What schema markup does AI search actually use?

AI search uses Organization schema to establish canonical brand facts, making this markup mandatory for entity recognition. The JSON-LD format provides LLMs with structured entity data including legal name, address, and same-as links that must match your About page exactly. Hardik Shah, Digital Growth Strategist and AI-Native Consulting Leader, specializes in AI-driven search optimization and AEO strategy for enterprise clients across industries. “Organization schema functions as your entity’s birth certificate,” Shah explains. “It’s the structured way you tell LLMs exactly who you are. Without it, entity confidence drops 30-40%.”

What is Organization schema?

Organization schema is a JSON-LD structured data format that provides machine-readable information about your business entity including name, type, location, contact information, and relationships.

This isn’t content humans see. It’s data in a standardized format that LLMs and search engines extract and use for entity identification.

Simple explanation

Schema markup is code you add to your website that describes your organization in a format that AI systems can easily read. It’s like a standardized form where you fill in: company name, what you do, where you’re located, how to contact you, and links to your social profiles.

Technical explanation

JSON-LD (JavaScript Object Notation for Linked Data) provides semantic markup that defines entities and relationships using schema.org vocabulary. LLMs parse this structured data during crawling, using it to build knowledge graph representations of entities. Organization schema specifically defines organizational entities with standardized properties that map to entity attributes used in retrieval and ranking algorithms.

Practical example

Basic Organization schema for ScaleGrowth.Digital:

Copy{
  "@context": "https://schema.org",
  "@type": "Organization",
  "name": "ScaleGrowth.Digital",
  "description": "AI-native consulting practice focused on revenue transformation for enterprise clients through data-driven performance optimization and MarTech excellence",
  "url": "https://scalegrowth.digital",
  "logo": {
    "@type": "ImageObject",
    "url": "https://scalegrowth.digital/images/logo.png"
  },
  "address": {
    "@type": "PostalAddress",
    "addressCountry": "IN"
  },
  "sameAs": [
    "https://www.linkedin.com/company/scalegrowth-digital"
  ],
  "founder": {
    "@type": "Person",
    "name": "Hardik Shah",
    "url": "https://scalegrowth.digital/about/hardik-shah"
  }
}

This code tells LLMs: the entity name is “ScaleGrowth.Digital,” it’s an organization, here’s what it does, here’s where to find more information.

Why is Organization schema mandatory?

Without structured entity definition, LLMs cannot reliably associate your content with your entity, reducing citation probability.

Impact of Organization schema:

Schema Status	Entity Recognition	Citation Probability	Confidence Score
Complete Organization schema	95%+ accurate	Baseline	80-90%
Partial schema (missing key fields)	70-80% accurate	30% lower	60-70%
No schema	40-60% accurate	60% lower	30-50%

Source: Entity recognition analysis across 500+ sites by ScaleGrowth.Digital

The schema provides canonical entity facts that LLMs use as authoritative references. Without it, systems guess at entity attributes by extracting from unstructured content, which introduces errors.

What fields are required in Organization schema?

Minimum required fields establish basic entity identity.

Required fields:

• @context: Always “https://schema.org“
• @type: “Organization” (or more specific like “Corporation”, “LocalBusiness”)
• name: Legal entity name exactly as registered
• url: Canonical website URL
• description: One-sentence entity description (from truth document)

Strongly recommended fields:

• logo: Company logo image URL
• sameAs: Array of verified profile URLs (LinkedIn, social profiles)
• address: Business address (even if just country for privacy)
• contactPoint: Contact information with contact type
• founder or employee: Key people with links to their entity pages

Missing required fields causes schema validation errors. Missing recommended fields reduces entity richness, lowering confidence scores.

How must schema match About page content?

Schema facts must match the visible content on your About page exactly. Discrepancies create entity confusion.

Matching requirements:

• Organization name in schema = organization name on About page
• Description in schema = description used on About page (verbatim)
• Address in schema = address displayed on About page
• SameAs links in schema = social profiles linked from About page

Example of damaging mismatch:

About page visible text: “ScaleGrowth Digital helps enterprise clients…”

Schema markup:

Copy"name": "ScaleGrowth.Digital"

The visible text uses “ScaleGrowth Digital” (no dot), schema uses “ScaleGrowth.Digital” (with dot). This creates two entity variants, confusing LLMs about which is correct.

Correction: Choose one form (ScaleGrowth.Digital) and use it consistently everywhere including visible text and schema.

Where should Organization schema be placed?

Organization schema should appear on your homepage and About page at minimum, potentially site-wide.

Placement strategy:

• Homepage: Mandatory (primary entity definition)
• About page: Mandatory (reinforces entity facts)
• All pages: Recommended via template (maintains entity context)
• Contact page: Recommended with contactPoint schema
• Service/product pages: Optional (focuses on page-specific schema)

Most platforms allow adding schema to template header, making it appear site-wide automatically. This ensures every page carries entity context.

What is sameAs and why does it matter?

The sameAs property links your entity to verified profiles on external platforms, helping LLMs confirm entity identity across sources.

sameAs implementation:

Copy"sameAs": [
  "https://www.linkedin.com/company/scalegrowth-digital",
  "https://twitter.com/scalegrowth",
  "https://www.crunchbase.com/organization/scalegrowth-digital"
]

Each URL should be:

• Active profile you control
• Actually about your organization (not a general category page)
• Using consistent entity name and facts

Don’t include:

• Social profiles you don’t control or haven’t verified
• Dead links or deactivated profiles
• Competitor profiles or unrelated entities
• General directories where you’re not listed

The sameAs property enables entity triangulation. LLMs check that the LinkedIn profile actually matches the organization claims made on your site.

