How Big Is Your AI Hallucination Risk?

AI hallucination risk is the chance that an AI system produces a confident but false answer, and that the error reaches a decision, a document, or a customer before anyone catches it. Two things drive it: how consequential the output is, and how tightly the model is grounded in verified source material. This assessment scores both.

It scores five dimensions — current accuracy issues, accuracy requirements, use-case sensitivity, current controls, and knowledge management — across 14 multiple-choice questions in about four minutes, then sends a risk report naming the specific gaps it found.

Every answer carries a weighted point value. Current accuracy issues and accuracy requirements count for 25 percent each, use-case sensitivity for 20 percent, and current controls and knowledge management for 15 percent each. The weighted total produces a 0 to 100 score that lands in Low Risk (0 to 40), Moderate Risk (41 to 70), or High Hallucination Risk (71 to 100).

Teams working in regulated content — medical, legal, financial — and teams whose AI output reaches customers directly, especially where a generic model answers without being connected to a verified knowledge base and without a required verification step. The enterprise hallucination rate runs near 20 percent, or roughly one error in every five queries, as documented in The AI Strategy Blueprint, Chapter 14.

The report names the weakest dimensions and the controls that close them: grounding AI in verified internal knowledge, requiring a source citation on every claim, and putting a verification step before output is used. For the technical account of why naive chunking produces errors, and the 78x accuracy improvement measured in an independent Big Four consulting firm evaluation of Blockify, see the AI hallucination data problem. To size the exposure in dollars first, run the AI hallucination cost calculator.