As large language models replace traditional search engines as primary discovery interfaces, businesses face a fundamental measurement problem. LLM outputs are stochastic. The same query submitted multiple times produces different vendor sets, different framings, and different recommendations, making ranking-based metrics structurally inapplicable.

We introduce a four-dimensional probabilistic visibility model, ISIC (Inclusion, Stability, Influence, and Coverage), as the replacement measurement framework for AI-driven discovery. Across 27 queries spanning 9 problem areas and 81 GPT-4o responses, results indicate an average Jaccard overlap of 0.03, with 19 of 27 queries showing zero overlap between LLM and Google results, indicating that these two discovery channels direct buyers toward fundamentally different resources.

AEO measurement probabilistic visibility LLM citation bias B2B discovery ISIC framework GPT-4o