Structured Data For Search And AI: Make Your Digital Presence Understandable.
Websites are no longer interpreted only by people and traditional search engines. Their information can also be retrieved, connected, summarised and cited by AI-driven platforms.
That makes clarity increasingly important.
A person visiting a website can usually recognise an article, author, organisation, service, product or case study from its design and context. Machines need to interpret those same relationships from content, links, technical structure and other available signals.
Structured data can make part of that meaning explicit. It can clarify that a name belongs to a person, that this person wrote an article, that the person works for an organisation, that the organisation provides a service or that a video belongs to a particular page.
Structured data does not guarantee higher Google rankings or visibility in ChatGPT, Gemini, Claude or other AI platforms. It does not create expertise, authority or relevance.
It is one clarity layer within a larger digital presence.
WHAT IS STRUCTURED DATA?
Structured data is a standardised way of describing the meaning of information on a web page.
A visitor may immediately understand that a page contains:
an article title;
an identifiable author;
a publication date;
information about an organisation;
a service;
a product;
a case study;
a video;
or an event.
A machine initially encounters HTML, text, images, links and code. Structured data adds explicit labels that describe what certain information represents and how different elements relate to each other.
Google defines structured data as a standardised format for providing information about a page and classifying its content. Google can use it to understand a page and, for supported content types, make that page eligible for enhanced search appearances known as rich results.
Eligibility is the important word. Correct structured data does not guarantee that Google will display a rich result, improve the ranking of the page or produce additional traffic.
For AI platforms, the relationship is even less direct. Different platforms use different crawlers, search providers, indexes and retrieval methods. There is no universal public rule stating that every AI platform processes every Schema.org property in the same way.
Structured data can make information more explicit. Whether and how a particular platform uses that information depends on the platform.
WHAT IS SCHEMA MARKUP?
Structured data is the general concept. Schema.org provides a shared vocabulary for describing people, organisations, articles, products, services, events and many other entities.
Schema.org was founded through cooperation between major search engines, including Google, Microsoft, Yahoo and Yandex. A shared vocabulary allows websites to describe information in a form that different systems can interpret more consistently.
The vocabulary contains types such as:
Person
Organization
Article
BlogPosting
Service
Product
Event
VideoObject
BreadcrumbList
ProfilePage
Each type has properties. A Person, for example, can have a name, jobTitle, url, image and worksFor relationship. An Article can have a headline, author, datePublished and publisher.
The vocabulary is not the same as the technical format used to add it to a page. Google supports JSON-LD, Microdata and RDFa, with JSON-LD generally being the recommended format.
Schema markup therefore uses the Schema.org vocabulary to turn visible information and relationships into structured data.
WHY THIS MATTERS BEYOND TRADITIONAL SEO
Traditional search results remain important, but the way people find information is changing.
Someone may still type a short query into Google. They may also ask:
Which agency can automate our reporting process?
Who is an experienced executive coach near Amsterdam?
Which organisation can build a branded AI avatar?
What are the best agencies for AI visibility?
Which expert has written about a particular leadership problem?
Who has demonstrable experience with this type of project?
An AI platform may break such a question into several searches, retrieve different sources, compare claims and generate an answer with supporting links.
To be selected as a useful source, a page must first be accessible and relevant. The platform must then be able to interpret what the page says, who is responsible for it and how it relates to other available information.
Structured data can support that interpretation by making certain relationships explicit. It can help distinguish:
an author from the organisation publishing the article;
a personal profile from a company page;
a service from an editorial article;
a real case from a general claim;
a video from a decorative animation;
one identifiable person from someone with the same name;
one organisation from another organisation with a similar name.
It does not replace the content or evidence. It helps label them.
WHAT STRUCTURED DATA CAN DO
Used accurately, structured data can help search systems:
identify the primary subject of a page;
distinguish a person, organisation, service, article or product;
understand who wrote an article;
connect an author to a profile page;
connect a person to an organisation;
recognise publication and modification dates;
understand relationships between related entities;
interpret videos, images and breadcrumbs;
make eligible pages available for supported rich results.
This can improve the way a result is presented in traditional search. A more informative result can attract more attention and may improve click-through rates.
