Voice search and AI assistant queries differ from typed search in pattern but not in substance. The prompts are conversational (‘what time is the company headquartered,’ ‘who is the CEO of [Brand]’), the expected response is a clean spoken answer rather than a list of links, and the selection mechanics favor content that is structured to be lifted. FAQPage schema is rewarded heavily because the question-answer pairs are explicit. Definitional content (a clear two-sentence ‘what is X’ answer) wins voice selection across most assistant platforms. Structured how-to content with HowTo schema gets selected for procedural queries. Underneath all of it, strong entity signals (Wikidata, Knowledge Panel, consistent attributes) are what let the assistant identify the right answer source in the first place. The discipline is closely related to AEO and to the writing-for-the-extract approach we apply across all engines.

An entity that the AI engines recognize and trust shows up consistently across responses with the same facts, the same relationships, and the same context. Building one is a layered job. Wikipedia is the keystone for any entity that meets Notability standards, because of how heavily the engines weight it. Wikidata is the structured-data twin, machine-readable and queried directly by the engines for entity facts. Schema markup on owned properties (Organization, Person, Article) with proper sameAs links to those canonical sources ties the entity together across the web. Authoritative third-party citations – mainstream press, industry registries, regulatory pages – add corroboration. Consistency across all of these is what produces engine confidence: same name, same affiliations, same dates, same relationships everywhere. The work compounds. An entity built deliberately over six to twelve months looks materially different in the engines than one that emerged ad-hoc.

Entity optimization is the technical and editorial discipline that makes a brand or person recognizable as a single coherent entity across the systems the AI engines depend on. The components are concrete: a Wikipedia article when Notability supports one, a complete Wikidata entry with sourced statements, schema markup on owned properties using Organization or Person types with sameAs links to the canonical identifiers, consistent name and affiliation conventions across the web, and authoritative third-party sources that reinforce the same entity facts. When the work is done well, an AI engine asked about the entity returns consistent answers across prompts and across engines, because the underlying entity infrastructure is unambiguous. When the work is missing, the engines guess – and the guesses produce the confusion, conflation, and inconsistency that send CCOs looking for help.