Building a safer path to autonomous industrial AI

THE NEWS

What happened

Industrial AI is entering a transformative phase driven by foundation models, physical AI, and agentic systems, enabling automation of complex tasks in critical infrastructure. However, unlike digital-only AI, industrial applications interact directly with physical systems, making safety and reliability paramount. AVEVA's Arti Garg emphasizes that responsible deployment requires new governance frameworks where AI augments rather than replaces human oversight, ensuring guardrails exist for automated actions. Key challenges include integrating disparate data sources (telemetry, logs, engineering docs) for real-time diagnostics and managing the environmental footprint of AI itself. The industry is shifting from predictive analytics to autonomous robots and drones that can operate in hazardous environments, reducing risk to workers while improving efficiency. Realizing this potential demands rethinking business processes, establishing safeguards, and empowering experienced workers to apply their expertise alongside new technologies.

CONTEXT

Why it matters

Industrial AI is evolving rapidly. With a 78% adoption spike, new autonomous systems now operate in critical infrastructure. The challenge? Ensuring safety and reliability when AI interacts with the physical world. Experts say we need 'responsible AI' that augments human judgment, not replaces it. Key focus: integrating data for real-time diagnostics and measuring AI's own environmental footprint.

AT A GLANCE

Key facts

• Industrial AI adoption has seen a near 78% increase in the industrial sector over the past two years, marking a significant inflection point.
• Newer AI systems like foundation models and agentic AI are harder to predict, necessitating robust governance frameworks that prioritize human safety and oversight.
• AVEVA advocates for a 'triple mandate' of responsible AI: security, efficiency (including environmental), and preservation of human safety and oversight.
• Advanced technologies now allow AI to correlate disparate data sources like telemetry, service logs, and engineering documents in real-time for rapid diagnostics.
• Autonomous robots and drones can gather data in hazardous environments, reducing the need for human workers to enter dangerous spaces.
• A new IEEE working group is developing a standard methodology to measure AI's environmental impact across electricity, water, carbon, and resources.
• The transition requires organizations to rethink business processes and create models of automation that are safer, more efficient, and sustainable.