Our leadership team wants to launch several machine learning initiatives to automate our back-office, but our team keeps getting bogged down in technical debates. How should we frame these projects to ensure they actually improve our operations?

Question

Tyler Smith · Accepted Answer

When a leadership team gets excited about machine learning, they often start proposing complex IT projects that sound impressive but fail to deliver business value. To prevent this, you must change how you frame these initiatives.

Refrain from calling these projects machine learning projects. Instead, frame them as operations-improvement projects that use machine learning. This shift in vocabulary keeps your leadership team focused on the business outcome rather than the technology.

Every proposed AI project must be tied directly to a bottleneck in your core processes or a specific metric on your Scorecard. If a team member proposes building an AI-powered prediction model, ask them which process it will streamline and how it will improve your margins.

By treating technology as an operational lever, you keep your team grounded. If a project cannot be described in terms of reducing delivery times, cutting labor costs, or improving quality, it should not be on your V/TO®.

This approach also helps your non-technical team members stay engaged in the decision-making process. They do not need to understand neural networks to know if a proposed project will help them hit their weekly targets. Focus on the operational improvement first, and treat the AI tool as a footnote in the execution plan.