Abstract
This paper examines how generative search systems change the way information is discovered and surfaced. Instead of returning ranked link lists, systems such as ChatGPT, Perplexity, Copilot, and Claude synthesise answers from retrieved or model-accessible sources.
Authors
AAI Labs
What the study evaluates
The study analyses how generative systems retrieve, cite, summarise, and prioritise information. It also develops strategies and measurement tools for understanding how organisations appear inside AI-generated answers.
Main findings
Generative visibility differs from traditional search visibility. A page can rank well in conventional search and still be absent from generated answers if it is not easily retrievable, semantically explicit, or trusted by the synthesis layer.
The paper argues that optimisation for generative search should focus on clear entity signals, source credibility, retrievable factual structure, and measurement methods that reflect how answer engines actually compose responses.
Why it matters
As users shift from search result pages to answer engines, organisations need new ways to audit and improve their visibility. This work provides a practical foundation for measuring that visibility and adapting content strategy accordingly.