Why Structured Data and JSON-LD Matter in the Age of AI

Imagine visiting a restaurant website to find its opening hours. You can probably distinguish the restaurant’s name from its address, recognise the menu and work out whether “Friday, 7pm” refers to a booking or a special event.

Software has to make those distinctions too. Search engines are increasingly capable of interpreting ordinary language, but websites still contain ambiguity: multiple dates, similar business names, outdated prices and information scattered across different pages.

Structured data gives publishers a way to label important facts explicitly. JSON-LD is a widely supported format for doing that.

With AI changing how people search, compare products and discover businesses, making information easier for software to interpret deserves attention.

What is structured data?

Structured data organises information into defined categories and relationships. On a website, it can identify a particular piece of information as a product’s price, an article’s author or an event’s location.

Consider a page advertising a pottery workshop. A visitor might read: “Join Maya at Riverside Studio on 12 October. Tickets cost £45.” Structured data can separately identify the event, instructor, venue, start date, ticket price and currency.

This creates a more explicit description of the page. It helps distinguish what each fact means and how the facts belong together.

Many websites use a shared vocabulary called Schema.org to apply these labels. It defines types such as Person, Organization, Product and Event, alongside properties describing them.

Using shared definitions means different organisations do not have to invent their own terminology for every exchange of information. Schema.org was founded by Google, Microsoft, Yahoo and Yandex, and its vocabulary is maintained through an open community process. It covers both individual things and the relationships between them.

Where JSON-LD fits in

JSON-LD stands for JavaScript Object Notation for Linked Data. The name sounds technical, but its purpose is straightforward: it provides a standard format for recording facts and connecting them using identifiers. Structured data is the organised information; Schema.org supplies the vocabulary; JSON-LD is a format used to express it.

On a webpage, JSON-LD usually sits in a dedicated block within the page’s code. Readers see the normal page while software can also access its structured description. Google supports other formats, including Microdata and RDFa, but generally recommends JSON-LD because it is usually easier to implement and maintain.

For a business owner or editor, the practical benefit is maintainability. A website can be configured to generate that description from information already entered into its publishing system. Updating the underlying product record, for example, can update both the displayed price and the structured price.

Richer search results and what the evidence shows

One of structured data’s clearest benefits is eligibility for richer search results. These are listings that include useful details beyond a conventional title and description.

For products, Google documents support for information such as prices, availability and review ratings. The details available depend on the content, the implementation and the search feature involved.

Those details can help people assess relevance before clicking. Someone shopping within a budget can see whether a product is affordable. Someone comparing options can see whether an item is available.

There is evidence that enhanced presentation can improve engagement. Google reports that Rotten Tomatoes added structured data to 100,000 unique pages and measured a 25% higher click-through rate on pages with structured data than on pages without it. Click-through rate means the proportion of appearances in search that lead to a click.

That is a useful reported result, but it should not become a forecast for every website. It concerns a particular publisher and implementation; it does not establish that every business will receive the same improvement.

Google also explicitly says that correctly implemented structured data does not guarantee a rich result. Eligibility, actual display and improved business performance are separate outcomes.

Helping search engines identify your business

Structured data can also help clarify identity. A company might share its name with another business, operate several branches or publish under a brand that differs from its legal name.

Google’s organisation guidance says structured data can help it understand and distinguish an organisation, using details such as its name, address, logo and identifiers. Some information may also influence how the organisation appears in search.

For publishers, this encourages a useful discipline: agree on the basic facts about the organisation and represent them consistently. An accurate description is easier to maintain when everyone draws from the same information.

Why structured data matters in the AI boom

Search increasingly includes generated explanations and comparisons, with links to supporting websites. Google’s AI Mode and AI Overviews can conduct multiple related searches while developing an answer. A page may therefore be discovered as evidence for one part of a broader question.

Microsoft has also introduced reporting for citations across supported AI experiences, including Copilot and AI-generated Bing summaries. That reflects a practical change for publishers: being referenced within an answer is now another form of visibility to measure.

In this environment, explicit facts and relationships have an understandable appeal. A system comparing events needs dates and locations. A system discussing products needs to distinguish the product from its seller and its current offer.

Where a system consumes structured data, those labels provide information it can use. Bing, for example, says it uses website annotations to supplement and validate its data sources, as well as improve search presentation.

However, this does not establish that every AI assistant reads every JSON-LD block, or that adding markup causes more citations. The reasonable inference is narrower: explicit, consistent information is useful infrastructure for systems designed to consume it.

There is no guaranteed shortcut to AI visibility

Google is particularly clear about the limits. Its guidance says there is no special Schema.org markup required for AI Overviews or AI Mode. Existing search fundamentals still apply, including accessible content, useful information and structured data that agrees with the visible page.

Businesses should therefore question promises that a particular schema package will secure AI recommendations. Markup can describe an author or organisation, but a description is not independent evidence of expertise. Labelling a claim does not verify it.

Accuracy matters more than adding more labels

Accuracy determines whether structured data helps or creates confusion. A page showing a £60 ticket alongside markup recording £45 sends conflicting information. The same problem arises when an event is cancelled but its structured description remains unchanged.

Google requires markup to represent the page accurately and prohibits misleading information, including fake reviews. Its policies also require relevant information to remain current.

This makes structured data an ongoing publishing responsibility. Someone needs to own it, understand where its values originate and check what happens when content changes.

AI tools may help prepare a draft, but review should compare every factual value with the source material. A neat-looking block can still contain an invented qualification, an incorrect date or a price copied from the wrong product.

How to get started

For most websites, the sensible starting point is a small set of important pages. Identify the information visitors need to make decisions, then choose a suitable description of that content.

An online shop might begin with products and offers. An organisation might begin with its identity and contact details. The choice should follow the real content and the features relevant to the business.

It also helps to distinguish vocabulary from platform support. Schema.org describes more concepts than Google uses for specific enhanced search features. A valid description does not automatically correspond to a Google rich result.

Before adding anything, check whether the website already produces structured data. Then decide which system will manage it. Multiple tools generating conflicting descriptions can turn a straightforward task into an avoidable maintenance problem.

Some content platforms also have various plugins available to help with the configuration and setup of this structured data. We have our own addon for Statamic that we support that provides site admins or developers and easy way to configure these structures across the different collections your website may have.

If you're unsure where to start, get in touch and we'll be happy to discuss your requirements further.

Test the implementation and check the facts

Testing should answer two questions: can the information be interpreted, and does it meet the requirements of the intended search feature? The Schema Markup Validator checks Schema.org markup, while Google’s Rich Results Test checks supported rich-result types.

Neither replaces a person checking whether the facts are true.

A lasting investment in clearer information

Structured data makes the facts behind a website more explicit, and JSON-LD gives publishers a practical way to express them. Its established search uses provide a reason to invest today. As AI creates more ways to discover and combine information, keeping those facts accurate, connected and easy to maintain is a sound publishing habit.