Structured data may also provide machine-readable context to other systems that choose to process it. This makes it a relevant component of a broader AI visibility strategy.
That is different from claiming that schema markup is a universal AI-ranking factor. No such universal ranking factor exists.
WHAT STRUCTURED DATA CANNOT DO
Structured data does not:
make weak content useful;
turn a claim into evidence;
create expertise for an author;
make an organisation authoritative;
guarantee a rich result;
guarantee higher rankings;
guarantee inclusion in an AI answer;
guarantee a citation in ChatGPT;
compensate for blocked crawler access;
replace internal links or clear navigation;
compensate for contradictory information;
create external credibility;
make invisible information legitimate.
An organisation cannot add awards, reviews, expertise, clients or professional relationships to its markup when those claims are not supported by the visible page.
Google requires structured data to represent the actual content of the page. Misleading markup, fabricated reviews or information hidden from visitors can make a page ineligible for rich results and may lead to a structured-data manual action.
Structured data should describe reality, not manufacture it.
VISIBLE CONTENT MUST COME FIRST
A common mistake is to begin with the schema and then decide which properties can be filled.
The process should run in the opposite direction.
First determine what the page is for, which information visitors need and which relationships must be explained. Then use structured data to describe that visible information.
If a page is about a person, the page should genuinely provide information about that person. If the markup states that someone works for an organisation, that relationship should also be understandable to visitors. If an article has an author, the author’s name and preferably a link to a relevant profile should be visible.
Google explicitly states that structured data should describe the page on which it appears and must not contain information hidden from readers.
This principle is also important for AI visibility. When visible content, internal links, metadata and structured data describe different versions of reality, machines must decide which version to trust.
Consistency reduces ambiguity.
WHICH TYPES ARE RELEVANT?
More markup is not automatically better. The appropriate type depends on the primary purpose and visible content of the page.
Organization
Use Organization to describe the organisation itself. Relevant properties may include its name, URL, logo, address, contact information and verified external profiles.
The same organisation should use one stable @id across the website.
Person and ProfilePage
Use ProfilePage when the primary subject of a page is one identifiable person or organisation. The mainEntity can reference the corresponding Person.
The profile page should contain meaningful visible information about the person, their experience and their relationship with the organisation.
Article or BlogPosting
Use Article or BlogPosting for editorial content. The markup can describe the headline, author, publication date, modification date, image and publisher.
The author should be connected consistently to the same profile and Person identity.
Service
Service can describe a real service offered by an organisation. It can clarify the service, provider, audience and area served.
Service markup does not currently create a standard Google service rich result, but it can still add semantic clarity when it accurately reflects the page.
BreadcrumbList
BreadcrumbList describes the position of a page within the website hierarchy. It helps communicate the relationship between a page and its parent sections.
VideoObject
Use VideoObject when a page contains a relevant video and the necessary visible information, such as a title, description, thumbnail and upload date.
A decorative background animation is not automatically a VideoObject.
CONNECT THE ARTICLE, AUTHOR AND ORGANISATION
Structured data becomes more meaningful when the same real-world entity is referenced consistently across related pages.
An article page can identify its author and connect that author to one stable identity:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@type": "BlogPosting",
"@id": "https://www.example.com/insights/example-article#article",
"url": "https://www.example.com/insights/example-article",
"headline": "Example Article Title",
"description": "A concise description of the article.",
"image": "https://www.example.com/images/example-article.jpg",
"datePublished": "2026-09-02",
"dateModified": "2026-09-02",
"author": {
"@type": "Person",
"@id": "https://www.example.com/authors/jane-doe#person",
"name": "Jane Doe",
"url": "https://www.example.com/authors/jane-doe"
},
"publisher": {
"@type": "Organization",
"@id": "https://www.example.com/#organization",
"name": "Example Organisation",
"url": "https://www.example.com/"
},
"mainEntityOfPage": {
"@type": "WebPage",
"@id": "https://www.example.com/insights/example-article#webpage"
}
}
</script>
The author page can describe the same person using the identical @id:
<script type="application/ld+json">
{
"@context": "https://schema.org",
"@graph": [
{
"@type": "ProfilePage",
"@id": "https://www.example.com/authors/jane-doe#profile",
"url": "https://www.example.com/authors/jane-doe",
"name": "Jane Doe",
"mainEntity": {
"@id": "https://www.example.com/authors/jane-doe#person"
}
},
{
"@type": "Person",
"@id": "https://www.example.com/authors/jane-doe#person",
"name": "Jane Doe",
"url": "https://www.example.com/authors/jane-doe",
"image": "https://www.example.com/images/jane-doe.jpg",
"jobTitle": "AI Strategy Lead",
"worksFor": {
"@type": "Organization",
"@id": "https://www.example.com/#organization",
"name": "Example Organisation",
"url": "https://www.example.com/"
},
"sameAs": [
"https://www.linkedin.com/in/jane-doe"
]
}
]
}
</script>
Only include properties that are accurate, relevant and supported by information visible on the corresponding page.
