To scale Physical AI, the next leap is the development of a Multimodal Large Language Model (MLLM), an AI model capable of understanding and reasoning across multiple input types including text, images, video, audio, LiDAR, and more.

NVIDIA is working with manufacturing and robotics leaders to drive reindustrialization using Physical AI.

Industrial edge computing processes data locally within industrial environments, enabling real-time analytics and machine control. It’s used in predictive maintenance, robotics, and quality assurance by reducing latency and enhancing automation.

In a recent blog post, IOTech’s Chief Technology Officer, Brad Corrion, shared his perspective on how “2026 will be the year that AI is deployed at the Edge”.

Solution from Software Defined Automation delivers industrial DevOps, security and AI capabilities for industrial controller management at scale.

Driving innovation and sustainability at the edge in collaboration with Arm®, Eurotech redefines Edge IoT for critical infrastructure with the launch of the ReliaGATE 15A-12.

New virtual safety fence and real-time quality inspection systems use industrial-grade network infrastructure. Accenture, NVIDIA, and Belden are creating safer, smarter, and more connected industrial operations where the physical and digital worlds converge.

Artificial Intelligence and Machine Learning technology applies reasoning with uncertainty, autonomous path planning, collision avoidance, swarm technology and sensor fusion support to autonomous trains and trolleys.

Manufacturers increasingly need to rely on embedded edge experts to support and consult their data management plans, because managing data at the edge is no longer a simple engineering task, but a complex, high-stakes discipline.

Environmental monitoring should be considered a foundational element of any data center management strategy.