Building a safer path to autonomous industrial AI
Industrial AI is entering a new phase. After decades of predictive analytics and other specialized applications, advances in foundation models, physical AI, and agentic AI are making it possible to automate more complex tasks across industrial environments. But unlike AI that operates purely in the digital world, industrial AI can interact directly with physical systems,…
Industrial AI is undergoing a transformation, moving beyond specialized predictive analytics to encompass foundation models, physical AI, and agentic AI. These advancements enable automation of more complex tasks in industrial settings, but pose safety and reliability challenges. Arti Garg, chief technologist at AVEVA, emphasizes the importance of responsible deployment to maintain safety and reliability while harnessing the potential benefits of industrial AI.
One key aspect is data connectivity and correlation, which can provide real-time support for operators diagnosing problems. AI-powered robots can gather information in hazardous environments, but this increased autonomy requires new governance approaches. AVEVA's framework for responsible AI focuses on security, efficiency, and human safety oversight, ensuring AI augments rather than replaces human decision-making.
Sustainability is also a crucial consideration, with AI playing a role in managing complex power systems as renewable generation increases. Garg notes that realizing the full potential of industrial AI will require organizations to rethink business processes, establish safeguards, and empower experienced workers to leverage their expertise, ultimately creating a safer, more efficient, and sustainable automation model.
Written by urgent.news from MIT Technology Review's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
