7 Key Lessons from Manufacturing Leaders Driving Digital Transformation

Over the past year, I’ve had the privilege of sitting down with manufacturing executives and innovators on The Manufacturing Intelligence Podcast. From automotive pioneers and electronics leaders to digital transformation strategists and operations veterans, each conversation has shared a common thread: the manufacturing industry is at an exciting inflection point.

I have a common saying, “The technology is the easy part. Governance is where the rubber hits the road.” No surprise, then, that what strikes me most from these conversations is not the power of technology alone. As incredible as AI, digital twins, and connected factories are, the real takeaway from the collective wisdom of these leaders is what it takes to actually make digital transformation stick.

After listening back to these conversations, seven lessons stand out as paramount to this objective.

To say that “technology is the easy part” does not mean that it’s actually easy. It’s not. It’s incredibly laborious to develop technology that actually performs as intended, effectively, and efficiently in the field (or factory). As true as that is, though, digital transformation in manufacturing is not about the latest platform or tool; it’s about people. Change management is an exercise in selling the vision to the people tasked with understanding and implementing it.

Tony Huffman (Koch Industries) described how “people-first” initiatives bridge the gap between abstract technology and daily operations. Jolly Ehiabhi (Otis Elevators) reinforced that real adoption happens only when workers feel included from the start, not when tools are mandated after the fact. 

This theme came up again and again: transformation doesn’t fail because the AI model was inaccurate; it fails because the workforce never trusted it or never saw how it connected to their day-to-day activities. Effective leaders are realizing that adoption is about empathy, communication, and training as much as it is about algorithms and data architecture.

Lesson: If you want technology to succeed, design for trust and adoption before rollout.

Pushpal Jagdale (Qualcomm) emphasized the reality that partial visibility leads to partial results. Today, competitiveness demands an end-to-end perspective– from suppliers & materials, through the shop floor, and all the way to the customer.

Dashboards alone aren’t enough. Visibility is about connecting data silos so that procurement decisions are made with production bottlenecks in mind, and shop-floor insights are used to inform realistic customer delivery timelines. Leaders who can “see the whole chessboard” are moving faster and responding to shocks with agility, while those stuck in silos are finding themselves constantly behind and flatfooted when inevitable disruptions occur.

Lesson: Only by connecting the entire chain can leaders unlock agility and resilience.

Dashboards don’t transform factories. Disciplined measurement does. 

Scalable change starts with enterprise-wide KPIs that align factory and business priorities. Bruce Coventry (former Chrysler, GM, Ford) emphasized this point. He stated that without governance, AI projects stall, no matter how important they are to boards and stakeholders or how powerful the technology appears in a demo.

The difference between a flashy pilot and a scaled transformation is a keen focus on governance. This involves understanding who owns key decisions, how KPIs are defined, and how accountability flows through the organization. 

Several leaders stressed that without disciplined structures, AI becomes another shiny distraction. With it, it becomes an engine for measurable outcomes.

Lesson: Establish KPIs that trigger decisions and governance that ensures accountability.

Marc Rosenmayr (former Hella, Motherson) observed that many manufacturers have reached “peak standardization.” Global systems are vital, but factories also need autonomy to adapt to local realities. Bruce Coventry agreed. Balance comes from incremental AI pilots and empowering local cross-functional teams.

This is a particularly relevant tension for global manufacturers. Standardization enables scale, but over-standardization can stifle responsiveness. Local teams need the ability to respond to cultural nuances, regional regulations, and legacy infrastructure. 

Effective leaders understand that the winning formula is not “standardize everything,” but “standardize where it matters, localize where it counts”.

Lesson: The future is “glocal” — shared platforms with built-in local adaptability.

The aim of most AI deployments currently is to augment the factory as a de facto co-pilot: speeding test setup, highlighting root causes, and accelerating expert decisions. One VP of Operations pushed the vision further, toward role-based AI agents that monitor operations continuously, escalate when needed, and even collaborate with each other. We’re seeing a growing appetite for this in the industry and agent-to-agent collaboration is a nearer reality all the time.

Lesson: The near-term power of AI is augmentation; the long-term horizon is collaborative agent ecosystems.

Leaders like Ronnie Darroch, Bruce Coventry, and Marc Rosenmayr reminded us that digital transformation in manufacturing doesn’t just change products—it changes factories. The rise of software-defined vehicles means factories must be designed for adaptability, continuous updates, and longer platform cycles.

This redefines what a “factory” is. This is a trend made conversational by Tesla, which often describes its plants as “the machine that builds the machine”. Meaning that the manufacturing process itself is designed, refined, and iterated upon with the same level of focus and innovation as that of the end product.

The factory is no longer just an asset where production happens. It’s a platform that must evolve over a decade or more–– supporting software-driven upgrades and new configurations. It’s becoming as dynamic as the products it’s producing.

This new reality requires investment, not just in machines but in resilience, scalability, and automation that can stretch across longer horizons.

Lesson: Treat the factory itself as a product. Invest in making it resilient, flexible, and upgradable.

Technology may be exponential, but adoption is human. László Bajai highlighted the urgency of intergenerational workforce shifts in Europe. Brian White (BAE Systems) and Jolly Ehiabhi stressed the importance of bringing shop-floor workers along early. All of them agree that, without cultural buy-in, even the coolest technology won’t take root.

At the same time, the right technology can make adoption easier by breaking down governance and cultural barriers. When tools are built for the realities of manufacturing they’re nondisruptive to workflows, grounded in accurate data, and able to deliver clear, actionable guidance. As such, they don’t just coexist with culture, they help shift it.

This is where transformation often succeeds or fails. Companies can buy AI platforms, but they cannot change mindsets overnight. Cultural readiness, training, and trust take sustained leadership. Leaders who invest here unlock a critical ingredient for success: momentum. Those who neglect it are often left with tools that never move beyond pilot stage.

Lesson: Culture change isn’t an afterthought— it’s the determining factor of digital success. The right technology can accelerate it dramatically, but it can never replace it.

The common thread within these seven lessons is that transformation is never just about tools or technology. It’s about people, processes, governance, and culture, first. It’s upon that foundation that technology and digital transformation in manufacturing can build and scale. 

As highlighted by these industry leaders, the manufacturing industry has grown beyond simple pilots when it comes to digital transformation. Industry leaders are now scaling it across enterprises as they lead the industry forward.  Those who are not yet embracing this transformation are falling behind. 

I’ll close with a point that crystallized for me in a conversation with a prominent head of operations at a global contract manufacturer. He noted how often leaders “go and figure out something” only to realize later that no one else understands what they’re trying to do. Without pulling people along and creating a shared sense of the common goal, even the best ideas fail to take root.

This underscores one of the deepest truths of transformation. You can be a brilliant systems thinker with a crystal clear vision, but if you don’t bring others along with you by selling the vision, you’re likely to fail. At best, you’ll experience a lack of buy-in for the initiatives. At worst, you’ll be met with active resistance—not because your ideas are wrong, but because people never had the context to see why they mattered. 

Transformation without buy-in isn’t transformation at all; it’s a fight you’ll always lose.

Each of these leaders, in their own way, understands the true heartbeat of digital transformation. The technology is powerful, but it’s people—aligned, empowered, and brought along for the journey—who determine whether transformation scales or stalls.