My first article for Search Engine Land is live. It’s about treating schema markup as infrastructure for machine understanding rather than a trick for chasing citations.
The short version: schema is how you teach search engines and LLMs what your brand actually is. By declaring your entities and their relationships, you build a knowledge graph, and that graph becomes a benchmark for identifying gaps in how machines understand you. In the article, I get into where Schema.org falls short, what the research actually says about schema and AI visibility, and how to prioritize the gaps that matter.
I go deeper on the framework, real-world examples, and Google’s entity patents in the full article. Read it on Search Engine Land: Schema for AI search: How to identify and prioritize entity gaps.
