Manufacturing Intelligence

Building Real-World AI for Manufacturing with Jonathan Wise

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.
Topics Discussed:
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

Beyond the Pilot: Turning Generative AI into Real Results in Automotive Manufacturing

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.
 
Topics Discussed:
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

Transforming Automotive Manufacturing: AI & The Future of Mobility - Part 2

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

CIO at the Helm: Scaling AI and Digital Transformation Across the Enterprise

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.
Topics Discussed:
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

Transforming Automotive Manufacturing: AI & The Future of Mobility - Part 1

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

Digital Transformation in Manufacturing: Michal Wierzchowski on Building Intelligent Operations

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.
Topics Covered in This Episode:
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

Modernizing Legacy Manufacturing Systems: Bridging AI and the Shop Floor with Right-Sized Technology

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
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Novelis’s Pushpal Jagdale on AI Business Cases That Operations Will Approve

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.
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80/20 Rule: Why Smart Manufacturing Culture Beats Tech

The most honest assessment of manufacturing leadership comes from someone who's lived through four decades of industry transformation — and our guest in this episode of Manufacturing Intelligence. Ronnie Darroch, former EVP and CTO at Plexus, doesn't sugarcoat the reality: technology is the easy part, getting people to act on insights is where most digital transformations fail.
Ronnie's career from component-level PCB repair to global executive reveals a consistent pattern: discretionary effort, not technology, determines competitive outcomes. His contrarian 80/20 rule advocates spending 80% of effort on culture and management processes, only 20% on connectivity. The conversation explores his systematic approach to global collaboration, AI governance frameworks, and why customer trust architecture matters more than contracts in low-margin manufacturing.
Topics discussed:
The discretionary effort framework for competitive advantage, explaining why voluntary team performance differentiates companies and how leaders create conditions to inspire it through authentic relationship-building rather than traditional management.
The 80/20 smart manufacturing rule prioritizing culture over connectivity, demonstrating why "getting data is the easy part" while building management processes to act on insights requires systematic cultural transformation.
Metrics management Hippocratic oath introducing the "do no harm" principle, including real examples of misaligned incentives destroying production flow and the diagnostic question: "Is your metrics system making you more competitive?"
Global collaboration systematic approach for multi-regional leadership, covering strategic face-to-face kickoff investments, rotating governance schedules, and leveraging diversity to achieve "1+1=3" team performance across cultures.
AI governance without stifling innovation, addressing artificial intelligence's trust problem through human accountability frameworks while leveraging technology for productivity gains, referencing "The Atomic Human" for manufacturing leaders.
Customer trust architecture mechanics in contract manufacturing's 10% margin environment, breaking down how every interaction builds or erodes relationships and why collaborative problem-solving outperforms contractual enforcement.
Early career acceleration principles from promotion to department manager at 24, emphasizing how "someone must give you a chance to lead" and why hands-on work understanding creates lasting team credibility.
Single-piece flow intuition development from PCB debugging experience, illustrating how cellular manufacturing with immediate feedback can emerge naturally when questioning established batch processes.
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NI’s László Bajai on Why Technology Is Digital Tranformation’s Smallest Challenge

The most honest assessment of factory modernization you'll hear comes from someone who's lived through decades of digital transformation failures and successes — and our guest in this episode of Manufacturing Intelligence. László Bajai, Factory Modernization Global Leader at National Instruments, doesn't sugarcoat the reality: companies claiming to be "super digital" often still use paper to assemble AI-driven products. 
László's early realization that he wanted to improve engineering processes rather than just implement solutions set the foundation for a career focused on domain understanding first, automation second. This perspective shapes his approach to every modernization project, where he consistently prioritizes people, then process, then technology. The conversation also explores his three non-negotiable pillars for factory modernization: brutal honesty about current maturity levels, comprehensive human engagement across all organizational levels, and flexible openness to external solutions. 
Topics discussed:
The importance of multidisciplinary expertise in manufacturing modernization, combining domain knowledge with technical skills rather than pure implementation focus to drive meaningful process improvements.
Why Plan B and Plan C thinking becomes essential for project success, illustrated through real examples of last-minute personnel changes and how preparation for multiple scenarios ensures delivery even when primary strategies fail.
Human-centered change management strategies that address resistance from engineering teams, including techniques for buying in stakeholders, involving them in testing phases, and managing expectations across cultural and geographical boundaries.
Model-based enterprise frameworks that eliminate traditional 2D drawing workflows, replacing annotation-heavy processes with data-driven approaches that enable end-to-end traceability from customer requirements through manufacturing and quality.
Smart scale integration methodologies for inventory precision improvement, demonstrating how MES systems connect real-time material consumption data to ERP systems while revealing underlying process problems that digitization exposes rather than solves.
Three pillars of successful factory modernization: maturity assessment honesty, comprehensive human engagement, and flexible openness to external solutions, emphasizing why technology selection becomes the smallest challenge when foundational elements are properly addressed.
Global stakeholder management tactics requiring embedded local champions, ensuring project knowledge and decision-making authority exists at every geographic location rather than centralizing expertise at headquarters.
Enterprise-level AI vision for competitive advantage creation, connecting machine-level OT data through ERP and SharePoint systems to enable organization-wide intelligent query capabilities that transform data accessibility and decision-making speed.
Why technology must prove itself innocent rather than being assumed guilty, addressing the natural human tendency to blame new systems first and strategies for building trust through transparent data quality and systematic problem-solving approaches.
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