Generative AI tools can cut maintenance times by 25%
14 April 2025 
Siemens has announced a version of its Industrial Copilot generative AI-based assistant that supports every stage of the maintenance cycle, from repair and prevention, to prediction and optimisation. It says that the development will redefine industrial maintenance strategies by helping users to move beyond traditional maintenance practices toward an intelligent, data-driven approach.
Initial pilot applications of the technology have shown that the Industrial Copilot for Maintenance can help to cut reactive maintenance times by an average of 25%.
The maintenance tool is one of a suite of Industrial Copilots that Siemens is developing for users in discrete and process manufacturing. It extends the company’s existing Senseye Predictive Maintenance technology with two new packages that offer generative AI-driven insights to enhance decision-making and efficiency:
- An Entry package which provides a cost-effective introduction to predictive maintenance, combining AI-powered repair guidance with basic predictive capabilities. It is designed to help businesses to transition from reactive to condition-based maintenance, offering limited connectivity for sensor data collection and real-time condition monitoring. With AI-assisted troubleshooting and minimal infrastructure requirements, companies can cut downtime, improve maintenance efficiency, and lay the foundations for full predictive maintenance.
- A Scale package, designed for enterprises wanting to transform their maintenance strategies. It integrates Senseye Predictive Maintenance with full Maintenance Copilot functions. It allows customers to predict failures before they happen, improve uptime, and cut costs with AI-driven insights. It offers enterprise-wide scalability, automated diagnostics, and sustainable business outcomes. The package will help companies to move beyond traditional maintenance practices, optimising their operations across multiple sites, while supporting long-term efficiency and resilience.
As industries seek to enhance reliability and cut costs, maintenance operations are evolving from reactive to proactive strategies. Traditional approaches to maintenance often lead to costly downtime and other inefficiencies, according to Siemens.
It adds that it is addressing this challenge by with the AI-driven maintenance tools that will help companies to improve their asset performance and operational uptime. The fusion of generative AI and predictive maintenance will allow them to harness real-time data and advanced analytics that allow timely interventions and strategic planning.
“This expansion of our Industrial Copilot marks a significant step in our mission to transform maintenance operations,” says 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.”
