Complete Schema Markup Guide for AI Search 2026

Look, you've probably heard about schema markup. Your SEO person mentioned it once. Maybe you even have some basic schema on your homepage. But here's the thing most people miss: schema isn't just about getting those fancy rich snippets in Google anymore.

For AI search - ChatGPT, Perplexity, Claude, Google's AI Overviews - schema is how you tell these systems who you are. Not vaguely, through keywords and content. Explicitly, through structured data they can actually parse.

This guide covers the schema types that matter for AI search optimisation, with actual code examples you can adapt. No fluff, no theory, just the stuff that works.

Traditional search engines use schema as one signal among hundreds. Nice to have, occasionally helpful for rich snippets, but not essential.

AI systems are different. They're trying to understand entities - who you are, what you do, how you relate to other entities. Schema speaks their language directly. Instead of hoping AI figures out that James Foster is the founder of SuperHub which is a digital marketing agency in Devon, you tell it explicitly through structured data.

The AI systems that are rewriting search don't just want to find relevant pages. They want to understand and cite authoritative sources. Schema helps them understand. Good schema helps them see you as authoritative. The worst outcome is they stop ignoring you and then fantasise about you or your business, app or software, thinking it, or you do something different. Schemas help avoid this.

This is the foundation. If you only implement one schema type, make it this one. Organization schema tells AI systems who you are as an entity.

What to include:

• Legal name and trading name
• Description of what you do
• Contact information
• Social profiles (these help verify your entity across the web)
• Logo
• Founding date
• Number of employees
• Area served

Code example:

Common mistakes: Using only basic fields. Missing the @id property (this is how you reference this entity from other schema). Forgetting social profiles that help establish cross-platform identity.

If you serve a geographic area - even if you work nationally - LocalBusiness schema matters. It tells AI systems where you're based and helps with local search queries.

What to include:

• Address (full structured address, not just a string)
• Geographic coordinates
• Opening hours
• Price range
• Accepted payment methods
• Service area (if you serve beyond your physical location)

Code example:

Common mistakes: Not including geographic coordinates (these help significantly with local AI queries). Using "ProfessionalService" when "LocalBusiness" with additional type is more appropriate. Missing opening hours.

If you have key people whose expertise matters to your authority - founders, authors, subject matter experts - Person schema connects their credentials to your organisation.

This is particularly important for AI search because these systems weight expertise heavily. An organisation with credentialed experts gets cited more than one without.

What to include:

• Full name
• Job title
• Description of expertise
• Publications (books, articles)
• Awards or notable achievements
• Connection to your organisation
• Social profiles

Code example:

Common mistakes: Not connecting the person to the organisation using @id references. Missing the knowsAbout field which explicitly states expertise areas. Forgetting publications.

For service businesses, Service schema tells AI systems exactly what you offer. This is crucial for appearing in recommendation-style queries like "who provides motorsport marketing services in the UK?"

What to include:

• Service name
• Description
• Service type (from schema.org taxonomy)
• Provider (link to your Organisation)
• Area served
• Price range or offers

Code example:

Common mistakes: Not creating separate service schema for each major service. Missing the provider link to Organisation. Vague descriptions that don't help AI understand what you actually do.

FAQ schema is underrated for AI search. These systems are answering questions. FAQ schema gives them pre-structured question-answer pairs they can cite directly.

What to include:

• Questions that people actually ask (not invented marketing questions)
• Comprehensive answers with specific information
• Only on pages that genuinely have FAQ content

Code example:

Common mistakes: Using FAQ schema on pages without visible FAQ content. Writing marketing-speak answers instead of genuinely helpful information. Having too many questions that dilute the relevance signal.

For blog posts and articles, Article schema helps AI systems understand what you've written and whether to cite it.

What to include:

• Headline
• Description
• Author (linked to Person schema)
• Publisher (linked to Organisation schema)
• Date published and modified
• Image
• Article section or category

Code example:

Common mistakes: Not linking author and publisher to existing Person and Organisation schema. Missing dateModified which signals freshness. Generic images that don't help establish topical relevance.

For instructional content, HowTo schema is gold. AI systems love this format because it gives them step-by-step information they can cite or present directly.

Code example:

Here's where most implementations go wrong. They add schema types in isolation. Each one is valid, but they don't connect to each other.

AI systems build entity graphs. They want to see relationships. James Foster works for SuperHub. SuperHub offers Motorsport Marketing services. This article was written by James Foster and published by SuperHub.

The @id property is how you create these connections. Every main entity gets a unique @id, and other schema references it:

This connected structure tells AI systems that we're a coherent entity with verified expertise, not just a random website with some structured data.

Use these tools to verify your schema is correct:

• Google's Rich Results Test - search.google.com/test/rich-results - Tests validity and shows which rich results you might get
• Schema.org Validator - validator.schema.org - Validates against the schema.org specification
• Google Search Console - Shows schema errors and which types are being detected across your site

Test after every change. Invalid schema is worse than no schema because it confuses rather than clarifies. Result = being ignored

To give you a concrete example, here's how we've structured schema across our site:

• Homepage: Organization, LocalBusiness, WebSite
• About page: Organization (extended), Person schemas for team members
• Service pages: Service schema for each service, linked to Organisation
• Blog posts: Article schema with author and publisher references
• FAQ sections: FAQPage schema on relevant pages
• How-to content: HowTo schema for instructional articles

The result is an interconnected entity graph that AI systems can traverse. They understand not just what SuperHub is, but who runs it, what we do, what we've written, and how it all connects.

If you're short on time or resources, prioritise these:

1. Organization schema - Foundation of your entity identity
2. LocalBusiness schema - Critical for local and service-area queries
3. FAQPage schema - Direct fuel for AI Q&A responses

Everything else builds on these. Get these three right, and you've covered the fundamentals.

Schema markup is technical implementation. It works best when combined with content structured for AI citation and authority signals that establish credibility. But it's also the foundation that makes everything else work better.

If you want help implementing comprehensive schema as part of an AI search optimisation strategy, book a call and we'll audit your current setup and show you what's missing.