Here’s a pattern that appears across client work consistently: the page that earns the most AI citations for a commercial term is often not the commercial page. It’s the page covering a related, non-promotional topic with genuine depth — and the commercial citation follows from the authority that depth creates.
The principle has a name: semantic adjacency. Covering the topic adjacent to a commercial query with genuine depth earns topical authority for the commercial term that sits beside it. The AI system that has learned to trust your content on the adjacent topic extends that trust when the commercial query arrives.
The Operation Soteria example
A criminal defence firm published detailed, genuinely useful content about Operation Soteria — the national policing approach to rape and serious sexual offence investigations. Not promotional. Not commercial. A resource for people navigating the process, written with real legal knowledge behind it.
That content established the firm as an authoritative source on sexual offence prosecution procedure. When AI systems subsequently received queries about sexual offence solicitors, the corroboration trail led back to a firm that had demonstrated deep, independent knowledge of the subject — not just a page optimised for the keyword “sexual offence solicitor”. The citation followed the authority, not the optimisation.
This is semantic adjacency in practice. The adjacent topic created the authority signal. The commercial term benefited from it.
Why this happens mechanically
AI systems retrieve across the full body of content they can access from a domain — not just the page that matches the query. When a domain has demonstrated consistent, depth-first coverage of topics adjacent to a commercial query, the model builds a higher confidence score for that domain on the related commercial term. Superficial coverage of many topics produces weak signals. Deep coverage of adjacent topics produces strong signals that radiate outward.
This is also why thin content strategies consistently fail in AI citation contexts even when they rank. Ranking is a page-level signal. Citation authority is a domain-level and entity-level signal. A page optimised for a keyword doesn’t generate domain-level topical authority. A body of genuinely useful adjacent content does.
The commercial consequence
The implication for content strategy is direct. Before asking “what commercial terms should we target”, ask “what adjacent topics would genuinely benefit our audience and demonstrate our depth of knowledge”. The content that answers the second question is often more valuable for AI citation than the content that answers the first.
This is also why the law firm content I produce for clients — detailed practice area content, procedural guides, regulatory analysis — consistently outperforms the straightforward “services” pages for AI citation. The services pages state what the firm does. The practice area content demonstrates that they understand the subject at a level that generates trust. Trust is what produces named recommendations.
How to identify your adjacent topics
Map the journey your ideal client takes before they need you. What questions do they have before they know they need legal representation, or IT infrastructure, or SEO consultancy? What do they need to understand before the commercial decision? The content that answers those pre-commercial questions is your semantic adjacency opportunity.
For a managed file transfer vendor, that might be content about secure file transfer protocols, compliance requirements, or enterprise automation architecture — not just “buy our MFT software”. For a criminal defence firm, it might be content about the legal process, what to expect at each stage, and how decisions get made — not just “contact us for representation”. The adjacent content builds the authority that the commercial content can’t build alone.
Semantic adjacency is one of the four criteria in the third-party citation filter of the AI Citation Dominance framework. The full strategic model — retrieval, extraction, and corroboration — is there. The AI Discovery Stack maps where semantic adjacency fits in the five-layer visibility architecture.
