Our department heads keep bringing proposals for shiny new AI platforms that derail our budget. How do we force our leadership team to evaluate these proposals strictly as operational process improvements rather than AI initiatives?

Question

Tyler Smith · Accepted Answer

Ban the phrase AI initiative from your leadership team vocabulary. When companies launch technology initiatives, they inevitably buy software first and search for operational problems to solve later. That sequence destroys capital.

Instead, mandate that all proposals enter your Level 10 Meeting™ as operations-improvement projects that happen to use machine learning as a footnote. Your leadership team should never evaluate software; they must evaluate process health, capacity constraints, and gross margin optimization.

Enforce a standard operational evaluation template before any platform enters pilot testing:
- Identify the documented SOP: The project owner must point to the specific, simplified core process that is creating operational drag. If the process is not already documented, automating it is disqualified.
- Define the reallocation of labor: Specify exactly what high-value work the affected employees will perform once low-value data manipulation or clerical steps are eliminated.
- Quantify the failure risk: Outline the worst-case operational outcome if the automation breaks, along with the human-in-the-loop fallback procedure.
- Set a single Scorecard measurable: The project must tie directly to an existing weekly metric, such as order turnaround hours, gross margin per technician, or error rates.

By shifting the burden of proof from technology novelty to process friction, you eliminate shiny-object syndrome. Your leadership team will stop chasing market hype and start using modern tools for their only legitimate purpose: removing cumbersome tasks from your highest-paid team members so they can drive revenue and client satisfaction.