Your predictive maintenance system catches failures—but it also generates dozens of false alarms. Your maintenance teams are skeptical. The ROI is unclear. A new benchmark study of 10,000 CNC machines reveals why: traditional ML optimizes for statistical accuracy instead of business outcomes.
The cost? Approximately $80,000 per 2,000 machines annually.
Asking for feedback can feel awkward, and giving it can be just as uncomfortable.
In many companies, the feedback process can devolve into struggles over punctuation or vague critiques that don't provide real value. Conversely, receiving requests for feedback often means wading through poorly constructed documents or hastily generated AI content, combined with unrealistic deadlines.
If you walk around enterprise-land in 2025, you get two versions of reality:
You sometimes hear: "Data show more people die from the smallpox vaccine than from smallpox itself, so vaccination should be banned."
This is a misinterpretation of the data if you only compare totals. The mistake is ignoring considering counterfactual reasoning.
We live in the "data economy," with dashboards, models, and petabytes to give us insights. And yet data is dumb. Left to its own devices, raw data can't tell you why anything happens. It can tabulate, correlate, and predict (sometimes) with accuracy, but data does not understand causes and effects like humans do.