Companies Race to Hire AI-Skilled Employees — But Managers Don't Know How to Lead Them

Employers are moving quickly to bring AI-native workers into their organizations, but managers are not moving at the pace they should. A new Indeed and YouGov survey puts a number on that huge disconnect, and it is a wide one.

From the report, 45% of employers say they are currently hiring for AI-native skills. On the other side of that same org chart, only 45% of managers say they feel ready to lead the people they just hired. Go a bit closer again, and the number gets smaller still, with only 13% strongly agreeing that they're equipped to manage AI-native employees. And strangely enough, nearly half of employers agree that direct reports often have stronger AI skills than their bosses. 

Most Workers Admit They Lack AI Fluency

The numbers from the new survey tell a consistent story: at every layer of the workforce, 14% of workers describe themselves as being “AI native" — a skill that 45% of employers say they're hiring for right now. That means a small supply of verified talent is chasing much higher demand.

Fluency looks different depending on who you ask and how old they are. Gen Z, the youngest workers, feel most comfortable, with 35% calling themselves AI fluent. Millennials come in at 24%, Gen X sits at 11% and Boomers at a paltry 2%.

The overall workforce average is 19%, and it’s the number employers should have in mind when they write a job post that assumes AI flunecy as a baseline.

Employees Say They're Uncomfortable With AI Use Mandates

Data also reveals employer expectations are far beyond what employees say they can do.

Nearly three-quarters of employers expect staff to use AI tools for individual tasks, and worker comfort ranks close behind at 66%. The gap widens fast after that: 58% of employers expect workers to automate repetitive workflows, but only 27% of workers say they're comfortable doing it.

For building or managing AI agents, employer expectations sit at 41%, compared with worker comfort at just 14%. Developing or customizing AI tools shows the same split: 41% expected, 13% comfortable.

AI use itself remains uneven across the workforce, adding another layer to the mismatch. Nearly half of workers say AI handles 10% or less of their daily tasks. Only 9% say it handles more than half. Taken together, about 49% of workers use AI for between 10% and 90% of their workload, a wide range that makes it hard for any manager to assume a fixed baseline for what a direct report is actually doing with these tools. 

Hiring AI Talent Outpaces Organizations' Abilities to Manage It

Timing is at the root of the disconnect between companies hiring AI talent that managers cannot lead, said Kyle M.K., Indeed's senior talent strategy advisor.

“A lot of this comes down to the hiring side and the leadership side moving on two different timelines. Companies moved quickly to bring in AI-native talent because the advantage was obvious. 59% of employers call it essential or important, and when you ask why, more than half point straight to productivity and efficiency.”

Managers, in his view, are catching up to a team they were not trained to lead. Only 13% feel strongly equipped to manage AI-native employees, and many of them are now overseeing people who use AI more fluidly than they do themselves, and in ways that never appeared on their career paths.

That creates an unfamiliar situation for a manager. Someone used to reviewing a report or a piece of code line-by-line now has to evaluate a workflow they did not build and might not fully follow. M.K. described it as a shift from checking work to auditing judgment. Managers have to ask what the AI was given, what got changed by hand and whether the result holds up.

He also pointed out that most manager training right now covers general AI literacy, when what managers actually need is confidence: how to evaluate work they cannot fully replicate themselves, how to ask better questions and how to tell when a result is genuinely good.

Until training makes that shift, he said, hiring will keep outrunning the organization's ability to lead the people it hires.

More Data Backs Up the Management-Employee Mismatch

Indeed and YouGov’s Survey is not alone in uncovering this disconnect.

A 2026 Acorn report, which surveyed more than 1,200 executives and employees in the US, Canada and Australia, also found a huge gap between what leadership thinks is happening and what employees say is occurring.

While 77% of execs believe their managers are prepared to guide AI skills development, 91% of individual contributors disagreed. This structural disconnect explains why organizations do not see meaningful gains in skills despite heavy investment in AI training.

