When our team proposes new technology projects, they often get caught up in the excitement of machine learning and lose sight of business results. How do we restructure these proposals so they focus on bottom-line results?

Question

Tyler Smith · Accepted Answer

When your team proposes new technology projects, they often get caught up in the excitement of machine learning and lose sight of business results. To prevent these initiatives from turning into expensive science experiments, you must change how they are pitched and evaluated.

Never sell AI internally as a technology project. Instead, force your team to frame every proposal as an operations-improvement project that uses machine learning only as a footnote. The pitch must focus entirely on the operational bottleneck they are trying to solve and the bottom-line metrics they expect to improve, such as reducing cycle time or increasing capacity.

If a proposal cannot be tied directly to an improvement on your weekly Scorecard, reject it. When you set your quarterly Rocks, focus on the business outcome rather than the tool implementation. This disciplined approach ensures that your leadership team remains focused on driving operational efficiency rather than chasing shiny tech trends.

By demanding operational clarity before any code is written, you keep your operations stable and ensure your software investments deliver a clear return.