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.
If you walk around enterprise-land in 2025, you get two versions of reality:
For decades, the digital world has been on a foundation of pattern recognition. We've celebrated systems that could tell us what happened, predict what might happen next, and optimize based on historical correlations. These systems have been revolutionary, powering search engines, recommendation systems, and countless applications that define modern life. But pattern recognition has reached its ceiling in the environments that matter most. Enterprise decisions. The future belongs to systems that understand cause and effect.
Despite the power of modern machine learning, wind farm operators still struggle to predict with confidence when a turbine's gearbox is about to fail and plan maintenance before disaster strikes.
Over the past decade, data analytics and big data have transformed the way organizations operate. Companies have invested heavily in collecting, organizing, and analyzing massive volumes of data. Dashboards, predictive models, and automated reports are now commonplace, helping decision-makers see trends, forecast outcomes, and optimize processes.