We have integrated several AI tools into our operations to handle customer support and outbound prospecting, but we do not know how to reflect their performance on our human-driven Scorecard. How should we track the efficiency and error rates of AI-powered workflows on our weekly Scorecard?

Question

Tyler Smith · Accepted Answer

Running AI-powered operations requires the same level of accountability as running human teams. You cannot let your automated workflows run in a black box. If you do, you risk sudden system failures that can damage your client relationships and valuation. Your weekly Scorecard must reflect the health of these AI integrations. First, assign a human owner on the Accountability Chart to monitor the technology. This is usually the head of operations or technology. They own the metrics. Second, track AI throughput and accuracy. For customer support, track the deflection rate, which is the percentage of inquiries resolved by AI without human intervention. To balance this, track human escalation rate, which is the percentage of conversations where the AI failed and a human had to step in. Third, track data integrity and quality control. You should have a weekly metric for AI quality audits. This is the number of automated outputs reviewed by a human team member to check for hallucinations or process errors. By putting these numbers on your Scorecard, you maintain total visibility over your automated systems. This ensures your operations remain efficient and scalable, proving to future buyers that your business runs on robust, managed technology rather than unstable scripts.