From SEO to AI: The Expanding Role of Structured Data
JSON-LD (JavaScript Object Notation for Linked Data) and Schema.org were created in 2011–2012 as a collaboration between Google, Bing, Yahoo, and Yandex to standardize how websites communicate structured information to search engines. A webpage about a restaurant could include JSON-LD markup specifying its name, address, cuisine type, opening hours, and price range in a machine-readable format, enabling rich search results.
For a decade, structured data was primarily an SEO tool. In 2026, it is becoming something more fundamental: the primary mechanism by which AI systems understand, cite, and reason about web content. Large language models are trained on web-scale text, but they struggle to reliably extract structured facts from unstructured prose. JSON-LD provides those facts in a format that is unambiguous, machine-readable, and directly consumable by AI systems.
The evidence for this shift is accumulating. Microsoft's guidance for Bing Chat explicitly recommends structured data markup for AI visibility. Google's Search Generative Experience preferentially cites pages with structured data. Perplexity, Claude, and other AI assistants are increasingly using structured data to extract facts for citation. The ROI of structured data has shifted from "better rich snippets" to "AI system visibility."
JSON-LD Fundamentals for AI
JSON-LD is a method of encoding Linked Data using JSON. It uses a context (the @context key) to map JSON property names to IRIs (Internationalized Resource Identifiers) from a vocabulary like Schema.org, and a type (the @type key) to specify what kind of thing is being described. A JSON-LD block embedded in a webpage's