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

What does it take to lead enterprise-wide AI transformation in one of the world’s largest manufacturing environments? May Yap, Senior Vice President and Chief Information Officer at Jabil, brings a unique C-level perspective to this question. Her view is grounded in experience scaling secure, governed, and trusted AI across more than 100 global sites.

In this episode, May shares how her team built the foundation for responsible AI at scale by aligning data ownership across the business, establishing centralized governance models, and empowering regional innovation. She discusses how AI is being applied beyond the factory floor into areas like finance, HR, and supply chain, and how CIOs today must act as both technologists and business strategists.

From creating a cross-functional data and AI council to launching organization-wide education on AI policy, May outlines the critical ingredients for success. She also explores the evolving role of the CIO as a change agent, shaping how modern manufacturing organizations operate, learn, and grow.

Whether you’re a technology leader, operational strategist, or digital transformation professional, this episode offers a roadmap for embedding AI into the core of your enterprise.

• How the CIO role is evolving from systems leadership to enterprise enablement
• The power of a cross-functional data and AI council in scaling trust
• Why FAIR data principles (Findable, Accessible, Interoperable, Reusable) matter for AI success
• The three strategic focus areas for AI investment in manufacturing
• How to empower regional teams without sacrificing governance
• Tactics for rolling out enterprise-wide AI policy and education
• Lessons learned from migrating decades-old systems to unified cloud platforms
• What the future of manufacturing looks like when AI is built into the fabric of the enterprise

What’s standing in the way of digital transformation on the factory floor? In Part 1 of this two-part episode, Bruce Coventry and Marc Rosenmayr unpack the deeply rooted challenges holding back progress, from legacy equipment that lacks basic connectivity to safety-driven skepticism about letting AI influence production decisions.

Drawing on their leadership experience at GM, AVL, Siemens VDO, and Magna, Bruce and Marc explore why trust is the cornerstone of successful modernization. They explain how manufacturers can start small with AI, focus on practical wins, and build confidence one use case at a time.

In part 1 of this episode, you’ll learn:

• Why legacy systems still dominate many production environments

• How safety concerns shape attitudes toward AI adoption

• Why many AI projects stall without strong operational buy-in

• How to run high-impact pilots that build trust and unlock value

• The critical role of governance and standard processes in enabling transformation

What does digital transformation in manufacturing really look like on the ground? Michal Wierzchowski, Vice President of Operations & Digital Transformation, shares his perspectives on how manufacturers can evolve into intelligent, connected, and AI-enabled enterprises. Rather than focusing on abstract concepts, Michal grounds the conversation in practical realities, including how technology, process, and people come together to deliver measurable improvements in speed, quality and resilience.

With decades of experience across supply chain, materials, and systems optimization, Michal brings a unique lens to transformation that spans from ERP and planning systems to the shop floor where products are built. He discusses how to unify planning and production, empower teams with better data, and prepare factories for the next era of intelligent operations. His insights highlight both the strategic frameworks and the cultural shifts needed to make digital transformation stick, ensuring that innovation scales beyond pilot projects into sustainable enterprise-wide change.

• What digital transformation means in practical manufacturing terms
• How to balance global standardization with local execution
• Turning raw machine data into enterprise intelligence
• Why supply chain leaders are driving digital transformation efforts
• The role of multi-agent systems in modern factories
• How to identify and work with the right digital partners
• Scaling real-time decision-making across operations
• The future of connected, AI-enabled manufacturing ecosystems

Jeevan Mulgund has spent decades driving transformation across industrial manufacturing. He led Honeywell’s global supply chain footprint and now invests in next-generation U.S. manufacturers through IGNOVA Ventures.

In this episode, Jeevan joins Andrew and Jennifer to unpack the real ingredients of successful transformation. He explains why right-sized technology only works when paired with trust, team diversity, and clear business objectives. He shares how he earned credibility by programming CNC machines himself and how to approach modernizing legacy manufacturing systems without having to wait a year to see results.

Whether you’re digitizing a single factory or rethinking a multi-site network, this conversation is packed with insight on how to apply right-sized technology while modernizing legacy manufacturing systems in a way that solves real problems.

Topics discussed:

• Change management as the foundation for transformation
• People, process, and technology as a decision framework
• Reducing time to value through edge-in approaches
• Right-sizing technology for small and mid-sized manufacturers
• Lessons from quoting systems, dashboards, and global factory visits
• Training AI agents to support planners rather than replace them
• How legacy systems and modern AI can work together
• Workforce development and how to make manufacturing attractive to a new generation
• IGNOVA Ventures’ mission to preserve and grow U.S. manufacturing

Listen to more episodes:

Apple

Spotify

YouTube

Website

Novelis's IT BRM of Manufacturing & Operations Pushpal Jagdale’s approach to manufacturing transformation challenges the technology-first mentality that derails most digitization efforts. His experience scaling connected systems across hundreds of machines at Honeywell, then transitioning to safety-critical AI applications at Novelis, illustrates why business alignment trumps technical sophistication in successful deployments.

Pushpal shares his methodology for cataloguing legacy assets before attempting any connectivity projects, explaining how understanding which machines can actually integrate with enterprise networks prevents expensive pilot failures. The conversation with Jennifer and Andrew also explores how safety-focused AI applications create the strongest business cases in heavy manufacturing environments.

Topics discussed:

• Asset cataloguing methodologies for legacy system integration before attempting digital transformation initiatives across global manufacturing operations.
• The strategic approach to AI business case development that starts with personal productivity applications like meeting transcription before advancing to factory-specific implementations.
• Safety-first AI deployment strategies using autonomous inspection robots in hazardous environments to eliminate operator exposure risks.
• Global scaling frameworks for digital transformation that account for non-standardized assets, processes, and product portfolios across international facilities.
• IT-OT alignment strategies that transform traditional client-vendor relationships into true partnerships through shared vision and collaborative execution models.
• Data strategy fundamentals as the foundation for successful AI deployment, including quality requirements and contextualization of legacy equipment outputs.
• Predictive maintenance applications using sensor-mounted machine learning models that integrate vibration, temperature, and torque data with product and process information.
• The evolution from discrete electronics manufacturing to metals processing and how core operational objectives remain consistent across different manufacturing environments.
• Knowledge management AI systems that convert SOPs and training materials into searchable troubleshooting resources for new engineers working with legacy equipment.

Listen to more episodes:

Apple

Spotify

YouTube

Website