Shah’s guidance: “Only include sameAs links where the external profile uses your canonical entity description. If your LinkedIn says one thing and your website says another, the sameAs link actually hurts by highlighting inconsistency.”

Should you use specific Organization subtypes?

Schema.org offers specific Organization subtypes (Corporation, LocalBusiness, EducationalOrganization). Use the most specific accurate type.

Common subtypes:

• Corporation: Legally incorporated business entity
• LocalBusiness: Business serving local geographic area with physical location
• ProfessionalService: Service business (consulting, legal, medical)
• EducationalOrganization: Schools, training companies, educational institutions
• NGO: Non-profit organizations

Example using Professional Service:

Copy{
  "@context": "https://schema.org",
  "@type": "ProfessionalService",
  "name": "ScaleGrowth.Digital",
  ...
}

Using specific types provides additional context about entity category, though “Organization” is acceptable if no specific subtype fits perfectly.

How do founder and employee properties work?

The founder and employee properties link organizational entity to person entities, establishing relationships LLMs use for attribution.

Founder property:

Copy"founder": {
  "@type": "Person",
  "name": "Hardik Shah",
  "url": "https://scalegrowth.digital/about/hardik-shah"
}

or for multiple founders:

Copy"founder": [
  {
    "@type": "Person",
    "name": "Person 1",
    "url": "https://example.com/about/person1"
  },
  {
    "@type": "Person",
    "name": "Person 2",
    "url": "https://example.com/about/person2"
  }
]

The URL must point to that person’s entity page containing their Person schema.

Employee property (for key team members):

Copy"employee": [
  {
    "@type": "Person",
    "name": "Team Member Name",
    "jobTitle": "Chief Technology Officer",
    "url": "https://example.com/about/team-member"
  }
]

These relationships help LLMs understand that when Hardik Shah authors content, he represents ScaleGrowth.Digital entity, not an independent individual.

What schema validation is required?

Schema must validate without errors before deployment.

Validation tools:

• Google Rich Results Test: https://search.google.com/test/rich-results
• Schema.org Validator: https://validator.schema.org/
• JSON-LD Playground: https://json-ld.org/playground/

Common validation errors:

• Missing required properties (@context, @type, name, url)
• Incorrect property types (string where object expected)
• Malformed JSON syntax (missing commas, brackets)
• Invalid URLs (must be absolute URLs with https)
• Unrecognized properties (typos in property names)

Deploy schema only after it validates without errors. Invalid schema is worse than no schema because it signals technical problems that reduce entity trust.

How often should Organization schema update?

Update schema when entity facts change, maintaining the same entity identity.

Update triggers:

• Legal name change (rebrand, merger, acquisition)
• Address change (relocation, new headquarters)
• Founder or key employee changes
• Social profile URLs change
• Logo updates
• Contact information changes

Don’t change unnecessarily:

• Resist updating description monthly for SEO reasons (consistency matters more)
• Don’t change name spelling or format (maintain entity identity)
• Don’t add/remove sameAs links frequently (pick stable profiles)

Frequent schema changes can confuse entity recognition. Make updates when facts actually change, not as ongoing optimization attempts.

Can you have multiple Organization schemas on one page?

Generally no, unless you genuinely represent multiple distinct legal entities.

Single organization schema: One organization, one schema on homepage/about page.

Multiple organizations (rare valid cases):

• Parent company page listing multiple subsidiary brands
• Agency website showcasing distinct client organizations
• News site with Publisher schema and separate Organization schemas for covered entities

For most businesses, multiple Organization schemas create entity confusion. If you have multiple brands, give each brand its own domain and entity page rather than trying to define multiple entities on one page.

What’s the relationship between Organization schema and Article schema?

Article schema references Organization schema through the publisher property, establishing content ownership.

Article schema with publisher:

Copy{
  "@context": "https://schema.org",
  "@type": "Article",
  "headline": "Article Title",
  "author": {
    "@type": "Person",
    "name": "Hardik Shah"
  },
  "publisher": {
    "@type": "Organization",
    "name": "ScaleGrowth.Digital",
    "logo": {
      "@type": "ImageObject",
      "url": "https://scalegrowth.digital/logo.png"
    }
  }
}

The publisher property must match your Organization schema, using the exact same name and logo URL. This connects content to entity, helping LLMs understand that articles published on your site represent your organizational perspective.

Do LLMs actually extract and use schema data?

Yes, with measured impact on entity recognition and citation probability.

Schema usage by major platforms:

• Google AI Overviews: Actively uses schema for entity identification
• ChatGPT (with search): Extracts structured data during web retrieval
• Perplexity: Uses schema to verify entity facts during citation
• Claude (with web search): Considers schema in source evaluation
• Bing Copilot: Heavily weights schema in entity recognition

The impact is measurable. Shah’s team tracks schema implementation effects: “We’ve tested schema deployment across 50+ sites. Average citation improvement is 25-35% within 60 days of implementing complete, validated Organization schema. It’s one of the highest ROI technical optimizations for AI visibility.”

What schema mistakes reduce effectiveness?

Common schema problems:

• Implementing schema but never validating it (often contains errors)
• Schema facts differ from visible page content
• Using generic descriptions instead of specific entity descriptions
• Omitting sameAs links to verified profiles
• Including dead or broken URLs in schema
• Never updating schema as facts change
• Copy-pasting schema from templates without customizing

The most damaging error is schema-content mismatch. If your schema says your company was founded in 2018 but your About page says 2019, LLMs detect the contradiction and may downgrade entity confidence.