Siemens announced an expansion to its Industrial Copilot offering with extended capabilities for Senseye Predictive Maintenance.
The new generative AI-powered solution will support every stage of the maintenance cycle, by helping industries move beyond traditional maintenance practices toward an intelligent, data-driven approach. To realize this, the Senseye Predictive Maintenance solution powered by Microsoft Azure will feature two new offerings.
Entry Package
This solution provides an introduction to predictive maintenance, combining AI-powered repair guidance with basic predictive capabilities. It helps businesses transition from reactive to condition-based maintenance by offering limited connectivity for sensor data collection and real-time condition monitoring.
With AI-assisted troubleshooting and minimal infrastructure requirements, companies can reduce downtime, improve maintenance efficiency and lay the foundation for full predictive maintenance.
Scale Package
Designed for enterprises looking to fully transform their maintenance strategy, this package integrates Senseye Predictive Maintenance with the full Maintenance Copilot functionality. It enables customers to predict failures before they happen, maximize uptime and reduce costs with AI-driven insights.
Offering enterprise-wide scalability, automated diagnostics and sustainable business outcomes, this solution helps companies move beyond traditional maintenance, optimizing operations across multiple sites while supporting long-term efficiency and resilience.
As industries increasingly seek ways to enhance reliability and reduce costs, maintenance operations are evolving from reactive to proactive approaches. Traditional maintenance strategies often lead to costly downtime and other inefficiencies. Siemens addresses this challenge by integrating AI-driven maintenance solutions that help companies optimize their asset performance and maximize operational uptime.
The fusion of generative AI and predictive maintenance allows customers harness real-time data and advanced analytics that ensure timely interventions and strategic planning.
First pilot use cases have shown that the Industrial Copilot for maintenance helps save on average 25% reactive maintenance time.