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AI in Industry: Leaders from AWS, NVIDIA, Siemens & Composabl on What’s Next | Automate 2025

Is generative AI just a buzzword, or is it starting to deliver real impact in manufacturing and industrial automation? In this candid panel from Automate 2025, leaders from Amazon Web Services (AWS), NVIDIA, Siemens, and Composabl explore how AI is being applied across the factory floor today. From large language models and copilots to autonomous agents and physical AI, the panel discusses what’s working, what’s still aspirational, and what it really takes to implement AI in environments where uptime and safety are critical. You’ll get actionable insights on workforce transformation, data infrastructure, simulation, and the growing role of engineers in industrial AI. 🔍 Topics covered: • What GenAI can and can’t do in industrial environments • Why engineers are key to successful AI adoption • Using synthetic data and simulation for continuous learning • Common misconceptions about LLMs and decision-making • Making operational data more accessible • Real use cases like AI-powered machine troubleshooting • Workforce training and human-centered AI strategies • What’s still missing: safety standards, benchmarks, and “Six Sigma for AI” 👥 Panelists: • Kence Anderson, CEO and Founder, Composabl, Inc. • Christi DeCuir, Sr. Director, Robotic Strategic Alliances, NVIDIA • Dianne Eldridge, Worldwide BD/GTM Leader for Industrial AI/GenAI, Amazon Web Services • Bernd Raithel, Director, Product Management, Siemens 🎙️ Moderator: Brian Heater, Managing Editor, A3 🔔 Subscribe for more conversations from Automate 2025 ⏱️ YouTube Chapters / Timecodes 0:00 – Panel kickoff + introductions 0:22 – What each company is doing with industrial AI 2:47 – Why Siemens, AWS, and NVIDIA are restructuring around GenAI 4:44 – The Super Bowl Problem of generative AI hype 6:02 – Engineering vs. AI research mindsets 8:20 – Synthetic data + simulation loops at NVIDIA 13:57 – The importance of domain expertise + engineering rigor 15:15 – Diane on 3 customer mindsets: Talkers, Testers, Transformers 19:31 – What is agentic AI? 21:03 – Educate workforce on AI 23:18 – Democratizing access to sensor data for engineers 25:07 – How to capture and codify expert knowledge (Machine Teaching) 29:47 – Machine co-pilots + real-world ROI in AI deployments 32:23 – The need for standardization and methodology in AI 34:12 – Audience Q&A: Data infrastructure and modeling challenges 38:58 – Final thoughts + closeout