My second article for Search Engine Land is live. The first one laid out how to use schema and knowledge graphs to find entity gaps. This one is the follow-up everyone asked for: what happens when you actually close them.
I walk through two university partners, a multi-campus international business school and a regional online nursing program, that both started at roughly 1% share of voice in AI answers. We audited each against our 85-entity model, sorted what we found into legible, ambiguous, and unverifiable, and fixed what the institutions could stand behind. One partner needed schema corrections. The other needed a content refresh. Both saw conversion quality improve, and the citation and lead numbers are in the article.
The honest takeaway: markup alone doesn’t guarantee growth, and entity reconciliation is a moving target you revisit, not a one-time project.
The full case study, including the before-and-after entity fixes and the data tables for both partners, is on Search Engine Land: Schema for AI search: Turning entity gaps into organic growth.