Industrial AI moves toward safer autonomy

Industrial AI is moving beyond predictive analytics into foundation models, physical AI, and agentic systems that can automate more complex work in plants, power systems, mines, and other operational environments. AVEVA chief technologist Arti Garg says the central challenge is using those capabilities without compromising safety, reliability, or critical infrastructure.

Data integration remains a core requirement. Industrial operators need to connect telemetry, service logs, engineering documents, and other sources so AI can support faster diagnosis and decision-making. Robots could extend that model by collecting information in hazardous areas while keeping workers out of risky environments.

AVEVA’s responsible AI approach emphasizes security, efficiency, human safety, and oversight. Garg argues that AI should augment people rather than replace them in critical loops, with guardrails limiting where systems can act autonomously and where human supervisors retain responsibility. SCG Chemicals is using AVEVA tools and predictive analytics to improve reliability, targeting 99% plant reliability after early pilots showed almost a 9x ROI.

Sustainability is also becoming part of industrial AI governance. Garg is chairing an IEEE working group developing a methodology to measure AI’s environmental impact across electricity, energy, resources, water, and carbon, while AI itself is being explored for grid resilience as renewable power grows.