Most contact centers have deployed AI agents without establishing governance frameworks to measure their performance against human agents. The CARES framework—Compliance, Accuracy, Resolution, Effort, and Sentiment—provides a unified standard for evaluating both workforces on the same scale, moving beyond the misleading metrics that currently dominate dashboards. Traditional containment rates and cost-per-contact figures obscure what actually matters: whether customers return within 72 hours with the same issue, whether agents stay within scope, and whether they recognize when to escalate. The critical gap emerges when AI agents sound equally confident whether correct or incorrect, meaning escalation competence must be actively measured rather than assumed. For teams already running Agentforce or similar platforms, this raises an uncomfortable question: are your current dashboards actually showing you customer resolution, or merely interaction resolution? A contained call that generates a callback three days later appears successful on traditional metrics but represents delayed cost and churn risk.
The implications for CX leadership are substantial. Scoring AI and human agents against identical dimensions reveals that no universal verdict on AI performance exists—only intent-specific verdicts. A password reset may be better handled by AI whilst billing disputes require human judgment, yet most organizations lack independent oversight to make these determinations. Vendors providing both the AI agent and the CCaaS platform have inherent conflicts of interest, whilst relying solely on vendor-supplied performance data mirrors allowing a home builder to conduct their own inspections. Teams should begin with one high-volume, unambiguous intent, verify whether they can currently track 72-hour callbacks, then score both workforces across all five dimensions to identify which agent type genuinely wins that specific work. This shifts AI deployment from a binary buy-or-don't decision into a managed, intent-by-intent allocation strategy that optimizes outcomes rather than costs.
5 dimensions for analyzing human and AI agent performance CX Network