The value lies in the connection. The article, author profile and organisation all refer to the same identifiable person and organisation instead of creating a new disconnected entity on every page.
HOW TO IMPLEMENT STRUCTURED DATA FOR SEARCH AND AI
Step 1: define the purpose of the page
Determine what the page is primarily about. Is it an article, service, person, organisation, product, event or video?
Do not begin by adding every schema type that appears remotely relevant.
Step 2: check the visible information
List the information that is genuinely visible on the page. If important context is missing, improve the visible content before extending the markup.
Step 3: select the appropriate vocabulary
Use Schema.org to identify the most specific applicable type and properties. Check whether Google has additional requirements for that type.
Schema.org supports many more types and properties than Google uses for rich results. Schema validity and Google rich-result eligibility are not the same thing.
Step 4: create stable identities
Use stable @id values for recurring entities such as the organisation, website, authors and important services.
Reference those identifiers consistently from articles, profiles, services and relevant case pages.
Step 5: connect the visible ecosystem
Structured data works best when it supports an already coherent structure:
articles link to authors;
authors link to organisations;
services link to relevant cases;
cases provide evidence for claims;
organisation pages explain ownership and responsibility;
external profiles confirm the same identity.
Without those visible connections, the schema describes a system that visitors cannot experience.
Step 6: allow relevant crawlers
Structured data cannot help a platform that cannot access the page.
For Google Search and Google’s AI features, the page must be accessible to Googlebot and eligible for indexing.
For ChatGPT Search, OpenAI states that websites blocking OAI-SearchBot will not be shown as sources in ChatGPT search answers, although navigational links may still appear.
Crawler access does not guarantee selection. It makes selection technically possible.
Step 7: add the markup to the correct page
Place structured data on the page it describes. In a CMS such as Wix, the implementation method may differ by page type.
The fact that markup can be inserted globally does not mean every page should contain the same complete graph.
Step 8: validate the implementation
Use Google’s Rich Results Test to check Google-supported search features.
Use the Schema Markup Validator to validate broader Schema.org markup.
A technically valid result does not prove that the markup is appropriate, truthful or useful. Manual comparison with the visible page remains necessary.
Step 9: inspect and monitor
Use Google Search Console’s URL Inspection tool to determine how Google accesses and renders the page.
Monitor:
indexing;
structured-data errors;
rich-result eligibility;
organic impressions and clicks;
referral traffic from AI platforms;
fixed commercial questions in AI search;
mentions and citations;
which source pages are selected;
changes after content or template updates.
NO SPECIAL AI SCHEMA EXISTS
The growth of ChatGPT, Google AI Overviews, Gemini, Claude and other AI interfaces has created a new market for supposed AI-visibility shortcuts.
Claims now include special GEO markup, AI-specific schema and machine-readable files that allegedly make a brand more likely to be recommended.
Those claims require caution.
Google explicitly states that no special Schema.org markup is required for AI Overviews or AI Mode. Existing SEO fundamentals remain relevant, including crawlability, internal links, visible textual content, good page experience and structured data that matches the page.
OpenAI documents which crawler allows a website to appear in ChatGPT Search, but it does not state that adding particular Schema.org types will produce a citation or recommendation.
