People also ask
In practice
Traditional research optimised for the typed query; AI research optimises for the asked question — longer, contextual, specification-rich (“best CRM for a 10-person agency using HubSpot”) — and the discovery sources shift accordingly: forum threads and Reddit (where buyers ask each other in prompt-like prose), sales transcripts, PAA, Perplexity’s and ChatGPT’s related-question surfaces, and emerging prompt-volume tools that estimate usage in AI chats — directional numbers, useful for prioritising, not comparable to search volume.
The intent shapes differ too: prompts skew comparison-heavy and specification-heavy — the buyer brings context the engine can use — which makes the long tail wider and the head terms less dominant than in classic search. The practical output mirrors keyword research’s: a prompt inventory clustered by intent (definition, comparison, ranking, recommendation), prioritised by relevance and estimated usage, mapped to pages that should win each cluster — and a measurement layer, because prompt “rankings” are unstable and require longitudinal presence tracking rather than a position check.
The strategy consequence for content: pages that answer prompts in quotable prose — direct answers, structured comparisons, current data — win the retrieval layer; and the placement consequence: the pages that currently get cited for each cluster are the target list, discovered by running the prompts and recording the citations. The two research layers converge rather than compete: search keywords still drive the index’s traffic, prompts drive the answer layer, and the same buyer sits behind both.
See also: Prompt research, AI search visibility monitoring, Search intent and link intent mapping.
Related service: Brand mentions.
