Our engineering team keeps proposing complex machine learning projects, but our leadership team struggles to see the business value. How do we frame these initiatives so they actually drive operational improvement?

Question

Tyler Smith · Accepted Answer

The problem is how these initiatives are being framed. You must stop calling them machine learning projects. Refrain from using technical terms that alienate the rest of your leadership team. Instead, frame them strictly as operations-improvement projects that happen to use machine learning.

When your engineers propose a new tool, require them to translate the technical features into operational outcomes. For example, do not let them pitch a natural language processing model for your customer support queue. Have them pitch a project to reduce customer support response times from four hours to five minutes.

By shifting the language, you force the entire team to focus on the business impact rather than the cool factor of the technology. This framing aligns perfectly with your EOS® framework. It allows your Integrator and department heads to evaluate the project based on its ability to streamline cumbersome processes and free your team from low-value tasks.

Every proposed tool must have a clear home on your Accountability Chart and a direct tie to a Scorecard metric. If a project cannot be defined by its operational improvement, archive it. This discipline keeps your company focused on building system-dependent operations that drive profitability and scale, rather than wasting capital on developer pet projects.