While AI is now embedded in nearly every workplace, most employees are not prepared to use it effectively, research from Skillsoft shows.

Although 86% of employees surveyed use AI tools at work, only 24% feel fully equipped with the skills required to use them effectively and drive results.

At the same time, 77% of leaders believe their organisations have set employees up for success, revealing a 53-point gap between how leadership perceives readiness and the reality for employees.

Beyond the perception gap, the data points to three structural shortfalls that help explain why readiness is lagging: limited skills visibility, training that follows adoption rather than leading it, and inconsistent governance.

Skills training visbility is deeply lacking, the research found. Only 11% of employees report receiving formal skills assessments, leaving most organisations without a reliable picture of what their workforce can actually do.

Just 16% of employees receive training before new AI tools are introduced, showing that training and deployment are out of sync.

Less than 1 in 10 employees say their organisation has comprehensive AI governance in place, while 21% say their organisation provides no AI guidance whatsoever.

Without these foundations, leaders are making workforce decisions based on assumptions rather than data.

“Organisations cannot afford to confuse AI adoption with AI readiness,” said Ciara Harrington, Chief People Officer, Skillsoft.

“When leaders and employees are operating from fundamentally different views of preparedness, performance becomes inconsistent at best and untrustworthy at worst.

“Closing that gap starts with treating skills as a business discipline and building the systems to align skill supply with evolving demand across the organisation.

“Most organisations have established processes for hiring, onboarding, and performance management, but our research shows many still struggle to bring that same rigour to understanding what skills they have, building the ones they need, and deploying them where demand is greatest.”

A lack of skills visibility is creating guesswork in workforce readiness. Without clear visibility into where skills exist and what’s needed, organisations cannot align learning to outcomes or connect skills to performance.

Only 11% of employees report the use of formal skills assessments or benchmarks, while 69% of employees are “somewhat” or “not very clear” on which skills matter. 43% of leaders say they are very clear on which skills matter.

Only 28% of employees strongly agree that their job description accurately reflects their day-to-day work.

Learning is available but out of sync with the pace of AI adoption, undermining confidence and performance. The biggest obstacle is not outdated content but time and prioritisation.

Only 16% of employees and 23% of leaders receive training before AI tools are rolled out. About six in ten (59%) employees cite a lack of time as the primary barrier to building new skills.

A fifth of empoyees are cautious about or do not trust AI tools. About three in ten (31%) employees say AI guidance varies by team or manager, rather than reflecting a company-wide standard.

Respondents say AI is influencing the nature of entery-level work – increasing expectations and enabling more time spent on higher-value tasks.

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Nearly a third (29%) of employees expect AI to reduce entry-level positions with 36% of employees and leaders anticiapte a shift toward problem solving and collaboration, with similar shares expeting faster advancement.

Nearly half (45%) of employees and 46% of leaders say training is primarily about building confidence in their current role, reflecting a workforce focused on keeping pace with AI.

As AI reshapes how work gets done, these findings make clear that the readiness gap isn’t a technology problem. It’s a workforce strategy problem. When organisations lack visibility into capability, fail to connect learning to business priorities, or operate without governance at scale, AI adoption outpaces their people.

“The organisations that pull ahead won’t be the ones that adopted AI first. They’ll be the ones that redesigned work and built a system to continuously develop the skills required to leverage AI in a way that drives purposeful business outcomes,” Harrington said.

“Traditional workforce systems help organisations manage employee records and workforce processes. What’s often missing is a continuous view of workforce capability, including what skills exist, what skills are needed, how quickly those gaps can be closed, and the impact they have on the business.

£Without that visibility, leaders are forced to make workforce decisions based on assumptions rather than evidence. That means moving from one-time training to continuous capability development and from ad hoc governance to a company-wide standard.”