We are automating eighty percent of our data entry and customer routing using AI, which is completely changing our operations. How do we shrink our bloated leadership team scorecard down to a fresh list of five to fifteen numbers that reflect this automated workflow?

Question

Tyler Smith · Accepted Answer

Introducing AI automation to your workflows means your old operational metrics are likely obsolete. If your systems are now executing eighty percent of your data entry and routing, tracking human keystroke counts or raw processing volume on your leadership scorecard is pointless. You must prune your scorecard back down to five to fifteen vital numbers.

To do this, use the Great Day or Lousy Day exercise. Ask your leadership team: if you were on a remote island with only an index card of data, what ten numbers would tell you if we had a great operational day?

When operations are heavily automated, your metrics must shift from measuring human activity to measuring system health and human intervention. Your new scorecard should focus on three categories.

First, measure system throughput, such as the total transactions processed by your AI systems weekly.

Second, track the exception rate, which is the percentage of automated tasks that failed and required a human employee to step in and fix. This is your primary efficiency indicator. If the exception rate rises, your automation is breaking down.

Third, measure user adoption and satisfaction. If your AI systems are working but your clients or employees are frustrated, your business is in jeopardy.

Keep your leadership scorecard high-level. Push the technical sub-metrics down to departmental scorecards so your leadership team stays focused on the big picture.