I am a non-technical owner who wants to run an operations-improvement project that uses ML, but I do not want our IT team or vendor to drag us into a multi-month software build. How do I scope this project to keep it focused strictly on operational results?

Question

Tyler Smith · Accepted Answer

To prevent an operations-improvement project that uses ML from turning into a bloated, multi-month software build, you must enforce strict operational constraints from day one. Do not let your team or vendors talk about algorithms, models, or technical infrastructure. Force them to define the exact operational bottleneck they are solving. Begin by setting a firm ninety day limit for the project, aligning it directly with a quarterly Rock. If the project cannot produce a usable, testable version within one quarter, it is too complex. Break it down into smaller, bite-sized components. Next, define success using your weekly scorecard metrics, not technical accuracy rates. For example, do not accept a goal like improving model prediction accuracy to ninety-five percent. Instead, demand a goal like reducing the time spent on manual inventory matching from twelve hours per week to two hours per week. Finally, require your team to build a working prototype using existing, off-the-shelf APIs and no-code connection tools before you authorize any custom coding. This approach allows you to test the workflow with real operational data for a fraction of the cost. As a non-technical owner, your job is not to understand the code. Your job is to protect the operating margin and keep the team focused on building system-dependent operations. Keeping projects short, simple, and tied directly to quarterly Rocks ensures you build real operational value instead of funding expensive technical experiments that drain your cash.