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Across the UK, "will AI take my job?" has become one of the nation's most searched fears, a concern echoed in a recent open letter signed by more than 200 economists and technology leaders, including former Google CEO Eric Schmidt.

The letter warned that without the right guardrails, increasingly powerful AI could drive large-scale job displacement.

This anxiety around job stability has, unsurprisingly, increased in line with some of the world's largest employers - including Microsoft and Meta - announcing cuts to thousands of roles over the last year.

Left to interpret what that means for their own futures, employees globally have become accustomed to fear and uncertainty.

The impact is far-reaching, with recent research indicating that widespread anxiety is now impacting career decisions. In fact, as many as one in three workers say they are planning to retire earlier than planned, while 25% of workers are considering moving into industries less exposed to AI.

Although concerning, this potential “white-collar exodus” isn't a rejection of AI itself; most workers say the technology makes them productive when introduced with proper training and clear goals. However, it’s a response to how AI is being rolled out: often with little clarity, minimal support and not enough acknowledgement of the potential impact on employees’ confidence in their own expertise.

Left unaddressed, the impact will be felt in retention, engagement and institutional knowledge. Designing and prioritizing ways of working that incorporate transparency and training, while prioritizing job satisfaction and performance, has become a board-level priority.

Many workers feel that AI could reduce the need for their role within a few years, and that fear is not misplaced. Finance, professional services, and IT jobs have already felt this pressure, with PwC cutting 200 entry-level roles this year.

But we must not fall into the trap of reading this as solely a graduate or entry-level problem. In reality, according to a Gartner survey of C-level executives, 56% are extremely likely to quit due to the impact of AI and burnout. This is in some ways more harmful, presenting a clear risk of knowledge attrition across organizations as expertise born of human experience is lost.

Job security writes only the first chapter. Knowledge workers are increasingly frustrated that AI allows tasks once requiring years of expertise to be completed by almost anyone. The concern is about whether experience, judgement and specialist knowledge still carry value. And when organizations fail to redefine what human expertise contributes, disengagement festers.

Compounding this is the growing issue of AI fatigue. More than half of all content online is now AI-generated. So workers clocking off from AI at work are simply wading into more of the same at home, extending far beyond the 9-5 that businesses can measure or manage. Harvard Business Review has linked AI fatigue to decision fatigue, errors, and higher intent to quit.

In response, employees are actively cutting back their use of AI tools. However, investment shows little sign of slowing. Enterprise AI spending reached £1.2 million last year, up 108% on the year before, highlighting the growing gap between organizational investment and employee adoption and engagement.

This combined effect results in a workforce that's disengaging, under-using costly tools and potentially planning their exit. This demands urgent action from businesses, who need to address how AI is implemented and communicated, and act before disengagement turns into departure.

Positively, some organizations are already demonstrating what successful AI adoption looks like, grounded in an understanding that communication and culture should be taken as seriously as the technology itself.

IBM, for example, is both expanding and redefining entry-level hiring for an AI-first workplace, detailing where the organization is headed. Since workers often don't understand why they're expected to use AI in their role, moves like IBM’s can help to close a critical communication gap.

Transparency, clear guardrails and genuine dialogue are what turn AI spend into engagement. When people understand where they fit into the future of work, they're far more likely to embrace the technology shaping it.

The instinct in most organizations is to let AI creep into everything at once. In practice, AI delivers the most value - and does the least damage to worker purpose and confidence - when it targets administrative burden and repetitive tasks.

AI tools shouldn’t undermine the judgement, experience and specialist knowledge that people bring to their roles. Careless implementation of AI is the fastest route to the fear of expertise erosion that drives people toward the exit.

Adaptavist’s recent research into the human cost of AI reveals that 74% of workers are already taking the initiative to build AI skills, showing that willingness to adapt isn't the problem. But organizations must match that commitment. Rather than treating AI training as a one-off exercise during rollout, businesses should implement continuous learning and support that evolves alongside the technology.

The value of this approach is already being recognized widely. The UK Government has introduced its AI skills program with the ambition of training 10 million workers, backed by partners including the NHS. The recent “skills compact” plan also puts the onus on employers to address skills gaps in AI across financial services.

Both initiatives reflect a growing understanding that successful AI adoption depends as much on investing in people as it does on technology and tools. Effective training should help employees understand how their expertise continues to add value, giving them confidence that AI is there to enhance their role, not to replace it.

An important reality is that none of the above works if people are afraid to use the tools they're being trained on. Infosys has acknowledged the importance of psychological safety by framing AI adoption as an opportunity to experiment and improve without fear that mistakes will damage confidence or career prospects.

A study published in Occupational Medicine found employees who had 1:1s five times or more within two months reported significantly higher psychological safety than those with fewer meetings. Direct conversations to understand concerns, discuss how AI fits into their career development, and address uncertainty also show a marked improvement.

None of these approaches requires organizations to pare back their AI ambitions. Ultimately, the businesses that retain their best employees will be those that treat AI anxiety as a change management problem, measured by whether their most experienced people still feel essential to the work.

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