Our software developers are pitching us complex machine learning models to improve our supply chain, but the leadership team cannot see the connection to our quarterly Rocks. How do we restructure these technical proposals so they focus on measurable bottom-line improvements rather than algorithms?

Question

Tyler Smith · Accepted Answer

When your technical team proposes machine learning initiatives, they often focus on the complexity of the algorithms rather than the business outcomes. This technical jargon causes confusion on the leadership team and delays projects that could drive real operational efficiency.

To fix this, banish the phrase "machine learning project" from your vocabulary. Instead, frame every single tech initiative as an operations-improvement project that uses technology to solve a specific bottleneck. Shift the focus from how the technology works to what business metric it will improve on your Scorecard.

For example, instead of proposing a project to "build an advanced natural language processing model for customer service," frame it as "reducing our average customer ticket resolution time from forty-eight hours to four hours." This framing forces your technical team to align their work with your business goals and quarterly Rocks.

By focusing on measurable operational outcomes, you make it easy for your leadership team to evaluate the project. You can objectively assess the financial return and determine if the initiative deserves resources, ensuring your technology investments always drive your bottom-line profitability.