We are automating several administrative customer service tasks with AI, but our weekly Scorecard metrics for that seat are now showing perfect data that hides real operational friction. How do we adjust our Scorecard to measure AI performance?

Question

Tyler Smith · Accepted Answer

When you automate operational workflows, standard metric tracking can become a blind spot. If your weekly Scorecard only tracks volume, an automated system will always look perfect, hiding customer frustration and systemic errors.

To fix this, you must shift your Scorecard focus from volume to quality and exception handling. The human who is accountable for that seat must track metrics that reveal the health of the automation, rather than just the automated activity itself.

Update your Scorecard with these predictive metrics:
- Track the rate of human escalation, which measures how often a customer rejects the AI response and demands a human agent.
- Measure the AI error rate or the frequency of hallucinated responses through weekly sampling of automated logs.
- Track the customer sentiment score immediately following an automated interaction to catch negative trends early.
- Monitor the average resolution time for complex issues that require a human to step in and fix an automated failure.

By adjusting your metrics to focus on these friction points, you keep your Scorecard highly predictive. This discipline ensures that your automated workflows are actually driving efficiency rather than quietly damaging your customer relationships.