AI Use Cases in Manufacturing: Three Real-World Lessons from László Bajai

Not every conversation about digital transformation feels real. But when László Bajai, Factory Modernization Global Leader at National Instruments, sat down with the Manufacturing Intelligence Podcast team, the sugarcoating came off.

László has lived through both the successes and failures of manufacturing modernization for over two decades. And instead of leading with flashy AI tools, he focuses on the gritty foundations that too many teams ignore.

“Each and every organization must be honest [with] itself about the maturity level, from a digitalization point of view… Most organizations do not measure themselves. They just start launching projects, saying they are so digital, but they do not know why they are doing it… First, you need to measure your own maturity level.”

This is where many AI use cases in manufacturing go sideways before they even start. László’s approach always begins with assessing what’s actually true about your operations. Only then can a roadmap reflect what’s needed instead of what’s fashionable.

Getting this wrong does more than slow progress. It leads to wasted budget, unmet expectations, and long-term credibility issues.

László shares a story from his first major implementation: a three-year initiative jeopardized when his lead developer resigned two months before launch.

“In half an hour, we found more than one solution… and finally we could finish on time… You must always have a plan B and a plan C. Never start a project with only one option, because nowadays, something is always changing.”

The lesson is not just about software development. Every successful AI project should include contingency strategies for tools, team members, and project timelines.

Despite working on the cutting edge, László is clear-eyed about the biggest barrier to progress.

He insists on three pillars for any modernization effort. The first is an honest assessment of current maturity. The second is complete engagement with people at all levels. The third is being open to external solutions rather than reinventing the wheel.

Only after those three are in place does it make sense to choose the right technology.

It’s not that László downplays AI. He sees huge potential for AI use cases in manufacturing. But that potential only matters if it’s grounded in process understanding and trusted by the people who use it.

“I truly believe in the power of [AI]. But still, big companies are not flexible enough to let AI come into our daily lives. That is a competitive disadvantage.”

As the conversation draws to a close, one theme becomes unmistakably clear: real modernization is not a technology project. It is an organizational shift. László reminds us that transformation efforts often fail because teams underestimate the difficulty of managing expectations, aligning incentives, and harmonizing processes across global sites.

His approach is refreshingly grounded. Rather than chasing every emerging tool, he focuses on identifying root causes and building systems that can adapt to real-world complexity. Whether it’s managing MES to ERP connections with precision or reshaping outdated workflows through model-based methods, the message remains consistent. Technology delivers value only when it is anchored in clarity, trust, and flexibility.

László’s perspective is not built on theory. It is shaped by decades of lessons, tested under pressure, and refined through experience. His reflections serve as a powerful reminder that the most valuable AI use cases in manufacturing are not just innovative. They are resilient, human-centered, and grounded in operational truth.

Listen to the full episode, Why Technology Is Digital Transformation’s Smallest Challenge with László Bajai.