AI Voice Agents vs. Human Agents: Metrics Compared
Blog post from Deepgram
Conventional contact-center metrics such as average handle time, containment, first-contact resolution, CSAT, and human-oriented QA scorecards do not cleanly evaluate AI voice agents because they can reward fast but incorrect responses, treat un-escalated calls as resolved, overlook routing differences, and miss confident misunderstandings. The discussion argues that AI evaluation should supplement operational measures with voice-native indicators including contained-call quality reviews, appropriate escalation timing, latency, interruption handling, speech-recognition accuracy for critical details, and patterns in failures across accents, intents, or times. AI agents are presented as effective and lower-cost for repetitive, structured requests such as scheduling, account inquiries, and order updates, while human agents remain better suited to ambiguous, emotionally sensitive, or exception-based cases requiring judgment. It concludes that organizations need call-level measurement and direct testing of speech and turn-taking systems to determine when AI genuinely resolves an issue and when human involvement is necessary.
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