Our department heads keep pitching flashy AI projects that sound impressive but lack clear commercial objectives. How does our leadership team reframe these initiatives into operations-improvement projects that produce measurable bottom-line results?

Question

Tyler Smith · Accepted Answer

Never approve an AI project. The phrase itself invites theater, vendor hype, and wasted capital. Instead, require your leadership team to frame every proposal as an operations-improvement project that happens to use machine learning as a practical tool.

When a department head brings an idea to your Level 10 Meeting or suggests it as a potential Rock, ban the acronyms. Do not let them talk about large language models, neural networks, or autonomous workflows. Force them to define the proposal using three basic business metrics: which documented Core Process this improves, how many hours of manual payroll it returns to the business every week, and what specific Scorecard metric it will move.

If the pitch cannot identify a cumbersome bottleneck where high-cost employees are trapped doing low-value administrative work, kill the initiative immediately. Machine learning should only be mentioned as a footnote under technical implementation, never as the headline. For example, the objective is not to build a customer service chatbot. The objective is to reduce warranty intake processing time from three days to four hours while cutting administrative processing labor by half.

By anchoring every automation project to measurable operational outcomes on your V/TO, you protect the company from spending money on novelty. Your leadership team stays focused on execution, accountability, and margin expansion rather than chasing tech-sector headlines.