Use of AI copilots set to grow further with new offering aimed at machine maintenance

Siemens says its generative AI-based Industrial Copilot is establishing new possibilities across the maintenance cycle for a data-driven strategy at a range of predictive levels.

The company has expanded its portfolio in this area, which now includes extended capabilities for Senseye Predictive Maintenance. It says the AI options will support every stage of the maintenance cycle, from repair and prevention to prediction and optimisation.

Powered by Microsoft Azure, Senseye has been extended to include two new offerings. The Entry Package provides an accessible and cost-effective introduction to predictive maintenance, says Siemens, combining AI-powered repair guidance and troubleshooting with basic predictive capabilities. Companies can reduce downtime, improve maintenance efficiency, and lay the foundation for full predictive maintenance, it adds.

The Scale Package is designed for enterprises looking to fully transform their maintenance strategy, says the company, integrating 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. This includes enterprise-wide scalability.

“This expansion of our Industrial Copilot marks a significant step in our mission to transform maintenance operations,” says Margherita Adragna, chief executive 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.”

Siemens claims its Industrial Copilot is revolutionising industry by enabling customers to leverage generative AI across the entire value chain – from design and planning to engineering, operations, and services. It quotes the example of the generative AI-powered assistant which empowers engineering teams to generate code for programmable logic controllers using their native language, speeding-up Structured Control Language (SCL) code generation by an estimated 60%, while minimising errors and reducing the need for specialised knowledge.

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