According to Blake Proberts, founder and CEO of Acorn, what this research makes clear is that two workforces are experiencing the same AI deployment from fundamentally different positions. “The deficiency in manager preparedness highlights a measurement infrastructure problem. Managers can’t guide development conversations they have no evidence to anchor on, and without that evidence, employees default to skepticism,” he said.

The reasons organizations are struggling to assess AI competency come down to a lack of definition. Most organizations have no formal mechanism to define, assess or track AI capability at the individual level.

The Acorn report found that 34% of companies have not defined AI competencies at the role level, and 47% have not built AI capability into formal performance reviews at all.

Two separate surveys, run by two different organizations, arrive at the same basic finding: Executives and employers believe managers are more prepared than managers actually feel, and the people reporting to those managers notice the difference first.

Manager Readiness Is Split Down the Middle

The survey data from Indeed and YouGov also revealed something quite interesting about managers' readiness to lead AI-native talent — there's a huge split, with managers themselves almost evenly divided.

From the survey, 45% say they feel equipped to lead AI-native talent, while 43% say they are not, close to a coin flip on a question that determines how well a growing share of the workforce gets managed day-to-day.

Direct exposure changes the picture among managers who already lead AI-native talent; nearly 9 out of 10 say they feel equipped to manage this class of talent. Additionally, 88% of employers already recruiting for AI natives provide manager training, compared to only 8% who are not.

Comfort with the skill gap itself also grows with exposure. Nearly half of managers who currently lead AI-native talent are open about the fact that their direct reports outskill them in this area. Among managers overall, only 26% say the same thing. The managers closest to the work are also the most honest about where they stand.

Training investment backs this pattern up. While 62% of employers provide AI-related training for executives, 39% of managers say they need more, and employees who self-identify as AI-native are 5x more likely to say they receive enough training (77%) than those who don't (15%).

According to Hannah Calhoon, VP of AI at Indeed, the most successful enterprise training programs are role-specific rather than generic. Instead of throwing AI at everything, she said, there are two specific questions you need to ask:

1. What is the specific business problem we’re trying to solve?
2. Then, is AI actually a good fit for it?

To back this up, Calhoon said her team at Indeed equips software engineers, marketers and legal staff with different tools, since each group does different work and a shared, generic toolkit rarely serves any of them well.

Experts Share How Organizations Can Close the Gap

M.K. identified a specific starting point for organizations seeking to close this gap: A hands-on training program built specifically for managers with AI-native direct reports.

"Seven in ten workers are already experimenting with AI at work on their own, so managers are walking into teams that are already building fluency day to day, and the training has to meet them there."

He also argued the skills that matter most in this kind of leadership have not changed: Strategic thinking, psychological safety and creative judgment still carry a team, regardless of who in the room knows the most about a given AI tool.

The second piece, in his view, is to build policy together rather than to hand managers a set of rules after the fact.

“Pull managers into the room with HR and IT early, so the people dealing with these questions on their teams every day help decide what's expected, what's acceptable and where a manager needs to step in," said M.K. "Those answers look different for a sales team than an engineering team, and managers should have a hand in shaping them from the start.”

Organizations, he added, should normalize managers agreeing that a direct report has skills they don’t possess, and treat that as ordinary information rather than a weakness to hide. Managers have always led people who know what they do not. AI makes the gap easier to see.

Building the Leadership Side Alongside the Hiring Side

The pattern across both surveys points to the same conclusion. Hiring AI-native talent solves one part of a company's problem and creates another one immediately after. A team that is highly capable with AI still needs a manager who can evaluate that work, ask the right questions and coach people whose technical skills may already exceed their own.

Organizations that build manager readiness alongside their AI hiring plans end up with managers who trust their own judgment and teams that trust them in return. That requires organizations to have role-specific practice, policies built with the managers who will use it and a workplace culture where a manager can agree that a direct report knows something they do not.

Until organizations normalize this, they’ll continue hiring AI talent that managers cannot lead.