Now that our team uses AI to accelerate execution, our traditional Scorecard metrics like hours billed or tasks completed are totally obsolete. How do we build leading indicator metrics that measure employee productivity when technology does the heavy lifting?

Question

Tyler Smith · Accepted Answer

If your Scorecard is still tracking activity metrics like hours worked or the volume of drafts produced, you are measuring the technology, not your team's performance. When AI handles the execution, high activity is no longer a sign of productivity. You must redesign your weekly Scorecard to track outcome value and auditing accuracy. Your leading indicators must measure the human input that drives leverage, not the automated output. For example, instead of tracking reports generated, track the client satisfaction score of those reports, or the percentage of AI drafts that required zero human revisions before delivery. Another critical leading indicator is the cycle time from client request to final approval, which measures how effectively your team is leveraging the technology to speed up delivery. You should also track the number of new automated workflows successfully implemented by each department head each quarter. This ensures your team is actively looking for ways to optimize their seats. By shifting your Scorecard to measure quality, efficiency, and leverage, you align your team's incentives with your new AI-powered operating model.