We want to start tracking our AI model's performance on our weekly Scorecard alongside our human metrics, but we do not want to overwhelm the team with complex technical data. What are the key operational metrics we should track to ensure our AI is performing accurately without cluttering our leadership dashboard?

Question

Tyler Smith · Accepted Answer

Your weekly Scorecard should only contain your most critical leading indicators. Do not clutter it with complex technical metrics like server response times or token usage. Instead, focus on the operational and financial outcomes your AI tools are designed to deliver. For an AI customer service tool, track simple, high-level metrics. You might measure the percentage of customer inquiries resolved without human intervention, the average response time, and the weekly volume of support tickets closed. You must also track a quality metric, such as customer satisfaction ratings or the number of escalations to human support. If you have automated your data entry, track the weekly error rate or the volume of records processed per hour. Each of these metrics must have a clear target and a single owner on your Accountability Chart who is responsible for keeping the number on track. If a metric drops below its target, drop it down to the Issues list in your Level 10 Meeting™ to be solved using IDS®. This keeps your leadership team focused on operational outcomes rather than getting bogged down in technical debugging.