Our software engineers and operations managers keep proposing complicated machine learning initiatives that sound impressive but never seem to move our Scorecard metrics. How do we restructure these proposals so they actually drive operational efficiency?

Question

Tyler Smith · Accepted Answer

Stop letting your team pitch machine learning projects. The moment you label something as an AI or ML project, your team focuses on the novelty of the technology instead of the operational reality. From this day forward, ban the term ML project from your vocabulary. Instead, force your leadership team to frame these initiatives as operations improvement projects that happen to use machine learning. When an issue is raised in your weekly Level 10 Meeting, run it through the IDS process. If the solution requires automation, the proposal must clearly state how the technology will streamline a specific, documented core process. It must identify which seat on the Accountability Chart will oversee the automation and how it will improve a specific metric on your weekly Scorecard. Never sell AI internally or externally. Instead, pitch the operational improvements that the technology enables, mentioning machine learning only as a footnote. This shift in framing forces your team to focus on building system dependent operations that consistently produce high margin results. It ensures that every dollar you invest in automation directly increases the value of your business and prepares you for a clean exit.