HR leaders struggling to bridge the gap between their people and AI readiness may be missing out on workforce productivity gains as high as 40%.

EY’s 2025 Work Reimagined Survey, which surveyed 15,000 employees and 1,500 employers across 29 countries, found that although the majority of employees are using AI in their daily workflow (88%), usage is primarily limited to basic tasks such as searching and summarizing documents.

Just 5% are currently using AI in more advanced ways to transform their work.

Meanwhile, anxiety concerning the impact of AI is exacerbating the gap, with one third (37%) of employees worried that overreliance on AI could erode their skills and expertise.

A further 64% believe their workloads have increased due to the introduction of AI.

Roselyn Feinsod, Principal, People Consulting, at Ernst & Young LLP (EY), exclusively tells UNLEASH that the research identifies a crucial disconnect where unstable talent foundations, such as weak culture, ineffective learning and misaligned rewards, means the potential benefits of AI are diminished.

“If AI remains a priority for organizations in 2026, which it will, HR and talent leaders need to focus on closing the gap between AI and human readiness,” Feinsod says.

As AI continues to grow in the workplace, HR and talent must focus on the human dimension—creating cultures that prioritize employee and talent health, training, rewards while enabling effective technology integration.”

The five pillars of the talent advantage

EY’s research found that organizations that can effectively integrate talent and technology are able to achieve greater value, what it terms as the ‘talent advantage’.

However, it also found that just one in four (28%) of organizations are on track to achieve this.

Feinsod explains that organizations that have achieved transformational results with the talent advantage have “refined five key capabilities that work in synergy with one another.”

These are: talent health; scaling AI adoption excellence across the enterprise; redefining culture and workplace norms; aligning rewards with new behaviors and outcomes; and embedding continuous learning into everyday operations.

Talent health must be continually monitored by HR and talent leaders, particularly among employees working with AI, Feinsod comments.

“Organizations that have a talent advantage have high talent health scores – in talent advantage organizations, 89% of employees are likely to recommend the company to a peer as a great place to work, compared to only 20% for companies with a talent disadvantage,” she explains.

Alongside working to improve workplace culture and ensuring rewards are matched with evolving employee needs and AI roles, HR leaders must also work with tech leaders to adopt an AI excellence mindset.

Feinsod explains this means aligning advanced use of AI with role-specific applications and tools and organizational strategic goals.

Organizations that are able to maximize employee AI adoption in this way can unlock up to 14 hours per week of time savings as employees learn to leverage higher complexity use cases that are aligned to business goals,” she says.

“To do this, organizations need to provide the right AI tools to get their employees to use it, feel confident and empowered to use it and crucially understand how and why the tools can help their day-to-day work.”

But it’s not enough to have each facet working on its own – Feinsod adds that “focusing on these aligning areas together should be a goal for HR and talent leaders.”

More AI training may mean more attrition

The final pillar of the talent advantage, embedding continuous learning into everyday operations, is a significant driver of AI success – EY found that employees with more than 81 hours of training often see major improvements to their skills.

However, cultivating highly skilled employees also increases the risk of them leaving the organization, with this group of workers 55% more likely to leave.

“Advanced training makes their skills highly sought after, and outside opportunities often move faster than internal ones,” Feinsod says.

“Leaders face a dilemma: under-invest and struggle to build AI capability or invest heavily knowing that employee flight risk increases.”

In response, HR leaders should “pair deep learning with smart retention strategies” such as personalized rewards and clear career paths that showcase opportunities better than external competitors can offer.

Increasing talent engagement through initiatives like internal talent marketplaces, tenure-based certifications, and learning cohorts that help employees build strong peer networks can also aid retention of skilled AI workers.

Ultimately, Feinsod says, organizational success in the AI era “isn’t just about tech – it’s about people too” and that a sustainable advantage derives from “blending strong human foundations with advanced technology.”

To do that, HR and talent leaders need to address many of the AI anxieties in the workforce today head on – whether that be concerns over skill erosion, training, access to the right technology – to develop trust, provide clarity on the value AI can deliver and help create a safe culture that overcomes hesitation, and can open up new ways of working,” she concludes.