The Siemens Industrial Copilot enables customers to leverage generative AI across the entire value chain – from design and planning to engineering, operations, and services.
For example, the generative AI-powered assistant empowers engineering teams to generate code for programmable logic controllers using their native language, speeding up SCL code generation by an estimated 60% while minimising errors and reducing the need for specialised knowledge. This, in turn, reduces development time and boosts quality and productivity over the long term.
Siemens is developing a full suite of copilots to industrial-grade standards for the discrete and process manufacturing industries – and is now strengthening its Industrial Copilot offerings with the launch of an advanced maintenance solution, designed to redefine industrial maintenance strategies.
Siemens brings generative AI to the entire maintenance cycle
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 realise this, the Senseye Predictive Maintenance solution powered by Microsoft Azure will be extended with two new offerings:
Entry Package: 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: 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, maximise 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, optimising 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.
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 optimise their asset performance and maximise operational uptime. The fusion of generative AI and predictive maintenance allows customers to 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.
This expansion of our Industrial Copilot marks a significant step in our mission to transform maintenance operations,” said Margherita Adragna, CEO of Customer Services at Siemens Digital Industries. “By extending our predictive maintenance solutions, we’re enabling industries to seamlessly shift from reactive to proactive maintenance strategies and drive efficiency and resilience in an increasingly complex industrial landscape.”
With this innovation, Siemens continues to advance its vision of a digitalised industry, by providing customers with an intelligent and integrated approach to maintenance that ensures long-term operational success.
