Structured data added to webpage HTML that helps search engines and AI understand exactly what the content means. Enables rich results and powers AI answer generation.
Schema markup is structured data added to a webpage's HTML that provides explicit, machine-readable information about the page's content — what it is, who created it, what it describes, and how it relates to other things. Schema is defined by Schema.org, a vocabulary maintained by Google, Microsoft, Yahoo, and Yandex. It's most commonly implemented using JSON-LD format, which Google recommends.
Schema markup helps search engines move from understanding that a page contains certain words to understanding what those words mean. A page about a plumbing business can describe itself as a LocalBusiness with specific services, hours, service area, price range, and reviews — not just text containing those words. This explicit structured understanding is what powers rich search results (review stars, FAQ dropdowns, business info panels) and increasingly, AI-generated answers.
For business websites, the most impactful schema types are: Organization (company identity and contact information), LocalBusiness (location, hours, service area), Service (what you offer), FAQPage (question-and-answer content), BreadcrumbList (page hierarchy), and Article (blog posts). Each type signals specific information to search engines and AI systems that synthesize answers from multiple sources.
The role of schema markup has grown significantly as AI-powered search (Google SGE, Bing Copilot, Perplexity, ChatGPT) has become widespread. These systems rely heavily on structured data to accurately represent businesses in generated answers. A page with comprehensive, accurate schema is more likely to be cited, quoted, and linked when AI systems answer relevant questions — making schema markup one of the core GEO (Generative Engine Optimization) strategies.