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Employees can be hopeful about AI and overwhelmed by it at the same time. For people leaders, the practical task is not to demand enthusiasm: it is to explain what is changing, provide useful training, protect room to practice, and let employees raise concerns about their work. Fear of job loss matters, but it is not the only reason people hesitate.
What FOBO means—and why the distinction matters
FOBO does not have one universal meaning in discussions about AI. Henley Business School uses it to mean “Feeling Optimistic But Overwhelmed.” Gallup uses the term for concern that technology could make a person’s job obsolete. The first describes mixed feelings; the second names a specific job-security fear. Keeping them distinct helps leaders avoid treating every employee concern as fear of replacement.
Henley Business School’s commissioned survey of 4,640 full-time workers in the UK, published on 7 May 2025, found that 56% were excited about AI’s potential while 61% felt overwhelmed by its rapid development. Those responses can coexist: interest in what a tool might do does not mean a worker feels ready to use it.
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What the surveys say about workplace AI anxiety
The findings below describe different populations and questions. They are useful context, not a single comparable measure of employee sentiment or a forecast of actual job losses.
#1 Best Overall
| Source and scope | Finding | What it measures |
|---|---|---|
| Henley Business School, UK full-time workers; survey published 2025 | 56% said they were excited about AI’s potential; 61% felt overwhelmed by its rapid development. 60% said proper training would make them more likely to use AI at work, and 24% felt their employer was not providing enough support. Henley Business School | Excitement, overwhelm, and reported training and support needs. |
| Pew Research Center, US workers; 2025 | 52% said they were worried about how AI may be used at work in the future; 36% said hopeful, 33% overwhelmed, and 29% excited. Worry appeared across age groups, though feelings varied by age. Pew Research Center | Feelings about future workplace AI use, not a direct measure of displacement risk. |
| BCG, 13,102 employees in 15 countries and regions; 2024 | 49% of regular generative AI users believed their job might disappear within ten years, compared with 24% of employees who did not use generative AI. Half of leaders had been trained in how AI would change their jobs, compared with 30% of managers and 28% of frontline employees. BCG | Job-disappearance beliefs and reported training coverage across roles. |
| Gallup, US workers and CHROs; findings reported in 2024 | 22% of workers said in 2023 that they worried their job would become obsolete because of technology, up from 15% in 2021. Separately, 2% of CHROs strongly agreed that their upskilling efforts were developing the skills employees need for the future. Gallup | Worker concern in two different years and CHRO assessments of upskilling. |
| American Psychological Association, Work in America survey; 2023 | Workers worried AI might make some or all of their duties obsolete more often said they felt they did not matter to their employer (41% versus 23%) or coworkers (37% versus 17%). They also more often worried new technology would take over duties within ten years (75% versus 23%). APA | Associations between reported AI-related worry and other reported concerns; not evidence that one caused the other. |
These results should not be combined into an overall percentage: the studies differ in geography, dates, respondent groups, and question wording. In particular, BCG’s finding that regular generative AI users were more likely than non-users to anticipate job disappearance suggests that trying a tool does not necessarily settle job-security concerns.
Why employees may hesitate to use AI
Concern about jobs, tasks, and expertise
Workers may wonder whether AI will remove tasks, change a role, or reduce the value attached to skills they have built over time. BCG and Gallup document concerns about jobs disappearing or becoming obsolete, but these survey responses do not establish that a particular role will be eliminated. Leaders should distinguish confirmed changes from possibilities still under consideration.
Fear of making a consequential mistake
An employee may hesitate to rely on an opaque system if an incorrect output could affect a customer, colleague, or the employee’s reputation. A 2025 article in Organizational Dynamics identifies this kind of fear as a recurring source of AI anxiety. It is a practice and conceptual article, not a measured causal study; its abstract says it used no data. Organizational Dynamics
Questions of voice, value, and oversight
AI adoption can raise concerns beyond job loss: whether employees still matter to the organization, retain influence over decisions, or are subject to technology-driven oversight. The APA survey found associations between worry about duties becoming obsolete and reported feelings of not mattering. Those correlations describe workers’ reported experiences; they do not show that AI worry caused those feelings.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What employers can do to make adoption safer and more practical
Explain what is known—and what is not
Describe the system’s intended use, which decisions remain with people, and what changes have been decided. Be equally clear about what remains undecided and when employees can expect an update. That candor is a management response to the mix of optimism, overwhelm, and replacement concern documented in the surveys; the surveys do not measure its impact.
Make training specific to the work
Training is more useful when it connects to real tasks rather than presenting AI as an abstract capability. Henley’s UK respondents reported greater willingness to use AI if proper training were available, while BCG found lower training coverage among managers and frontline employees than among leaders. Together, these findings make training a relevant support need, but they do not establish the effect of any particular course or program.
Rank #4
- Set aside protected time to practice rather than expecting workers to learn on top of their usual workload.
- Use examples drawn from the employee’s actual tasks and explain when an output needs human review.
- Provide a clear way to ask questions and report errors without being blamed for surfacing them.
Make practice psychologically safer
People are more able to explore a new tool when they can acknowledge uncertainty, ask for help, and flag a problem without fearing that one mistake will define their competence. The Organizational Dynamics article proposes psychological safety alongside support tailored to different employee perspectives. Treat this as a reasoned framework, not an intervention proven by experimental data.
Give employees a role in workflow decisions
Ask workers to identify where AI could help, where a person must review or make the decision, and where a proposed workflow could create risk or extra work. This is a practical recommendation, not an action tested by the cited surveys. It can make implementation questions visible to the people closest to the work.
Track adoption separately from employee experience
Usage alone cannot show whether employees accept a tool or whether it is helping sustainably. A balanced check-in can track access and usage alongside training coverage, confidence, error escalation, workload, and employee feedback. Treat each measure as a separate signal rather than assuming a short-term productivity gain proves durable benefit.
Quick Recap
How to read the evidence without overclaiming
- Keep populations and geographies attached to figures. Henley surveyed UK full-time workers; Pew’s findings concern US workers; BCG surveyed employees across 15 countries and regions.
- Keep the question in view. Excitement, overwhelm, worry about workplace use, and belief that a job may disappear are not interchangeable measures.
- Separate reported concern from actual outcomes. A worker’s fear of obsolescence is evidence about their experience, not proof that their job will be lost.
- Read associations as associations. The APA results do not establish cause and effect, and the conceptual article in Organizational Dynamics does not report measured intervention outcomes.
- Do not treat a recommendation as a guarantee. The cited surveys establish relevant concerns and reported support needs; they do not identify one intervention that guarantees sustained adoption.
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