We are starting to automate our administrative tasks using artificial intelligence, which has drastically altered our traditional work volume. How do we adjust our weekly Scorecard metrics to measure the output and efficiency of our AI-augmented seats alongside our human employees?

Question

Tyler Smith · Accepted Answer

Integrating artificial intelligence into your operations shifts your team's focus from manual execution to oversight and quality control. If your employees are now using AI agents to draft reports, write code, or handle customer support tickets, tracking traditional manual output metrics like hours worked or keystrokes becomes completely obsolete.

Your weekly Scorecard must adapt to measure the efficiency and quality of your AI-augmented human seats.

- AI adoption rate: Track the percentage of weekly tasks completed using automated tools to ensure your team is leveraging the technology.
- Quality assurance pass rate: Since AI can produce high volumes of work quickly, you must track the percentage of output that passes human review.
- Average resolution time: Measure how much faster your team completes projects or resolves issues with the help of AI.
- Exception rate: Track the percentage of complex issues that your AI systems cannot handle and must be escalated to a human expert.

These metrics ensure you are getting a clear return on your technology investments while maintaining strict quality standards. It shifts your leadership team's focus from tracking basic human activity to measuring high-value system throughput, ensuring your operations remain lean, highly scalable, and attractive to future buyers.