The two schema markup types that most directly improve executive AI citation presence are Person schema and Article schema. Both are implemented as JSON-LD blocks in the HTML head of relevant pages and are the standard structured data implementation recommended by Google.
Person schema belongs on the executive's author page. It declares the executive's full name, job title, employer, areas of expertise, and links to their profiles on other indexed platforms via the sameAs property. The sameAs links are particularly valuable: they tell AI engines and crawlers that the person described on this page is the same individual at each linked URL, building a connected identity graph that attributes content across multiple domains to a single named individual.
Article schema belongs on every article the executive publishes on domains they control. The author property within Article schema should point to the executive's Person schema record or contain a nested Person object. This creates an explicit machine-readable declaration of authorship that is more reliable than a byline in plain text.
FAQ schema is useful for FAQ-structured articles. It marks up each question-answer pair explicitly, making those pairs directly extractable by AI engines and improving eligibility for Google AI Overviews.
Implementation requires placing JSON-LD blocks in the page head section. Most modern content management systems support this natively or through plugins. After implementation, verify correctness using Google's Rich Results Test at search.google.com/test/rich-results.
Without schema markup, AI engines infer authorship and topical relationships from text. With schema markup, those relationships are declared explicitly. The difference is measurable in attribution confidence and citation reliability.