Siemens expands its Industrial Copilot offering with extended capabilities for Senseye Predictive Maintenance.
Siemens is expanding its Industrial Copilot suite with a new maintenance solution designed for discrete and process manufacturing industries. The offering aims to improve industrial maintenance strategies through advanced automation and AI capabilities.
Siemens brings generative AI to the entire maintenance cycle
Siemens’ new AI-powered solution enhances maintenance by enabling a data-driven approach across all stages. To realize this, the Senseye Predictive Maintenance solution powered by Microsoft Azure will be extended with two new offerings:
- Entry Package:Â This solution provides an accessible and cost-effective 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:Â 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.
The new offering enables comprehensive coverage of the entire maintenance cycle – from reactive repair to predictive and preventive strategies – by leveraging generative AI-driven insights that enhance decision-making and efficiency across industrial environments.
Industries are shifting from reactive to proactive maintenance to improve reliability and reduce costs. Traditional maintenance methods can lead to downtime and inefficiencies. Siemens aims to address this by integrating AI-driven solutions to optimize asset performance and operational uptime. By combining generative AI with predictive maintenance, companies can use real-time data and analytics for timely interventions and planning. Initial pilot tests indicate that Siemens’ Industrial Copilot for maintenance reduces reactive maintenance time by an average of 25%.
For more information, visit siemens.com.
