Siemens unveils breakthrough innovations in industrial AI

Siemens has unveiled ground-breaking advancements in Industrial AI and digital twin technology at CES 2025, showcasing its vision for the future of industry.

The innovations highlight a transformative convergence of data, AI, and software-defined automation, enabling businesses of all sizes to optimise operations, enhance flexibility, and drive continuous improvement.

Under the banner of the Siemens Xcelerator portfolio, the company demonstrated cutting-edge tools designed to empower industries to remain competitive, resilient, and sustainable in a rapidly evolving global landscape.

“Industrial AI is a game-changer that will create significant positive impact in the real world across all industries,” said Peter Koerte, Member of the Managing Board, Chief Technology Officer, and Chief Strategy Officer at Siemens AG.

“It allows us to harness the vast amounts of data generated in industrial environments and turn it into insights that drive real business impact. We are adding new industrial AI capabilities across the Siemens Xcelerator portfolio to enable our customers to stay competitive, resilient and sustainable in an increasingly complex world.”

At the forefront of Siemens’ innovations is the Siemens Industrial Copilot for Operations, a platform designed to bring Industrial AI directly to the shop floor. This technology allows AI-powered tasks to be executed in close proximity to industrial machinery, enabling real-time decision-making by shop floor operators and maintenance engineers.

The Industrial Copilot is engineered to boost productivity and operational efficiency; minimise downtime through rapid, actionable insights; and enhance human-machine collaboration for workers across all skill levels.

This innovation seamlessly integrates with Siemens’ Industrial Edge ecosystem, which has been upgraded with advanced AI capabilities. By combining edge computing with AI, the enhanced ecosystem provides manufacturers with a robust framework for deploying, operating, and managing AI models within production environments.