Evaluating enterprise voice AI requires looking beyond voice quality to examine verifiable task completion, context retention, real-time adaptation, security, latency, and system integration. This guide provides the operational framework required to evaluate AI voice agents for enterprise use.

Very few conversational AI systems survive a live, unscripted phone call where the IVR menu is unfamiliar, the hold music runs eight minutes, and the human on the other end interrupts mid-sentence.

Our last two posts covered what breaks — real incidents like McDonald's drive-thru pilot and the Air Canada tribunal case, and the technical failure points inside a live voice pipeline. This post is about the fix, and it's more structural than most teams expect: the problem isn't that language models occasionally get things wrong. It's that most systems never built anything to catch it when they do.

McDonald’s and Air Canada show what happens when companies ask one probabilistic AI model to manage deterministic business rules like orders, policies, and refunds.