Grupa Insight

Structured Data

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Structured data is machine-readable markup added to web pages that describes what a page is, who created it, what it contains and how it relates to other entities — without requiring AI systems to interpret prose.

Why it matters

Structured data does not directly cause AI citations. But it gives systems unambiguous signals about page meaning, authorship, dates and entity relationships — reducing interpretive load and increasing the probability of correct attribution.

For Google AI Overviews and AI Mode specifically, Google states that no special schema.org markup is required to appear. Structured data should not be treated as an AI shortcut. Its role is more fundamental: it makes pages self-describing so that systems can answer who published this, when and on what topic — without parsing prose.

Minimum viable schema for AI Search readiness

Organization

Where

Homepage + About page

Key fields

name, url, logo, sameAs, contactPoint

WebPage / Article

Where

Every content page

Key fields

author, datePublished, dateModified, publisher

Person

Where

Author bio pages

Key fields

name, jobTitle, worksFor, sameAs — links back to articles

FAQPage

Where

Service pages + long-form articles

Key fields

Question + acceptedAnswer pairs

BreadcrumbList

Where

Sitewide

Key fields

itemListElement with position, name, item

LocalBusiness / ProfessionalService

Where

Service businesses

Key fields

address, areaServed, serviceType

The self-describing page test

Can a system answer "who published this, when, and on what topic" by reading only your structured data — without parsing the visible page content? If not, your schema is incomplete. The goal is not to accumulate schema types but to make every important page self-describing.

Common structured data failures

Implementation checklist

Related resources