We want to track the efficiency gains of our new AI-driven customer billing system on our weekly Scorecard, but we do not know what leading indicator metric to measure. How do we design a Scorecard metric that tracks human-machine efficiency instead of just output?

Question

Tyler Smith · Accepted Answer

A common mistake when deploying AI is tracking lag metrics like total bills processed, which does not tell you if your team is actually operating more efficiently or just generating more volume. To measure true human-machine efficiency on your weekly Scorecard, you must focus on leading indicators of time and friction.

Instead of tracking the output, track the hours spent per billing cycle or the number of billing errors that require manual intervention. For example, design a metric called billing cycle hours, which tracks the exact amount of human labor required to complete the billing process each week.

If the AI is working effectively, this number should steadily decrease, showing that your team is gaining capacity. Alternatively, track the percentage of straight-through processing, which measures how many bills are completed by the AI from start to finish without a human needing to step in and correct an error.

If your straight-through processing percentage is low, it indicates your team is spending too much time fixing AI mistakes, meaning your system is inefficient. By focusing on these friction and time-based metrics, your leadership team will have a clear, weekly picture of whether your AI investment is actually driving operational efficiency.