Council Post: The AI Accuracy Conundrum

AI accuracy isn’t a single metric, and it’s increasingly landing on executives’ desks—from board meetings and audits to customer complaints and regulator questions. The article argues that “95% accurate” is often meaningless because accuracy varies by AI type. For conversational and voice systems, leaders should track factual accuracy against verified test sets, hallucination rates, and for voice add word error rate alongside task-completion accuracy. Retrieval and knowledge systems should be evaluated on retrieval relevance, faithfulness to sources, and citation accuracy, since small retrieval errors compound. Document and image intelligence requires scoring by content type and measuring handoff-to-humans for hard cases.