Predictive maintenance is often praised as the silver bullet for reducing unplanned downtime, extending asset life, and lowering maintenance costs, but the reality often tells a different story. Across industries, organizations are investing heavily in sensors, analytics platforms, and other digital tools, only to end up overwhelmed by noisy, fragmented, and siloed data. Programs stall before delivering value, leaving critical assets at risk and millions of dollars wasted. At the heart of these failures is a fundamental misalignment between strategy, technology, and day-to-day operations, an issue that is far more common than most leaders realize.

In this expert-led session, Matt Kirchner, Principal Solution Engineer for Asset Performance Management at Prometheus Group, will share insights from over 20 years in software, engineering, and consulting for the utility, industrial, and manufacturing sectors. He began his career in operational intelligence and engineering at Black and Veatch, later serving as Co-Owner and Chief Product Officer of Atonix Digital. Matt joined Prometheus Group when they acquired Atonix Digital and its APM solution, now known as Prometheus APM. Leveraging his background in complex industrial settings, he will outline what causes predictive maintenance programs to fail, how to fix those problems, and how to scale with confidence.

Predictive maintenance should be a driver of results, not a drain on resources. Discover the key practices that separate successful programs from those that struggle and gain the insight needed to build a smarter, more efficient maintenance future. Register now to get started.

Learning Objectives

• Identify common causes of predictive maintenance failure and how misalignment between strategy, technology, and operations stalls results.
• Learn methods to fix underperforming programs by addressing data fragmentation, process gaps, and operational silos.
• Understand how to align predictive maintenance insights with business goals for measurable impact on reliability, uptime, and costs.
• Gain a framework to scale predictive maintenance with confidence while avoiding resource and budget overextension.