Why websites today must speak to machines
Over the past twenty years, we have optimized websites for search engines that operated according to relatively stable rules: crawling, indexing, and ranking based on links and on-page signals. That model has not disappeared, but today it is no longer sufficient.
An increasing share of informational search no longer goes through a traditional SERP, but through systems that return synthesized answers: AI assistants, generative engines, and conversational environments. In this scenario, a website can be perfectly indexed and at the same time irrelevant to AI systems.
The reason is simple: artificial intelligences do not read pages like users do, nor do they interpret them like SEO specialists. They normalize, compare, and make decisions based on the structural clarity of the information.
From ranking to recognition: AEO and GEO
Today the issue is not doing SEO for AI, but making your business understandable to machines. Generative engines do not only look for relevant content, but for reliable entities: clearly defined companies, services, professionals, and brands.
AEO focuses on a content’s ability to answer user questions in a direct, verifiable, and unambiguous way, while GEO aims to make the brand citable and consistent within responses generated by language models.
In both cases, the starting point is not copywriting, but structure: without a solid semantic foundation, even the best content risks not being used as a source.
The BRMA framework as a method for governing complexity
At HT&T, this transition is not approached in a tactical or fragmented way. We work on AEO, GEO, and semantic modeling within the BRMA framework, an approach that integrates strategy, information architecture, development, and measurement into a single coherent path.
BRMA was designed to avoid isolated interventions that may work in the short term but fail over time. Every technical decision, including JSON-LD implementation, is evaluated in relation to business objectives, data structure, and the site’s ability to evolve alongside search systems and AI.
In this context, structured data is not a final output, but a component of a broader system in which content, technology, and performance are designed to work together rather than as independent silos.
JSON-LD: the semantic layer of a website
JSON-LD is not a technical detail nor a simple SEO aid, but the semantic layer of a website. It is the standard way to declare identities, roles, relationships, and business context to machines.
Without structured data, an AI system must infer who you are, what you do, and the domain you operate in by analyzing text, context, and indirect signals, with a high margin of ambiguity. With JSON-LD, this information is not interpreted but explicitly declared in a stable and reusable way, drastically reducing the risk of semantic misunderstandings.
Structured data formats and why JSON-LD is used today
Structured data did not originate with JSON-LD. Historically, several formats have existed to expose semantic information to machines, each with its own logic and limitations. The main ones are Microdata, RDFa, and JSON-LD.
Microdata and RDFa require attributes to be embedded directly within HTML, mixing content and metadata. This approach makes markup fragile, hard to maintain, and tightly coupled to the DOM structure, with a high risk of regressions during redesigns or refactoring.
JSON-LD, on the other hand, clearly separates the semantic layer from the presentation layer. Structured information is declared in a dedicated block, independent of layout, easier to version, test, and update. This is why it is the standard recommended by Google and major search engines.
How JSON-LD actually works
Conceptually, JSON-LD does not describe pages but entities. Each block defines a specific subject, such as an organization, a service, a person, or a product, using shared vocabularies like Schema.org.
Each entity is associated with clear and normalized attributes such as name, description, geographic scope, relationships with other entities, and external references. This allows automated systems to connect information from different sources and build a coherent representation of the brand.
In practice, JSON-LD works as a semantic contract: what is declared in the markup becomes the baseline truth upon which search engines and AI build classifications, answers, and citations.
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "ProfessionalService",
"name": "Client Name",
"url": "https://www.client.com",
"description": "Professional services in sector X with a data-driven approach.",
"areaServed": {
"@type": "Country",
"name": "IT"
},
"brand": {
"@type": "Brand",
"name": "Brand Name"
}
}
</script>Why this is an urgent issue today
Many websites published in recent years feature generic content, information architectures designed only for users, and minimal or absent use of structured data. In an ecosystem dominated by generated answers, this means not taking part in the conversation.
AI systems favor verifiable, linkable, and synthesizable content. If content is not structured correctly, it is unlikely to be used as an authoritative source.
Conclusion
Today a website does not compete only with other websites, but to be selected as a source. JSON-LD is the language through which a business is represented in the invisible layer that powers search engines and AI systems.
Ignoring it means letting others define who you are. Structuring it correctly means controlling how you are interpreted.
The difference is not made by those who produce more content, but by those who structure information better and make it coherently interpretable by automated systems.
Frequently asked questions
What is JSON-LD and why has it become so important today?
JSON-LD is a structured data format that explicitly declares to machines who a brand is, what it offers, and the context in which it operates. It has become central because search engines and generative AI no longer rely solely on keywords and links, but on entities, relationships, and information reliability.
Is JSON-LD only useful for Google, or also for AI systems like ChatGPT?
JSON-LD was created for the semantic web and is used by search engines, but it also has a direct impact on AI systems. AI models leverage structured data and semantic graphs to disambiguate information and select reliable sources, making JSON-LD a strategic asset for AEO and GEO as well.
What is the difference between traditional SEO and the AEO/GEO approach?
Traditional SEO mainly focuses on ranking in search results, while AEO and GEO aim to make content and brands understandable and citable within automated response systems. In this scenario, it is not enough to appear; being recognized as an authoritative source is what matters.
Is it correct to have multiple JSON-LD blocks on the same website?
Yes, it is not only correct but recommended. A mature website should declare different entities such as Organization, WebSite, Service, Article, or Product through multiple coherent JSON-LD blocks. This improves the readability of the semantic graph and reduces interpretative ambiguity.
Does JSON-LD directly affect rankings?
JSON-LD is not a direct ranking factor, but it affects how automated systems understand a website. Correct semantic modeling increases the likelihood that content will be used, cited, or synthesized within generated answers.
Who should manage JSON-LD within a company?
Managing JSON-LD is not just a technical responsibility. It requires collaboration between development, content, and strategy, because every structural or editorial change can impact the semantic graph and the brand’s representation within AI systems.
Can JSON-LD be added to a website that is already online?
Yes, but it is advisable to start with an analysis of the existing information model. Adding structured data without an overall vision can create inconsistencies or duplications that reduce effectiveness in the medium term.
How long does it take to see tangible benefits from a correct implementation?
Benefits are not immediate like an advertising campaign, but progressive. A correct semantic structure improves brand understanding, citability in AI systems, and response consistency over time, delivering structural and lasting advantages.
References and sources
The considerations developed in this article are based on official documentation, technical standards, and public guidelines from the main players in the search and AI ecosystem, used as operational references for semantic design, AEO, and GEO activities.
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Schema.org – Structured Data Vocabulary
Reference standard for defining entities, relationships, and structured data used by search engines and automated systems.
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Google Search Central – Structured Data & Rich Results
Technical guidelines on the use of structured data and its role in content understanding and interpretation.
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Google – Search Generative Experience (SGE)
Official communications on the evolution of search toward generative models and synthesized answers.
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OpenAI – Documentation & Model Behavior
Public documentation on how language models work and on criteria for information selection, synthesis, and citation.
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Perplexity AI – Source selection & citations
Insights into source citation mechanisms and the importance of semantic clarity in content.
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W3C – JSON-LD 1.1 Specification
Official technical specification of the JSON-LD format for representing linked data on the web.
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