Jonathan Wise, Chief Technology Architect at CESMII and veteran leader across Rockwell Automation, Microsoft, GE, and Amazon, joins The Manufacturing Intelligence Podcast to unpack what it really means to be “AI-ready” in manufacturing and what it takes to build real-world ai for manufacturing that works.

Drawing from decades of experience and over 50 digital transformation projects, Jonathan discusses the hidden complexity beneath data structures, why unified namespaces aren’t enough, and how AI success depends on the right semantic and ontological foundation. He also offers a refreshingly honest perspective on standards adoption, hallucination risks in LLMs, and how to bridge both technological and generational divides on the factory floor.

• Why semantic consistency is the missing link in most manufacturing AI efforts
• How ontologies and knowledge graphs unlock decision automation
• The limitations of current standards and how modular “Lego block” approaches can work better
• Lessons from CESMII’s digital transformation projects across U.S. manufacturing
• The disconnect between LLMs and messy, real-world industrial data
• Strategies to capture tribal knowledge and embed it into next-gen AI tools
• Bridging generational divides between OT experts and digital-native workers
• Why repeatability is key to successful AI implementation
• The future role of humans in AI-augmented factory environments

What does it take to move generative AI from pilot projects to real productivity in complex automotive environments? Rick Sturgeon, global engineering and technology leader, brings a practitioner’s view to this question, shaped by decades of experience leading transformation at General Motors, Johnson Controls, Dassault Systèmes, Infosys, and others.

Rick shares what he’s learned from helping large OEMs modernize legacy systems, scale digital platforms, and implement AI in the real world. He explores how manufacturers can unify siloed knowledge across thousands of systems, the importance of expert oversight in tuning AI models, and why success depends on more than just technology.

From aligning engineering teams around change to unlocking cross-functional productivity with AI, Rick offers grounded, forward-looking advice for manufacturers ready to move beyond buzzwords and start delivering value at scale.

Whether you’re a product development leader, transformation strategist, or operations executive, this episode delivers hard-earned lessons on building the next generation of connected, intelligent factories.

• Why generative AI is different from previous technologies—and what that means for implementation
• How to modernize engineering workflows without getting stuck in legacy complexity
• The hidden value of cross-system knowledge and how AI can scale it
• Common blockers that prevent pilot projects from turning into enterprise impact
• What it really takes to empower teams for AI-driven change
• Why domain expertise and model tuning are essential to trustworthy AI
• The new metrics for measuring generative AI success in manufacturing
• What’s next for AI in automotive product development and production

In Part 2 of our conversation with Bruce Coventry and Marc Rosenmayr, we turn to the future: where software-defined vehicles, flexible platforms, and data-driven manufacturing are redefining what’s possible in automotive.

Bruce and Marc discuss how the shift from hardware-first to software-first design is changing supplier strategy, why standardization with room for upgrades is key to future-proof systems, and how leading manufacturers are beginning to treat data itself as a core asset.

In part 2 of this episode, you’ll learn:

• How software-defined vehicles are reshaping the automotive supply chain

• Why smart platforms must balance current performance with future flexibility

• How to align hardware decisions with evolving consumer use cases

• What it means to treat data as a core manufacturing asset

• How electronics manufacturing leaders like Jabil and Flex use data to drive innovation