Forty-two percent of employees say their employer expects them to learn AI on their own. Two HR leaders explain why that is the wrong approach – and what employers owe their workforce instead
The scale of the mismatch is measurable.
Deloitte’s State of AI in the Enterprise 2026 report identifies insufficient worker skills as the top obstacle to integrating AI into existing workflows, ranking it above technology limitations, budget constraints, and leadership skepticism. A separate Harris Poll survey in late 2025 found that 42% of employees say their employer expects them to learn AI on their own. SHRM’s 2026 State of AI in HR report finds that 92% of chief human resources officers anticipate AI will be further integrated into the workforce this year.
The question increasingly confronting HR leaders is not whether to close that gap, but who owns closing it – and whether that ownership is an obligation or merely a strategy.
The stewardship argument
Christine Vigna, chief people officer at Dejero, a technology and broadcast solutions company, does not describe AI literacy as a recruitment benefit or a retention lever. She calls it a responsibility.
“There’s a real responsibility in my mind for all employers right now to help upskill their employees to get them where they need to be from an AI perspective,” she said.
The framing extends beyond standard workforce planning. The obligation exists not just for what the employee can do for the company during their tenure, but for what they can do in the labor market after.
“It is very rare for somebody to start and finish their career at the same employer,” Vigna said. “I hope that when they decide it’s time to seek out a new opportunity, their AI skills and overall literacy make them an ideal candidate in the market.”
That framing shaped how Dejero structured its AI implementation. Rather than treating AI adoption as an IT initiative, Vigna’s team approached it as a workforce transformation, investing in AI literacy across all divisions, including the company’s manufacturing arm.
The rollout, launched in November 2024 under a 24-month strategic plan, began with governance and communication: clear guidelines on what tools exist, where data can safely be shared, and what productive failure looks like. Vigna describes the cultural underpinning as essential to the approach.
“Organizations that are seeing the most success with AI right now are usually the ones that have normalized learning early instead of waiting for perfect certainty,” she said. “You’ve got to be comfortable with that failure piece.”
The counterargument – and its limits
The business case objection is straightforward: if employers invest in building AI skills and those workers leave, the investment walks out with them. The concern is not hypothetical.
PwC’s 2025 Global AI Jobs Barometer found that workers with AI skills command wage premiums of up to 56% over their peers, making AI-literate employees among the most competitive candidates in the market. The employer that trains is also, by this logic, the employer that subsidizes the next hire for someone else.
Vigna acknowledges the tension directly, then reframes it. The real risk, she argues, is not the worker who leaves carrying AI skills. It is the worker who stays without them – and the tool investment that produces nothing as a result. She identifies a pattern she sees repeatedly: companies that adopted AI aggressively because competitors were doing it, without defining the business problem the technology was meant to solve.
“Many of those AI adoptions are failing,” she said. “That’s because many organizations hadn’t actually defined the business problem they were trying to solve with AI.”
Employee literacy is the precondition for tool investment to produce returns at all. The reputational gain of genuine development is also real: the same Harris Poll survey found that 55% of employees say access to AI training or certification would make them more likely to stay.
Nokia’s view: fundamentals before adoption
Linda Krebs, global talent acquisition leader at Nokia, offers a perspective shaped by operating at a different scale. Nokia employs between 70,000 and 80,000 people across more than 130 countries and legal jurisdictions, meaning that AI adoption in HR moves deliberately, navigating GDPR, regional employment standards, and the complexity of a global workforce with very different starting points.
Krebs, speaking to her personal perspective on the company’s approach, describes an internal AI platform available to all employees, structured learning sessions delivered through Nokia’s learning and development portal, and a culture that encourages experimentation within governance guardrails.
“It’s encouraged for all of our employees to adopt and utilize this to make their jobs a little bit easier,” she said.
The skill Krebs places at the center of her thinking, however, is not technical fluency.
“The biggest thing that we look for is adaptability to change,” she said, “because change is the new norm” in an environment where AI tools are evolving faster than any training program can track.
She points to reverse mentoring as one mechanism available to organizations at scale: younger employees who bring greater digital familiarity helping to surface process improvement opportunities that longer-tenured staff may not see. The approach reflects a view Krebs shares with Vigna: that AI literacy is not a single training event but an ongoing capability, built into how the organization learns.
What the obligation looks like in practice
The gap between the principle and the practice remains significant. Only 35% of leaders report having a mature, organization-wide AI upskilling program; most programs are fragmented, optional, and disconnected from actual job tasks. The Harris Poll data shows what that gap produces: 34% of employees feel unprepared for AI-driven changes and 42% are simply waiting to figure it out on their own. The contrast with structured investment is stark: when employers provide AI training, adoption rises to 76%, against 25% among those without employer support.
That gap is the operational argument for Vigna’s position, and it is the one that most directly addresses the walk-out-the-door counterargument.
“Bringing employees in on that loop, training them how to use those tools, engaging employees with that, is going to lead you to a far better outcome than just sort of shoving in AI tools without actually knowing why you’re putting it in or what you’re trying to solve with it,” she said.
Whether framed as obligation or strategy, the math is the same. The companies most likely to generate returns from AI are the ones most willing to invest in the people who have to deliver those returns. The question of whether those people stay is secondary to the question of whether they can actually do the work.
