Managers aren't ready to lead the AI talent they're hiring

Companies are racing to hire workers who default to artificial intelligence for nearly everything they do. Their managers aren't ready for them.

That's according to a new survey from Indeed and YouGov, which found that 45% of employers are actively recruiting AI-native talent, defined as employees who default to AI to design, execute and scale their workflows. Yet only 45% of managers feel equipped to lead them, and just 13% feel strongly equipped. Nearly half of managers who already lead AI-native employees say their direct reports outskill them.

Companies already recruiting AI-native talent are far more likely to provide AI-related manager training than those that are not, at 88% compared with just 8%. In other words, manager preparation tends to follow the decision to hire, rather than come before it. That reactive pattern is at the center of how organizations are redesigning entry-level roles around AI, often without a parallel plan for the managers who will lead those roles.

Why managers feel behind

Stephan Meier, the James P. Gorman Professor of Business at Columbia Business School in New York, said the confidence split doesn't surprise him. Leading people who use AI well requires a different skill set than using AI itself, he said.

"I think everybody feels a little bit behind. You will never catch up, so will nobody else," Meier said. "They actually need to have different skills than the ones who are actually prompting the AI."

Meier compared it to an earlier workplace technology. A manager whose team members are far better at Excel, with all its macros and shortcuts, doesn't need to match their skill level, because managing someone who uses a tool well is a different job than using the tool itself.

"You don't need to be an Excel wizard to manage people who use Excel," Meier said. "I think the same is true with AI."

Wen Wen, an associate professor at the McCombs School of Business at the University of Texas at Austin, pointed to a structural reason managers lag behind their teams. Employees mostly use AI to complete specific, well-defined tasks, like writing, coding or analyzing data. Managers have a harder job. They have to coordinate an entire workflow that blends human work and AI output across a whole team.

"The team members are doing all those discrete tasks, and then the managers need to think about how to organize this kind of AI-enabled workflow," Wen said. "This is probably one of the reasons managers are behind their teams, because they're managing a much more complex and uncertain problem."

The evaluation problem

Wen's research, including a recent Harvard Business Review study she co-authored on AI-enabled workflows, found that when employees rely heavily on AI, managers lose visibility into what those employees are actually contributing.

"What you see is not really what the employees do individually. It's really about the output from this human-AI collaboration," Wen said, making it harder to evaluate performance or decide who deserves a promotion.

In the past, Wen said, managers could look at a report or a deliverable and reasonably assume it reflected an employee's own thinking. Now, an employee might hand in polished work that's almost entirely AI-generated, and it can look just as strong as work someone did largely on their own. To separate the two, Wen said, a manager would first need a clear sense of what AI alone can produce, then work backward to figure out what the person actually added.

"Those tasks are very hard to even assess," Wen said, because most organizations don't have a clean benchmark for what AI can do on its own in a given role. Without one, she said, managers are often left guessing at how much of an employee's output reflects real skill, which complicates decisions about promotions, team structure and how work gets assigned in the first place.

Training on AI trails behind hiring

Kyle M.K., senior talent strategy advisor at Indeed, based in Austin, Texas, traced managers' lack of readiness to how companies have historically chosen them for the role.

"We typically hire managers who are operationally gifted," he said. "Now with AI, especially with an AI-native team, they've got to focus a little bit more on the output, not necessarily the process itself."

Meier and Kyle M.K. both said manager training hasn't kept pace with how fast AI tools are changing.

"The higher level skill of managing people with those tools is a completely different ballgame than using the tool," Meier said. "I don't think the leaders need to be the best prompters or users of AI agents. They need different skills, and I don't think organizations have caught up with that quite yet."

The survey data backs that up: 62% of employers already offer AI-related manager training, yet 39% of managers say they still need more, a sign that existing programs aren't keeping up with what managers actually need. That mismatch has pushed many managers toward informal, self-directed experimentation instead of structured skill-building, Kyle M.K. said.

"Any progress that's been made today has been due to a DIY culture that's come out of the lack of training," he said. "Folks are just experimenting on their own."

What comes next for managers

Wen said her research points to three priorities for managers: change how you evaluate people, since it's now hard to tell where human skill ends and AI output begins; get ahead of the coordination problems that come from a whole team suddenly producing more work with AI; and make sure junior employees still get the hands-on experience they'll need to become good managers themselves someday.

Wen said what matters more is how managers redesign the work itself.

"What they need to really focus on is how they can redesign the workflow and the job responsibilities," Wen said.

Meier expects the manager's role to keep shifting rather than disappear, as more employees direct their own AI agents and managers increasingly become managers of managers.

"Everybody in your team is also now kind of a manager, because they manage agents," he said. "Coordinating those processes becomes probably harder, but it's the same kind of techniques we used to manage before. Maybe it's just a faster pace."

Kyle M.K. said that shift points toward a different kind of leadership skill altogether, one he calls becoming an "architect of trust": a manager who prioritizes transparency and relational skills over technical mastery of the tools themselves.

"You really strengthen those relational skills, not just the technical skills we've been used to, and be more of a people leader," he said.

The pressure on managers is part of a much larger shift in the workforce. The World Economic Forum's Future of Jobs Report 2025 found that employers expect 39% of workers' core skills to change by 2030, with leadership and talent management among the skills rising in importance. That means the work of redesigning manager training can't wait until hiring catches up. For HRD America's audience, redesigning manager training to keep pace with AI is becoming just as urgent as the push to hire AI-native talent in the first place, as AI reshapes what productivity and performance actually look like across the workforce.

Kyle M.K. said companies that don't adapt their approach to management are taking a real risk. Sticking with old assumptions about what a manager needs to know, he said, means falling behind as the workforce itself changes.

"It would most likely be a bad thing if you continue to operate with the same playbook you might have been using for the last 30 years," he said.