Structured data remains useful because clarity remains useful. It should not be presented as a secret AI-optimisation mechanism.
STRUCTURED DATA WITHIN AI VISIBILITY
A useful way to understand AI visibility is to separate it into layers.
Access
Can the relevant search engine or AI crawler reach the page?
Content
Does the page provide a specific, useful and supportable answer?
Identity
Is it clear which person or organisation is responsible for the information?
Relationships
Can articles, authors, organisations, services, products and cases be connected coherently?
Evidence
Do original experience, cases, data and external sources support the claims?
Technical clarity
Do metadata, internal links and structured data describe the same visible reality?
Observation
Is the organisation monitoring actual questions, mentions, citations, source pages and referrals?
Structured data primarily supports the identity, relationship and technical-clarity layers. It does not replace the others.
THE BENSTEENSTRA.COM CASE
The work around BenSteenstra.com demonstrates why structured data becomes meaningful only when connected to the broader brand and content architecture.
The website contains hundreds of articles, multiple services, books, videos, tools, Dutch and English content, AI Ben and decades of entrepreneurial and coaching experience.
Adding a Person schema block could not, by itself, explain how all those components belonged together.
The larger task was to connect:
one consistent expert identity;
Ben Steenstra’s different professional roles;
the relationship between the person and his organisations;
articles and authorship;
expertise and topic architecture;
services and products;
first-hand experience;
books, videos and AI tools;
internal pages and external profiles;
visible content and structured data.
The markup helps describe these relationships. The content, experience and external evidence make them credible.
This distinction matters. Machines may understand what a relationship claims. They still need evidence before that relationship becomes a strong reason to trust, cite or recommend the source.
BENEFITS AND LIMITATIONS
Accurate structured data can provide meaningful benefits:
clearer interpretation of page content;
stronger connections between related entities;
eligibility for supported rich results;
more informative traditional search appearances;
consistent machine-readable identities;
clearer relationships between authors and organisations;
better technical documentation of a large content ecosystem;
additional context for systems that process structured information.
There are also limitations and risks:
invalid code may not be processed;
incomplete markup can prevent rich-result eligibility;
misleading markup can violate Google’s guidelines;
global templates can describe the wrong entity on many pages;
schema can become inconsistent with updated visible content;
excessive markup creates unnecessary maintenance;
AI platforms do not all disclose or use structured data identically;
correct implementation still provides no guarantee of rankings, citations, recommendations or traffic.
The objective is not to add as much schema as possible. It is to describe the right information as clearly and consistently as possible.
HOW QUICKLY DOES STRUCTURED DATA PRODUCE RESULTS?
There is no reliable standard period after which structured data will produce a measurable result in either search engines or AI platforms.
The page must first be crawled and processed. This can take days or longer, depending on the platform, website and crawl frequency.
Even after processing, there may be no immediately visible change. Google may decide not to show a rich result. An AI platform may not retrieve the page for the monitored question. The structured data may primarily reduce ambiguity without producing a separately measurable result.
Measure observable outcomes instead of assuming them:
Was the page crawled?
Is the markup valid?
Does it match the visible content?
Are important entities connected consistently?
Is the page indexed?
Is a rich result shown?
Does an AI answer mention or cite the organisation?
Which source page is used?
Did impressions, clicks, referrals or conversions change?
Structured data should never be sold with a guaranteed traffic increase, AI citation or fixed results timeline.
MAKE THE MEANING CLEAR
Digital information is relational.
People write articles. People work for organisations. Organisations provide services. Cases demonstrate experience. Videos belong to pages. External profiles confirm identities. Content answers questions for particular audiences.
Search engines and AI platforms must reconstruct those relationships from the information they can access.
Structured data can make part of that reconstruction easier, but only when the underlying website tells a coherent and truthful story.
Start with identity, positioning, content and evidence. Create a website structure that makes their relationships understandable to people. Ensure that relevant crawlers can access the information. Then use structured data to describe the same reality to machines.
That is where structured data becomes valuable.
Not as an SEO trick. Not as a guaranteed route into AI answers. But as one important layer in a clear, credible and connected digital presence that search engines and AI platforms can understand.
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