Industrial AI’s Next Phase Needs Safety, Governance, and Human Oversight, AVEVA Technologist Says

Industrial AI is not new. Garg said AVEVA has worked on it for more than 20 years, but the type of AI has changed substantially in recent years. General-purpose foundation models have enabled more people to use advanced AI technologies, while physical AI and agentic AI have expanded rapidly, she said.

Unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems, where an unexpected decision can affect safety, reliability, and critical infrastructure. Garg said the challenge is acute because newer AI systems are more capable but also harder to predict and explain. “How do we leverage these technologies while maintaining safety, while maintaining reliable operations, while still being able to deliver on the promises of the new capabilities?” she asked.

Data is one foundation for managing that transition. Industrial systems often hold information across telemetry, service logs, engineering documents, and other disparate sources. Newer technologies can connect and correlate that information more quickly, giving operators real-time support when diagnosing problems. AI-powered robots could take that further by gathering information in hazardous environments without requiring workers to enter them.

Greater autonomy also requires new approaches to governance. AVEVA’s framework for responsible AI emphasizes security, efficiency, and human safety and oversight. Garg argues that AI should augment rather than replace people in critical decision loops, with guardrails determining where automated systems can act and where human supervisors remain responsible.

Sustainability is another part of the equation. AI can help manage complex power systems as renewable generation grows, while organizations also need better ways to understand AI’s own environmental footprint. Garg is involved in an IEEE working group developing a standard methodology for measuring that impact across electricity, energy, resources, water, and carbon.

The next phase could bring industrial AI further into the physical world, from autonomous robots and drones to AI-assisted coding that allows domain experts to build new applications. Realizing that potential will require more than deploying new technology, Garg said. Organizations will need to rethink business processes, establish appropriate safeguards, and give experienced workers new ways to apply their expertise, creating a model of automation that is not only more autonomous, but safer, more efficient, and more sustainable.

“Autonomous systems, whether they’re robots or drones, are really going to change the way that we work in plants, in power systems, on mining sites,” Garg said. “In a way, that will make these types of operations more efficient, much safer for the human beings involved and more productive.”