Measure what Matters

A Data Science Manager at a major global CPG company described it this way: his team built a demand forecasting model with 72% accuracy, a meaningful improvement over the 60% it replaced. Leadership was pleased. The model was technically sound. Nobody used it.

The production planning team kept doing what they had always done. The model sat there producing the right answer for nobody.

A Director of Supply Chain Strategy at a large CPG organization frames the same problem from the business side: the organization doesn’t care how the model was built, what data it used, or what accuracy it achieved. They want the answer, and they want it fast. If the tool doesn’t fit into the way they already work, it doesn’t get used. And if it doesn’t get used, it was worth exactly nothing.

This is the metric most AI initiatives never track: Adoption.

Not model accuracy. Not infrastructure uptime. Not the number of use cases in the pipeline. Whether the people it was built for show up every day and use it.

The Data Science Manager put it plainly: you can build something brilliant and still end up with zero adoption. New equipment nobody drives. New processes nobody follows. A forecasting tool nobody opens. The value of anything with zero adoption is zero. It does not matter how good the underlying work was.

Building for adoption means starting with the end user before the first line of code is written. What decision does this person make today, and how does this tool make that decision faster or better? If you can’t answer that before you build, you are already building something that will go to the closet.

The most expensive AI project is the one that works perfectly and gets used by nobody.