How do you prepare for voice search and AI assistants?

Voice search and AI assistant queries share a structural demand that distinguishes them from standard typed search: the engine needs a single, speakable answer rather than a list of links to evaluate. Every content and entity choice that follows should serve that goal, making the right answer obvious, attributable, and easy to extract.

Step 1: Mark up Q&A content with FAQPage schema

FAQPage schema marks question-answer pairs as explicitly machine-readable, allowing AI models and assistant platforms to identify the content as structured Q&A and extract it cleanly. Google Search Central confirms that properly marked-up FAQ pages “may be eligible to have…an Action on the Google Assistant,” directly connecting the markup to assistant selection. The Schema.org FAQPage type defines the page as one presenting one or more “Frequently asked questions”, a signal the engine can act on without further interpretation.

Note: Google retired FAQ rich results in 2026, so FAQPage markup no longer produces a visual accordion in the standard Google SERP. The markup remains valuable because it makes page structure machine-readable for AI models (including Google’s own AI Overviews) and for other assistant platforms that did not adopt the same policy.

Step 2: Front-load concise definitional answers

Voice assistants and AI engines resolve entity and definitional queries by retrieving the clearest, most authoritative passage that directly answers the question. Placing a clean two-to-three-sentence answer at the top of a page on a topic, before the surrounding context, reduces the interpretive work the engine must do. Google’s featured-snippet and AI Overview systems use closely related selection logic, favouring clean extractable answers supported by source authority; pages that satisfy this criterion tend to perform across multiple AI answer engines at higher citation rates. The specific claim that definitional content “wins” these slots unconditionally is not independently sourced; the mechanism (extractability combined with source authority) is supported, but placement alone is not a guarantee.

Step 3: Use HowTo schema for procedural queries

Procedural voice queries (“how do I…”, “what are the steps to…”) are a distinct query pattern. Structuring the answer as numbered steps and applying HowTo schema allows AI engines to extract the procedure cleanly: Schema.org defines HowTo as “instructions that explain how to achieve a result by performing a sequence of steps,” and Google Search Central confirms that structured data is “a standardized format for providing information about a page and classifying the page content.” Whether HowTo schema is specifically selected by voice assistant platforms at higher rates than plain prose is not independently supported in the research; the extractability mechanism is the supportable basis for the recommendation.

Step 4: Build strong entity signals (Wikidata and Knowledge Panel)

Before an AI assistant can select a piece of content as the right answer, it must identify the entity the query is about. That identification relies on machine-readable entity signals. Wikidata is a free, collaboratively edited multilingual knowledge graph that “allows digital assistants (Siri, Alexa, Google) and bots to add and pull information from Wikidata and present it to their users.” A Google Knowledge Panel is “a primary source for direct answers” in voice search, not merely a search result. Ensuring the entity’s Wikidata entry is accurate and complete, and that the Knowledge Panel reflects current facts, is the foundational layer that makes the content-level steps above effective.

Step 5: Connect the pieces with schema sameAs links

Schema markup on the site itself (Organization, Person, Article types) can carry sameAs links pointing to the entity’s Wikidata entry, Wikipedia article, and official profile pages. This ties the entity together across the web and connects it to the canonical identifiers AI engines use for disambiguation. The Schema.org sameAs property is defined as a “URL of a reference Web page that unambiguously indicates the item’s identity,” citing Wikipedia and Wikidata as explicit examples. Taken together with accurate Wikidata and Knowledge Panel data, this gives the assistant a coherent, cross-referenced picture of the entity when resolving a query about it.