As AI Closes Knowledge Gaps, Experience Stops Being a Manager's Edge

The views and opinions expressed are those of Nico Orie and do not represent the official policy or position of any organization.

AI is changing what employees can do on their own, which also changes what managers need to contribute. When nearly everyone can access powerful models capable of producing analysis, ideas, and information on demand, experience alone provides less of an informational advantage. Managers still need deep business knowledge, but their value increasingly depends on helping people develop the judgment, creativity, and critical thinking required to use those tools well.

Nico Orie, VP People and Culture, Global Operations and AI Strategy at Coca-Cola Europacific Partners, has spent more than three decades working through large-scale organizational and technology change. Before joining CCEP, he led the global rollout of Workday across 70 countries and roughly 120,000 employees at Philips. His current remit sits across the people and technology questions companies are confronting as AI changes how individual jobs are performed.

“To really apply AI, you need to know a bit more about the technology than you actually want,” Orie says. Managers don’t need to become engineers, but they do need enough technical understanding to judge how AI should fit into the work. 

Why managers can no longer rely on experience alone

For much of a manager’s career, accumulated experience created an information advantage. Someone who had spent years in a function generally knew things a newer employee didn’t, and passing down that knowledge formed part of how teams developed. AI is introducing changes to that dynamic.

Orie argues that every employee will increasingly have access to an amount of knowledge that would have been difficult to imagine even a few years ago. He also expects less differentiation between large language models as their core capabilities converge, placing more weight on what people do with the information available to them. The shift puts greater pressure on managers to develop qualities that access to information alone can’t provide. ”It really comes back to the human element and our capacity for innovation and creativity,” Orie says. 

Functional expertise remains important, but expertise by itself becomes a weaker definition of good management when employees can use AI to close knowledge gaps quickly. The harder managerial task is helping people decide when an answer is useful, where judgment needs to override it, and how to create value beyond what the technology can generate on its own.

The work AI replaces is often where foundational learning starts

Lower-level assignments often appear easiest to automate because experienced employees can complete them quickly. Yet repeated exposure to those tasks gives less experienced employees opportunities to build judgment, recognize patterns, make mistakes, and learn how decisions are made. “We lack a bit the ability to teach people those skills, to develop those skills, because AI is so addictive,” Orie says.  

Removing the work can remove part of the development path attached to it. Companies therefore have to think more deliberately about the learning environments they create for employees. “We really have to think about, as companies, how do we create those learning environments for the leaders, the future leaders, but also for the new staff coming in?” Orie says.

AI fluency as part of the manager’s job

Managers face their own version of the same learning problem. Orie sees a gap between the confidence with which many leaders discuss AI and their understanding of how the technology works. “A lot of managers really lack a bit deeper knowledge on AI, even as they’re expected to speak confidently about it,” he says. In his view, applying AI effectively requires managers to understand more of the underlying technology.

Managers increasingly have to make decisions about where AI fits into a role, what employees should own, and how work should change as the technology improves. Looking only at the final output leaves them poorly equipped to make those calls because they can’t distinguish effective AI use from weak processes that happen to produce acceptable results.

Orie compares the current situation with earlier stages of technology adoption, when users felt a stronger need to understand the technology beneath the interface. “It’s a bit like when the computer first arrived. Everyone wanted to know how it worked. The same was true with processors and cars. Perhaps in five years, AI will be so common that this changes. But right now, because the technology is developing so fast, you need to understand more about its basic concepts,” he explains.

Future leadership depends on bridging people, business and AI

Orie’s view of the role ultimately brings together stronger people management, continued focus on the business, and deeper AI knowledge. None operates independently. Technical understanding helps managers make better decisions about the work, while people leadership determines whether employees can develop the human capabilities that become more important as AI handles more of the execution. 

Leaders need enough AI fluency to understand how the work is changing, while also becoming better at cultivating critical thinking, creativity, innovation, and the other human capabilities. “If we no longer have the places to learn those along the way in our career, then how will we be able to develop future leaders?” Orie concludes.