Strategic collaboration to accelerate physical AI for process industries

While much of today's Physical AI conversation is focused on robotics, humanoids and autonomous machines, process industries face a distinctly different challenge. Operators must manage complex, continuous operations where reliability, safety, cybersecurity and energy efficiency are critical to performance. Yet until now, there has been no clear blueprint for how Physical AI can be practically applied across these complex environments.

Schneider Electric, NVIDIA and HPE share a vision to bring AI closer to industrial operations, embedding intelligence directly within the environments where decisions are made and value is created. As an initial demonstration of this vision, the companies are today introducing Industrial AI Agents at the Edge.

The solution combines HPE Private Cloud AI, co-engineered with NVIDIA within the HPE AI Factory with NVIDIA portfolioSchneider Electric’s deep operational technology expertise to enable organisations to securely deploy and scale AI applications at the industrial edge, bringing real-time intelligence closer to critical operations while maintaining control over sensitive data, security and costs.

Many industrial organisations continue to face challenges when scaling AI across operational environments. Requirements around data governance, cybersecurity, operational performance and AI deployment can create barriers to broader adoption. By operating at the edge, the solution helps address three challenges that continue to limit industrial AI at scale:

• Data sovereignty. Critical operational data is often too valuable, sensitive or regulated to move beyond operational boundaries. HPE Private Cloud AI provides the secure foundation for Industrial AI Agents at the Edge, enabling organizations to deploy AI close to operations while maintaining control, security, and compliance.
• Cybersecurity. Increased connectivity can introduce additional risk across industrial and critical infrastructure environments. HPE Private Cloud AI reduces the attack surface by keeping and processing the data close to its source.
• AI consumption costs. As AI usage grows, cloud-based inference, data transfer and token-related costs can increase, creating challenges for organizations looking to scale AI across sites, assets and operations. Deploying AI at the edge with HPE Private Cloud AI can help reduce some of these costs while providing greater control over where and how AI workloads are executed.

 Through Schneider Electric's EcoStruxure Industrial Insights apps customers can deploy, manage and govern multiple Industrial AI Agents through a single environment, simplifying lifecycle management, security oversight and maintaining full control of critical operations and infrastructure.  

The first Industrial AI Agents focus on operational technology monitoring, cybersecurity and energy optimisation. Their practical application can already be seen in industrial environments with companies such as CMPC Chile.

The foundation for this next step is already proven at CMPC Chile, part of Empresas CMPC S.A., one of Latin America’s largest pulp, paper and packaging producers. For the past three years, CMPC Chile has used Schneider Electric’s OTMS in production to monitor the health and availability of control systems across its continuous pulp and paper operations.

OTMS provides CMPC’s teams with a unified view of control systems, servers, workstations and switches, helping specialists identify emerging issues earlier, diagnose problems faster and establish clearer maintenance priorities. This proven deployment demonstrates the value of OTMS as a control-system monitoring solution and provides a strong operational foundation for the future introduction of Industrial AI Agents, with the potential to accelerate investigation and decision-making while keeping people in control of critical action

The first three Industrial AI Agents at the edge  

OT Monitoring Agents: Combines real-time operational data, equipment configurations, process topology, alarms, maintenance history and engineering context to understand the physical state of industrial operations. The agents identify emerging issues, reasons about likely operational impacts, recommends or initiates corrective actions, and verifies that the intervention delivers the expected physical outcome.

Cybersecurity Agents: Correlates network activity, control system behaviour, asset configurations, operational events and process context to detect abnormal conditions. The agents assess potential impacts on industrial operations, recommends or executes response measures, and validates that systems have returned to a secure and stable state.

Energy Optimisation Agents: Integrates process data, energy consumption patterns, equipment performance, operating conditions and production requirements to understand how energy is used across industrial operations. The agents identify optimisation opportunities, recommends or implements adjustments, and verifies the resulting impact on energy efficiency, operational performance and sustainability outcomes.

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