluded from?”. Every recurring exclusion signals an information gap: if AI uses you for the general explanation but uses a competitor for costs, it means that your block on costs was not considered authoritative or clear enough.
- Brand attribution and recognition
Content can be used without the brand clearly emerging. This is the risk of absorption: the information is used, but the entity is not recognized. SEO for AI only really works when there is persistent attribution. Finally, there are indirect signals that help confirm the picture: an increase in brand searches, growth in navigational queries, and a reduction in informational traffic accompanied by greater exposure of the name. These are not definitive KPIs, but signals that confirm that value has shifted from clicks to repeated use of information.
How SEOZoom makes the invisible measurable
The operational challenge of SEO for AI is analytical: how do you verify that what you do is actually being used by systems? How do you optimize for a “black box” that does not return traffic or ranking data?
Generative responses do not show rankings, do not expose native metrics, and do not return direct signals—and even Google Search Console does not give you any information on AI Overview, for example.
Our response at SEOZoom is pragmatic: if you cannot track the internal algorithm, you must measure the external signals that feed it. With the platform, you can measure and correlate observable signals, transforming optimization from a theoretical hypothesis into a controllable process.
It does not attempt to guess the machine’s thinking, but makes the selection criteria (structure, identity, authority) that transform content into a source observable and quantifiable. Using SEOZoom for SEO for AI means linking content, identity, and responses in a single reading system. There is no “magic” tool, but a process that allows you to reduce uncertainty, observe concrete signals, and correct your strategy before exclusion becomes structural.
- AI Engine: verifies the relevance of content for AI engines
Does this content have the characteristics that today lead an AI system to use it as a source? What is it missing? And what content is better?
AI Engine answers these questions and quickly tells you if your text has a chance of competing on AI engines. It builds a predictive model of popularity, analyzes your content, and compares it with pages that are already gaining visibility in AI contexts, evaluating structure, information coverage, and clarity. It does not simulate prompts or responses, it allows you to iterate: you modify the text, relaunch the analysis, and measure whether you are heading in the right direction. This allows you to move away from the “publish and hope” approach and enter into a dynamic of continuous verification before publication.
- GEO Audit: analyzes identity consistency
Source selection always starts with entity validation. GEO Audit analyzes the domain to verify if and how the brand is interpreted as an entity, with what thematic associations, with what stability, with what authority (E-E-A-T). The tool highlights discrepancies between what the brand claims to be and what emerges from the web and published content, an increasingly critical step: without passing this identity check, even the best content risks being ignored because it lacks an authoritative “signature.”
- AEO Audit: measures the role in responses
AEO Audit works on the final result: if and how the brand enters AI responses, whether it is a primary, secondary, recurring, or occasional source. Even if there is no data to show, you can read patterns, continuity, and variations. This is where SEO for AI becomes observable and the use of information becomes measurable over time.
- AI Prompt Tracker: tests information activation scenarios
There is another insurmountable technical limitation to measuring your “success” and visibility in AI: you cannot know exactly what each individual user will ask. However, SEOZoom gives you a chance to test likely scenarios, constructed in a manner consistent with the intentions and questions that an AI system might activate.
AI Prompt Tracker allows you to build and monitor a set of probable prompts related to your brand and industry, and periodically query conversational engines to see if and how you are mentioned. By analyzing changes in responses over time, you can understand whether your strategy is working: if you go from being ignored to being mentioned as a source, you have tangible proof that your work on the entity and content is paying off.
- Question Explorer: monitor information fan-out
SEO for AI stems from the breakdown of intent into sub-questions, and Question Explorer is the tool that allows you to read this fragmentation before it is executed by AI engines. It does not make you collect keywords to repeat, but maps the real and potential questions that revolve around a topic. This is where the fundamental operational shift takes place: from the keyword as a target to the question as an information unit. This allows you to immediately see which response blocks must exist within a piece of content to avoid information gaps that lead to exclusion from the summary.
Quotes are taken from “SEO for AI. Inventiamo la SEO del futuro” (SEO for AI. Inventing the SEO of the future) by Ivano Di Biasi, published by Palladino ©2024, reproduced with the author’s permission. For information: SEO for AI.
