Our leadership team gets distracted by flashy AI demonstrations from vendors instead of focusing on bottom-line results. How do we reframe AI initiatives inside our company so everyone evaluates them strictly as operations-improvement projects?

Question

Tyler Smith · Accepted Answer

The fastest way to burn capital on software is to treat machine learning as an exciting technological experiment. Whenever a leadership team starts talking about AI for the sake of modernization, they end up with unused software licenses, distracted managers, and zero measurable return on investment.

Establish a firm rule across your leadership team: never sell AI internally, and never evaluate an AI project. Reframe every single proposal as an operations-improvement project that happens to use automation or language models as a minor tactical tool.

When a department head brings a software request to the table, forbid them from highlighting technical buzzwords. Require them to define three basic operational elements: the cumbersome manual process keeping employees trapped in low-value tasks, the specific payroll hours or hard dollar costs being eliminated, and the exact EOS Scorecard measurable that will move once the project goes live.

If a proposed tool cannot measurably reduce overtime, eliminate a back-office bottleneck, or accelerate customer turnaround times, reject it immediately. Machine learning should only be mentioned as an implementation detail on an internal Rock or operational issue during IDS. By stripping away vendor theater and demanding clear operational justification, you keep your leadership team focused on enterprise value, cash flow, and scalable systems.