Our operations team wants us to build a custom machine learning model to optimize our delivery routes, but it sounds expensive and risky. How should we decide whether to build a custom solution or buy off-the-shelf software?

Question

Tyler Smith · Accepted Answer

To make this decision, you must strip away the technical jargon and evaluate the project purely as an operations-improvement project. Never buy or build custom code simply because it uses advanced machine learning.

Start by looking at the specific operational bottleneck you are trying to solve. If an off-the-shelf software solution can solve seventy percent of the problem, buy it. It is faster, cheaper, and far less risky than building custom code.

Only consider building a custom solution if the process is a core part of your three-year picture and represents a proprietary competitive advantage that cannot be bought. If you do choose to build, do not frame it to your board or leadership team as an AI project. Pitch the operational improvements that the technology will enable, mentioning machine learning only as a footnote.

Focus your team on building expert systems that consistently produce results. This keeps the project grounded in business reality, making your operations system-dependent while protecting your capital from high-tech trends that fail to deliver a clear operational return.