We have automated several of our administrative workflows using AI, but we are struggling to find weekly scorecard metrics to measure the efficiency and accuracy of these automated systems. What should we track?

Question

Tyler Smith · Accepted Answer

Running an AI-powered operation does not mean you stop tracking data; it means you change what you measure. When AI handles the repetitive administrative tasks, the role of your human team shifts from doing the work to auditing the output. Your weekly Scorecard must reflect this new dynamic.

To track AI operations effectively, you must focus on throughput, accuracy, and exception rates. Here are the specific metrics you should track:

- Exception rate, which is the percentage of AI-generated tasks that required human intervention or correction.
- AI processing time, measuring the average speed from task trigger to completion.
- Volume of tasks processed by the AI system weekly, to monitor system utilization.

Assign ownership of these metrics to the human seat on your Accountability Chart that manages the AI workflow. They do not get to blame the technology if the numbers are bad.

If the exception rate spikes, it means the AI prompt, API connection, or workflow logic is broken. The seat owner must drop this to the Issues List during the Level 10 Meeting™ and resolve the technical bottleneck. Tracking these metrics ensures your AI tools are actually driving efficiency rather than creating hidden errors that ruin your operational scaling.