A citation event is a retrieval artefact. The engine assembled an answer for one prompt in one context and named the pages it leaned on. The source is careful about the limit: a citation is evidence of what the system surfaced for that user, not proof of what the model was trained on, and it does not expose the ranking or retrieval decisions behind the answer [8].
So read the 30 million events as a table about someone else's query mix [1]. For the ordering to describe your buyers, two things would have to hold: their prompts would have to resemble the ones sampled, and your category's discussion would have to already live on the platforms that scored. The study does not claim uniformity. It says the mix differs by engine and query context [5], and the article gives the split it observed: Google AI Mode and AI Overviews surfacing more social material including Facebook and Yelp, ChatGPT drawing more on Forbes, TechRadar and Wikipedia in some contexts [10].
Then there is the table itself. The list published as the top 10 domains carries seven names: YouTube, Wikipedia, Forbes, G2, Yelp, Medium and TechRadar [6]. Ten minus seven leaves three entries unaccounted for, and neither Reddit nor LinkedIn appears in the list at all, though both are the finding the piece is built on [7]. That gap matters because missing rows in a domain ranking usually hide the entries most relevant to the argument being made.
The operational split is where the engineering sits. Of the three governance areas the article recommends, exactly one is a deploy: owned information, meaning product pages, help content, case studies and factual company descriptions kept current and internally consistent [12]. The other two are surveillance. Community visibility means tracking recurring questions and misconceptions on Reddit, YouTube and LinkedIn [12]. Attribution and accuracy means checking whether AI answers cite relevant sources and describe the company correctly [12]. A thread you did not write has no build step you control, and one that aged badly has no rollback you can trigger.
That is why the recommended method is a prompt suite rather than an audit: assemble realistic customer prompts around the problems you solve and the alternatives buyers consider, run them across the platforms your audience uses, and record which domains and descriptions come back, repeatedly [11]. The value is not the absolute ranking. It is the diff against your own previous run, on prompts you chose, because the published sample is a baseline someone else drew.
Where this bites hardest is the case the source names: purchases that depend on explanation rather than a simple transaction, such as software, professional services and technical products, where buyers ask about setup, alternatives, pricing models and limitations [13]. In my context that leaves the docs as the highest-leverage work, because they are the only input in the chain I can version. That makes the remaining work monitoring, not the marketing task it resembles.
