Photo by Markus Spiske on Pexels
Photo by Markus Spiske on Pexels
Microdata embeds structured attributes directly inline in HTML tags, which forces a parser to reconstruct meaning from a tangle of nested elements, a task original human-authored pages rarely execute cleanly and one large language models handle inconsistently. JSON-LD instead sits as a single self-contained script block, a clean object graph a model can ingest without untangling markup at all, which is precisely why every major AI assistant and knowledge-graph pipeline treats it as the preferred structured-data channel. This is not a stylistic preference; it is a parsing-reliability difference. A business described only through scattered microdata attributes is frequently read partially or not at all, while the same facts expressed as JSON-LD, entity by entity, are ingested whole. Done-for-you structured data work means auditing existing microdata, consolidating it into accurate JSON-LD blocks covering organization, offerings, hours, and location, and verifying the model actually reflects those facts back correctly when asked.
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Keep both only if they agree exactly; conflicting values in each format confuse parsers about which is authoritative. If you cannot guarantee they stay in sync, retire the Microdata and let JSON-LD be the single source of truth.
Most parsers read JSON-LD regardless of placement since it is a self-contained script block, but head placement ensures it renders before scripts fail or timeouts occur, which matters most for crawlers with limited render budgets.
Organization, LocalBusiness (or its subtype), Service, and FAQPage carry the most weight for AI question-answering, because they map directly onto the entity facts and buyer questions models are asked to resolve.
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