Chirag Agrawal is a Senior AI Researcher and industrial AI leader with extensive expertise in Artificial Intelligence, Machine Learning, Generative AI, Computer Vision, Data Science, and Smart Manufacturing. He currently serves as Director and Global Head of AI, GenAI, Computer Vision, and Simulation at Novelis Inc., where he leads global AI strategy, innovation, and implementation across multiple regions. His work focuses on applying AI to automation, predictive maintenance, productivity, safety, sustainability, and intelligent manufacturing. He has authored and contributed to research in areas including digital twins, LLM agents, intelligent predictive maintenance, AI governance, computer vision, and multi-agent AI systems. Chirag is a Senior Member of IEEE and a Fellow of IETE and SCRS. He holds an MS in Machine Learning, an MS in Finance, and a BS in Mechanical Engineering from IIT Dhanbad.

Topic: Agentic AI and Gen AI in Industrial and Manufacturing Industry   Generative AI captured the enterprise imagination, but for industrial and manufacturing organizations, the harder and more valuable question is what comes next: moving from AI that responds to AI that acts. Agentic systems, capable of reasoning, planning, and executing across the messy realities of the plant floor, promise a step change in how operations are run. Yet the gap between a compelling demo and a deployed system that holds up in physical, not just digital, contexts remains wide. This session cuts through the hype to examine where generative and agentic AI are already delivering measurable value in industry, from quality systems and process optimization to operational intelligence and decision support, and where they are not yet ready. Drawing on real-world deployments across global manufacturing environments, it explores the architectural, governance, and workforce realities that separate pilots that stall from initiatives that scale. Attendees will leave with a clear-eyed view of the opportunity, a framework for identifying high-value use cases, and a practical understanding of what it actually takes to move agentic AI from proof of concept to production in environments where reliability, safety, and trust are non-negotiable.