Obility Editorial · · 4 min read
One question becomes many searches
A page can rank first for the phrase a buyer types and still be missing from the AI answer to that same question. The usual reason is that the answer was never built from a search for that phrase alone.
Google says so directly. Its Search Central guide to AI features states that AI Overviews and AI Mode may use a “query fan-out” technique, issuing multiple related searches across subtopics and data sources to develop a response. Google’s May 2025 AI Mode announcement described the same step: the question is broken into subtopics and a multitude of queries are issued at once on the user’s behalf. The same announcement said Deep Search in AI Mode takes the technique further and can issue hundreds of searches.
OpenAI describes a similar pattern in its developer documentation for the web search tool. Reasoning models can search as part of their chain of thought, analyse the results and decide whether to keep searching, and each search action usually records the queries it ran. That documentation covers the API rather than the ChatGPT app, but the idea carries over: the engine decides what to look up, and your head keyword is only one of the things it might look up.
Your ranking page competes in a different pool
Picture a buyer asking which payroll software suits a 40-person company with staff in two countries. An engine that fans out might look separately for multi-country payroll support, pricing for small teams, accounting integrations, complaints about specific vendors and local compliance rules. Those sub-questions are our illustration. Google does not publish the queries it generates for a given answer.
Each of those searches returns its own results. The page that ranks for “best payroll software” may be a broad listicle that says little about paying staff abroad. A vendor’s help page on running payroll in a second country may never rank for the head term and still be the strongest result for one sub-question. When the answer is written, it cites the pages that supported specific sentences, which is how a page outside the top results for the typed phrase ends up as a source.
This also explains why reading and citing diverge. OpenAI’s documentation separates the full list of URLs the model consulted from the inline citations shown in the answer, and says the number of sources is often greater than the number of citations. Being retrieved puts your page in the reading pile. Being cited requires a passage that supports a claim the answer actually makes.
Map the sub-questions before you write anything
The practical response is to plan against the sub-questions rather than the head term. Take one buyer question that matters commercially and write down the separate facts a careful researcher would need to answer it: what the product does, which companies it fits, how pricing is structured, what it connects to, where it falls short, and how it compares with the obvious alternatives.
Then read the answers engines already give. The cited sources are the best evidence you have about which sub-questions an engine pursued, because each citation is attached to a claim. Group the citations by the sub-question they support. Where a competitor’s page or a third-party review is cited for a fact about your category, you have found a sub-question you answer poorly or not at all.
For each gap, decide which existing page should carry the answer before creating a new URL. Often the fix is a clearly headed section on a product or help page that states the fact plainly: which countries you support, what pricing is based on, what you do not do. A fact that lives only in a sales deck or a gated PDF is not available to a search that needs it.
Obility’s Answer Engine Insights is organised around this review. Prompts are grouped by audience and buying stage, and the surrounding answer, the competitors it recommends and the sources it cites are read together, so each gap can be traced to the claim it affects and given an owner.
Some fan-out advice goes further than the evidence
A lot of what circulates under this label outruns its sources. Posts quote a specific number of sub-queries per AI Mode answer, but Google’s announcement gave no count for AI Mode, and a number you cannot observe is a poor thing to plan around. Tools that claim to show Google’s fan-out queries are generating plausible queries of their own. That can help with brainstorming, as long as the output is labelled as a guess.
The other common recommendation is special markup. Google’s AI features guide says there are no additional requirements to appear in AI Overviews or AI Mode, that you do not need to create new machine-readable files, AI text files or markup, and that there is no special schema.org structured data to add. Clear headings and direct answers still help, since they make a passage easy to find, lift and check.
Covering sub-questions earns eligibility, not a citation
Answering more sub-questions increases the number of searches your pages can compete in. It does not mean you win any of them, and Google’s guide notes that meeting every requirement does not guarantee a page will be crawled, indexed or served. The engine still chooses among the pages it retrieved, and for questions about reputation or comparison it may reasonably prefer a third party over the vendor.
Measurement needs the same care. Google counts AI Overviews and AI Mode inside the overall Web search type in Search Console’s Performance report, so clicks from these answers are blended with classic results. Record the buyer question, the sub-questions you mapped, the page you changed and the date, then compare the cited sources on repeat observations. If the pages cited for a sub-question start to include yours, that is evidence. A rise in impressions for the head term on its own is not.