Most guidance on schema markup, including our own, focuses on what it does for Google: richer listings, star ratings, FAQ panels. That’s still true, but it’s no longer the whole picture. A growing share of legal research now happens through AI-powered tools, Google’s AI Overviews, and search using Claude, ChatGPT, Perplexity, Microsoft Copilot or other LLMs; and schema markup plays a distinct role there too.
This post looks specifically at that role. For a full walkthrough of schema types with real-world examples, see our earlier post on schema markup and richer Google listings.
Why AI search relies on structured data
Traditional search engines crawl and rank pages, then leave interpretation to the person reading the results. AI search tools do more of the interpreting themselves: they extract facts from across the web and stitch them into a generated answer, often (hopefully) citing sources as they go.
That shifts what a page needs to communicate. It’s no longer enough to be well-written and relevant, the AI system has to resolve ambiguity quickly and correctly. Is this person a barrister or a solicitor? Which chambers or firm are they part of? Is a practice area the whole of the firm’s work, or one of several? Schema markup answers these questions directly, rather than leaving the AI to infer them from prose or page layout.
Google’s own guidance confirms structured data supports newer AI-driven features, including AI Overviews, alongside classic rich results. The same principle extends to other AI tools that draw on the open web: content that clearly labels what it is and who it’s about gives any AI system a better chance of representing a firm correctly.
What this doesn’t mean
Schema markup improves the odds that AI tools understand and describe a firm accurately. It doesn’t guarantee a citation or a mention, and exactly how much weight each AI system gives structured data, relative to backlinks, third-party mentions and overall content quality, isn’t fully public. It’s one part of a wider visibility strategy, not a shortcut around one.
Where it matters most for AI search
Two schema types carry particular weight for AI search, on top of the value they already bring for Google:
- Person schema, because a growing number of AI search queries are effectively “who specialises in X” or “recommend a barrister for Y”. Clear, structured profiles give the AI a direct answer to draw on rather than a biography it has to interpret.
- FAQ schema, because they presents ready-made question-and-answer pairs, close to the exact format a conversational AI tool needs to generate a response. Well-written, genuine FAQs give these tools something clear and quotable.
Organisation, Service, Event and Review schema all still contribute, mainly by helping an AI system correctly identify which firm it’s dealing with and reducing the chance of confusing similarly named firms or individuals.
The takeaway
Schema markup won’t guarantee a mention in an AI-generated answer any more than it guarantees a rich Google result. What it does is remove ambiguity, giving AI systems a clear, structured account of who a firm is, what it does, and who its people are. As AI search tools take up more of the space traditionally occupied by a search results page, that clarity is becoming a meaningful part of a firm’s visibility, not just a technical nicety. If you would like any help with setting this up, email us at [email protected].
