Artificial intelligence (AI) helps many people work faster and be more productive, but it is also exposing a pressing skills gap across organizations. Agentic AI raises the stakes as it moves from experimentation into everyday enterprise workflows. Workers and managers alike need to understand, direct and collaborate with autonomous agents, a shift that demands technical, managerial and organizational capabilities most enterprises have not yet built.
The agentic AI skills gap is the shortfall in workers' ability to understand, manage and collaborate with autonomous AI agents that plan, make decisions and trigger actions across enterprise systems with varying levels of autonomy. According to a report on "learning debt" from training platform TalentLMS, 59% of employees use AI tools to complete tasks they were never trained to do. Another 37% say AI has made them appear more competent than they actually are.
EY reached similar conclusions in an October 2025 survey. It found that 84% of employees are eager to embrace agentic AI in their roles, but 54% feel they are falling behind their peers in using AI agents. The anxiety is pronounced among managers: 53% of people managers worry they may not be good at supervising AI-augmented teams, and 82% think managing AI agents will make their jobs more challenging.
"We are at a critical juncture where leaders must provide structured and comprehensive training," said Kim Billeter, EY global people consulting leader, in EY’s press release about the survey.
"This isn't just a technology rollout; it's a human transformation that requires intentional support to redefine the partnership between people and AI."
Enterprises don’t just have to worry about employees knowing how to use AI tools. Now, they also have to make sure employees can understand, manage and work effectively alongside autonomous AI agents. That requires recognizing the agentic AI skills gap, then building the technical, managerial and organizational capabilities needed to bridge it.
Are AI Agents Just Better Chatbots?
"The biggest misconception is that AI agents are simply better chatbots. They are not," Sandip Patel, cloud and secure agentic AI architect and digital solutions engineer at Microsoft, told The Forecast.
He said the skills gap arises from this basic misunderstanding.
"Agents introduce a fundamentally different operating model because they can plan, make decisions, trigger actions and interact with multiple systems with varying levels of autonomy."
As these agents take on more tasks, a potential disconnect arises.
"This is a new way of working, with handoffs between autonomous agents who don't need to work like humans and handoffs back to humans who don't and can't necessarily work like agents," said Mike Dymott, principal consultant and general manager at ISM Inc., an independent enterprise technology advisory firm.
Organizations that fail to recognize this "end up creating even more significant and devastating forms of technical debt," said James Stanger, chief technology evangelist at CompTIA.
The risk mirrors traditional technical debt, which creates weak workarounds in place of long-term solutions, Stanger said. This new "learning debt" threatens to do the same in agentic operations.
What Are the Costs of Poor Agentic AI Literacy?
When employees do not understand how to manage agents properly, there are practical impacts. A pressing question for many enterprises is how to know whether an agent's work is any good, explained Dimitris Tsingos, co-founder and CEO of Epignosis, the company behind TalentLMS.
"If you are a manager and you cannot validate that the deliverable of your team is sound, you are in trouble," Tsingos said.
With agents delivering outputs, people start to fall behind, Dymott explained. They get the answers they need, but they aren't learning anything.
"It shows up as a gap in exactly the moment you need a human most, when the agent is confidently wrong, when the output looks right but isn't, when there's no obvious error message to flag it," Dymott said.
There is a real risk that organizations may be "unleashing agents into environments where they run amok, rather than thrive and help business move forward with confidence," Stanger said. "Ever seen what happens when you let a toddler roam through a house that hasn't been baby-proofed? The resulting chaos is a wonder to behold. It's the same with agents."
Every time an employee relies on agentic AI without developing the underlying knowledge needed to control it, the learning debt grows, Patel said.
"Over time, it can weaken innovation, increase security risks, impair decision-making and make organizations more vulnerable to large-scale errors," he said.
Although closing the agentic AI skills gap will take effort, it also will yield long-term wins.
"The organizations that gain the greatest advantage from agentic AI will not be those that automate the most tasks, but those that build the most AI-literate workforce," Patel said.
Which Skills Matter in an Agentic AI World?
Tackling agentic AI learning debt requires leaders to know which skills are fading and which capabilities will matter going forward.
With the rise of agents, people will not need skills that support routine repetitive work, Stanger said. "Routine QA activities, simple tier zero and one technical support activities, and routine data entry" are on the way out, he said.
Skills becoming obsolete "are the ones tied to task execution, manual data entry, rote reconciliation, first-draft generation of routine documents and code," Dymott said. "Those are being absorbed by agents quickly, and organizations shouldn't fight that."
At the same time, "the skills gaining value are AI literacy, critical thinking, systems thinking, agent orchestration, governance, risk management and outcome validation," Patel said.
For years, training has focused on the "how." Now the emphasis needs to shift to the "why," Tsingos said. Anyone using agents is effectively managing a team, and they will have to become managers, architects and supervisors.
"You will supervise [agents]. You will correct them when necessary. You will instruct them, like every manager does," Tsingos said.
Leaders at the executive level also will need to learn how agentic AI works to ensure they use it effectively and appropriately.
"Too many autonomy- and permission-based decisions are being made by leaders who don't yet understand [how agents operate]," Dymott said. "And that gap sits at the top of the organization, where the blast radius of a bad decision is largest."
Executives do not need to become AI engineers, explained Stanger. They do need to go deeper on the technical side than they typically do. Executives "increasingly need to become systems thinkers and have a strong technical foundation," he said.
How Can Organizations Build Agentic AI Literacy?
Organizations can take action now to close the agentic AI skills gap.
For starters, they can revamp their training, including guidance for managing agents. In place of task-based training, they can focus on skills like "structured review of agent output," along with mentorship that builds skills around judgment and evaluation, Dymott said.
To that end, "enterprises should invest in role-based AI literacy programs" that "measure understanding as well as adoption, and ensure employees continue developing foundational expertise," Patel said.
This investment in agentic AI literacy matters as much at the top of the organization as it does at the bottom, and every level in between, Patel said. "Executives approving agent autonomy need to understand what 'non-deterministic' actually means for risk," he said. "The organizations that get this right will treat human expertise as the control system for agentic AI, not as a legacy cost to be optimized away."
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Adam Stone is a journalist with more than 20 years of experience covering technology trends in the public and private sectors.
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