Full Answer

AI shopping agents work from machine-readable fields, not your page copy. A Product schema block carries name, price, currency, availability, ratings, and identifiers like GTIN in JSON-LD, and the agent matches a shopper's request against those fields directly. When the markup is missing, invalid, or out of sync with your live catalog, the agent either skips the listing or quotes stale numbers — Globe Runner's 2026 analysis of structured data in AI search found schema is now read as a trust signal, not a formatting nicety.

Product markup is only half the picture. FAQ and Article schema around your products gives agents citable answers — why one model suits a use case, how sizing runs, what the returns policy says — and that is the material AI actually quotes when it recommends. Recommendations also increasingly happen off-site: an agent can carry a shopper from question to checkout with barely a session in your analytics, as our piece on the AI-agent data collapse in GA4 shows.

The question isn't whether to add structured data anymore. The question is how complete and how current yours is. Validate your Product schema, keep price and availability synced automatically rather than by hand, and build the surrounding answer content in schema-marked form — tools like Cherry Tree generate FAQ and article content with markup designed for AI-engine citation, one way to build that layer without writing it all manually.