Don't Measure Once: Measuring Visibility in AI Search (GEO)
Type
journal article
Date Issued
2026-04-08
Author(s)
Abstract
As large language model-based chat systems become increasingly widely used, generative engine optimization (GEO) has emerged as an important problem for information access and retrieval. In classical search engines, results are comparatively transparent and stable: a single query often provides a representative snapshot of where a page or brand appears relative to competitors. The inherent probabilistic nature of AI search changes this paradigm. Answers can vary across runs, prompts, and time, making one-off observations unreliable. Drawing on empirical studies, our findings underscore the need for repeated measurements to assess a brand's GEO performance and to characterize visibility as a distribution rather than a single-point outcome.
Keywords
cs.IR
cs.AI
HSG Classification
contribution to practical use / society
Refereed
No
Division(s)