As a non-technical owner, how do I use the EOS Accountability Chart and the GWC tool to manage the performance of our automated machine learning workflows without getting bogged down in the technical details?

Question

Tyler Smith · Accepted Answer

You do not need to understand code to lead an AI-powered operating company. You manage automated workflows exactly the same way you manage human team members: through the discipline of the Accountability Chart and the GWC tool.

Every machine learning script or automated system must have a single human owner on your Accountability Chart. That human is responsible for the system's output and its scorecard metrics. To ensure the right person is in the seat, evaluate them using GWC.

Do they truly Get the automated system's operational purpose? Do they Want the responsibility of managing its output and maintaining its accuracy? Do they have the Capacity, meaning the time and resources, to oversee the system, troubleshoot failures, and ensure the data remains clean?

If the human owner does not meet these criteria, your automated workflows will quickly decay into fragile liabilities. By keeping the accountability strictly human, you prevent your team from pointing fingers at the technology when things go wrong. Your job as the owner is to hold your leaders accountable to their scorecard metrics, letting them handle the technical execution while you focus on system-dependent operational consistency.