An AI assistant names some and omits the rest. What decides that is measurable.

Xato · CC0

WhitepaperBA-W-2026-012026

Measuring AI visibility: statistical requirements and common failures

A brand's visibility in AI assistants is routinely 'verified' with a single screenshot or one daily query. This paper argues such verification is not measurement. Answer engines are stochastic and their retrieval changes continuously: lightly rewording a query while holding its intent fixed cut the overlap of the brands an assistant recommended to a Jaccard similarity near 0.3 — far below the 0.50–0.61 overlap of a plain re-run — and an identical prompt re-issued a day later overlapped only 34–42% in cited sources and 45–59% in mentioned brands. A single observation of a moving distribution estimates nothing. The paper enumerates what voids a visibility claim — no sample size, no interval, no window, no engine version, one fixed phrasing, uncontrolled personalization, discarded refusals — and shows a three-sigma jump can be pure drift. It then states what valid measurement requires — repeated sampling to a declared precision, bounded intervals near the extremes, a phrasing distribution, partial pooling, explicit windows and versions, change-point monitoring, recorded refusals — specified in BA-C-2 and BA-C-3.