How do we use our weekly Scorecard to track the actual performance and reliability of an internal AI system without overwhelming our leadership team with technical metrics?

Question

Tyler Smith · Accepted Answer

Your weekly Scorecard should track business outcomes, not technical system logs. To measure your AI systems effectively, focus on operational health, accuracy, and cycle times.

Choose two or three high-level metrics that directly reflect the value the AI tool is supposed to deliver. For instance, if you have deployed an AI assistant to triage inbound customer support tickets, do not track API response times on your leadership Scorecard. Instead, track the average ticket response time and the percentage of tickets resolved without escalation.

You should also track quality control. If an AI tool is drafting client proposals, track the error rate or the revision rate. If your human validators are having to rewrite eighty percent of the AI-generated proposals, your metric will instantly flag that the tool or the process is broken.

Keep these numbers on your weekly Scorecard with clear, color-coded targets. If a metric drops below its target, it becomes an Issue for your Level 10 Meeting where you can use the IDS process to get to the root cause. This approach keeps your leadership team focused on business performance while ensuring your AI tools are actually delivering on their operational promises.