"A new chapter has begun in the realm of enterprise asset management (EAM) across various industries. The role that was once viewed as merely traditional-monitoring assets, organizing repairs, and minimizing downtime-has evolved into a strategic force driving efficiency, sustainability, and competitive advantage. The catalysts behind this change are artificial intelligence (AI), digital twins, and virtual operations centers (VOCs). These technologies have evolved beyond mere experimental additions. These are established strategies transforming the way organizations oversee, control, and enhance their resources."
"However, numerous individuals still depend on outdated systems that struggle to meet contemporary needs. These antiquated tools hinder our ability to foresee failures, streamline resources, or react swiftly to disruptions. Instead of scrapping existing systems, organizations are layering in AI-powered technologies to extend the value of their current EAM software and maintenance management software. Jason Dietrich, the Chief Revenue Officer at TwinThread, articulates: "AI and digital twins do not supplant legacy platforms; rather, they augment them, revealing potential that might otherwise stay concealed.""
Enterprise asset management has shifted from basic asset monitoring to a strategic discipline driven by AI, digital twins, and virtual operations centers that enhance efficiency, sustainability, and competitive advantage. Industrial organizations face intense pressure to minimize downtime, increase productivity, reduce costs, and meet sustainability and compliance obligations. Many organizations still rely on legacy systems that lack predictive and coordination capabilities, creating blind spots and slow responses. Rather than replacing existing platforms, organizations are overlaying AI-powered layers and digital twins onto current EAM and maintenance systems to extend functionality, reveal hidden value, and enable more timely, data-driven operational decisions.
Read at Business Matters
Unable to calculate read time
Collection
[
|
...
]