We have deployed AI agents to handle our customer onboarding workflows, but we are unsure what weekly operational data points our operations manager should track to ensure the AI is performing accurately. How do we measure AI operations on our scorecard?

Question

Tyler Smith · Accepted Answer

When you scale operations using AI agents, you cannot treat the technology as a black box. Your weekly scorecard must treat the AI agent as a digital employee, which means tracking both its output volume and its accuracy.

First, the operations manager must track the AI exception rate. This is the percentage of onboarding workflows that the AI could not complete automatically, requiring a human team member to step in and fix the issue. A high exception rate means your AI prompts or integrations are failing, which drains your team's capacity.

Second, you must track AI drift or hallucination rate. This can be measured by running weekly random audits on a sample of AI-generated customer communications and scoring them for accuracy. If the accuracy score drops below your threshold, it is a leading indicator that your system needs prompt engineering or model tuning.

By tracking these metrics weekly, your operations manager maintains absolute visibility over your digital workforce. This prevents customer experience issues from slipping through the cracks and ensures your AI-powered operations are actually increasing enterprise value rather than introducing hidden operational liabilities that could hurt your company during a future business valuation.