Manufacturing Intelligence

Human First Leadership in AI-Driven Manufacturing | Fernando Guedes

Fernando Guedes, Global Manufacturing Engineering Manager at Aptiv, joins The Manufacturing Intelligence Podcast for a deeply human and practical conversation on leadership, learning, and how manufacturers can navigate rapid technological change without losing what matters most.
Drawing from more than 30 years in electronics manufacturing, Fernando traces his journey from shop floor technician to global engineering leader and shares how hands-on experience, teaching, and empathy shaped his leadership philosophy. He explains why trust and mental health are foundational to performance, how continuous improvement frameworks like Six Sigma still apply in an AI-driven world, and why real-time data has fundamentally changed the speed of decision making.
Throughout the episode, Fernando offers a grounded perspective on AI and automation, emphasizing that technology should enable people, not control them. He discusses how leaders can use data as a signal for opportunity rather than blame, why lifelong learning is essential, and how manufacturers must prepare teams for significant workforce and skills shifts in the coming years.
Topics Discussed:
Leading global manufacturing teams through trust and human connection
Learning by doing and why shop floor experience still matters
Teaching as a way to scale expertise and build empathy
Why Six Sigma and Lean still work, but faster than ever
Using data as opportunity rather than judgment
How AI and real-time analytics change continuous improvement
Preparing for workforce shifts driven by automation and AI
The importance of curiosity, adaptability, and lifelong learning
What excites Fernando about the future of manufacturing leadership

Professional Growth to Global Manufacturing Engineering Manager in SMT | Guido Heuser

Thirty years. Nokia. Flex. Vario Systems. HARMAN Automotive. And a career that started on the line as an operator.Guido Heuser doesn’t talk about manufacturing from a boardroom, he talks about it from the shop floor up. As Manager of Global Advanced Manufacturing Engineering at HARMAN Automotive, he oversees quality, OEE, and process standards across factories on multiple continents. And he has some hard-earned things to say about where AI fits in and where human judgment still has to lead.In this episode, Guido walks us through the real tension between quality and cycle time (and how data-driven insights can actually resolve it), how to make global standards stick across cultures without imposing them top-down, and why the next generation of engineers should start from scratch — on purpose.He’s also refreshingly direct about AI: fully open to it, genuinely excited, and absolutely clear that curiosity and critical thinking must remain at the center of how we innovate.You’ll hear:
How HARMAN prevents quality escapes before they become field issues
Why ownership beats enforcement when rolling out global standards
What US and German manufacturing cultures can learn from each other
The AI stance every experienced practitioner should have right now
Career advice that compounds slowly — and pays off for decades

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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