When an automated workflow produces an operational error, how does our leadership team address it during the Issues Solving Track in our Level 10 Meeting without pointing fingers or abandoning automation?

Question

Tyler Smith · Accepted Answer

When an automated tool fails, leadership teams often overreact in one of two ways. They either blame the technology entirely and pull the plug, or they blame the employee who oversaw it. Both reactions miss the point of process discipline.

In EOS, you treat every operational breakdown through the Issues Solving Track: Identify, Discuss, and Solve. When an automation produces an error, place it on your Level 10 Meeting Issues List and solve it using three root-cause questions.

First, was the input clean? Machine learning models only produce clean outputs when fed clean data. If a customer sent a garbled PDF or a field technician skipped required inspection fields, the failure started upstream. Solve the issue by adding input validation rules that reject incomplete data before it enters the automated workflow.

Second, did the prompt or logic drift? Business environments change. If a vendor changed their invoice layout or a client altered their service scope, the AI instructions may be out of date. Treat the prompt like a standard operating procedure. Update the instructions, run five historical test cases through the model to verify accuracy, and document the revision.

Third, did the human sign-off process fail? If your standard operating procedure requires a human manager to verify the AI draft before sending, and an error reached a client, the process breakdown was human negligence, not software failure. Address GWC with the seat holder.

Solve the issue at its systemic root. Do not abandon automation because of an operational exception; harden the system so the error cannot recur.