The fears and challenges facing older workers

“I find it difficult to use these technologies because my body movements are slow, and I don’t know how to start.” (Zhao et al., 2025)

“I feel like I am stupid after learning something… I get worried I will make a mistake.” (Zhao et al., 2025)

“I do not dare to keep asking my kids. I am afraid they will find me annoying.” (Ji & Liu, 2026)

These statements come from studies involving older adults learning and using digital tools and AI, rather than research specifically on employees. They offer a useful starting point for organisations: learning unfamiliar technology can involve uncertainty about operating it and discomfort about needing help.

The first two quotations express hesitation and a fear of making operational errors. The third reveals how difficult asking for guidance can become. In interviews with 36 older AI users, Ji and Liu (2026) documented repeated setbacks, anxiety, and reluctance to seek assistance. Participants’ accounts suggest that difficulties with technology can become entangled with concerns about burdening others.

Workplace research identifies related pressures. Yoon et al. (2026) studied 336 healthcare workers aged 50 and above in South Korea. Workers experiencing particularly strong technological complexity and pressure to keep up with younger users reported high anxiety and low confidence in their ability to use transfer-assistive robots.

The study measured anticipated acceptance of robots after participants received information about them, rather than actual generative AI use. Its findings nevertheless highlight an important consideration: difficulties learning technology and pressure to remain included can occur together.

Organisations should therefore consider several questions when introducing AI. Can employees use the tool? Can they ask for help comfortably? Do expectations allow enough time to learn? These are practical questions arising from the research, rather than a diagnostic checklist validated by these studies.

Older workers also bring different histories of education, technology use, and opportunities to practise. Zhao et al. (2026) studied 75 older adults, including interviews with 13 participants. Their discussion described less technically proficient participants as particularly valuing guidance, companionship, and security when learning.

For employers, this supports assessing individual needs rather than assigning training solely by age. Some employees may need introductory guidance; others may be ready to experiment independently. The practical implication is to provide different routes into learning, while avoiding assumptions about an employee’s abilities.

The evidence also requires careful interpretation. Studies of older adults, workplace technology, care robots, and generative AI examine overlapping but different experiences. They can inform organisational decisions, but they do not establish that every older worker faces the same barriers or will benefit from the same intervention.

How organisations can support effective AI use

Begin with employees’ needs and real work. Zhou and Fan’s (2026) systematic review of 37 studies identified comprehensive training, involving older workers in user-friendly technology design, and organisational support as priorities for supporting work participation.

Employers could apply these recommendations by asking employees which tasks are difficult, selecting useful AI applications together, and inviting them to test tools before wider introduction. For example, a team might explore whether AI helps prepare a first draft of a routine document, then assess its usefulness and limitations together.

Offer guided practice with room for repetition. Zhao et al.’s (2026) findings highlight differences in technical experience and the value some participants placed on guidance. A practical response is to offer short demonstrations, clear instructions, and opportunities to repeat familiar tasks.

Make assistance predictable and respectful. Ji and Liu’s (2026) interview accounts show why help-seeking deserves attention. Someone may need assistance while worrying that repeated questions will annoy the person helping them.

Named support contacts, regular drop-in sessions, and patient peer learning are possible organisational responses. Managers can make these arrangements easier to use by welcoming questions and checking whether employees feel comfortable asking them. Employers should evaluate whether these approaches actually help their workforce.

Build confidence through manageable accomplishments. Low et al. (2025) surveyed 400 older employees in China and Malaysia. Their analysis identified positive relationships between self-efficacy—the belief that one can successfully perform a task—AI literacy, and lifelong learning. These findings provide a rationale for considering confidence alongside technical skills. Organisations could begin with achievable activities and gradually increase complexity to build confidence.

Teach judgement alongside operation. Knowing how to request an answer is only one part of AI capability. Long and Magerko’s (2020) academic framework identifies critical evaluation and understanding AI’s capabilities, limitations, and ethical implications as elements of AI literacy. Their framework concerns AI literacy generally, rather than older workers specifically.

An organisation could put this into practice by teaching employees to compare outputs with trusted sources, identify missing context, and recognise when specialist review is needed. Training could include examples of questionable outputs and discussion of privacy, bias, and accountability. Clear escalation routes would help employees know whom to approach when an output seems unreliable or inappropriate.

The findings from Zhou and Fan’s (2026) review point towards a shared organisational responsibility: providing training, designing usable technology with workers involved, and maintaining support. For employers introducing AI, these priorities offer a foundation for helping experienced employees develop new capabilities while continuing to contribute their knowledge.

References

Ji, P., & Liu, X. (2026). Who adapts to whom: Technology or older adults? Mechanisms of technology anxiety among older AI users. Frontiers in Psychology, 17, Article 1725814. https://doi.org/10.3389/fpsyg.2026.1725814

Long, D., & Magerko, B. (2020). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1–16). Association for Computing Machinery. https://doi.org/10.1145/3313831.3376727

Low, M. P., Wut, T. M., Lau, T. C., & Tong, W. (2025). The interplay of self-efficacy, artificial intelligence literacy and lifelong learning for career resilience among older employees: A comparison study between China and Malaysia. Current Psychology, 44, 7879–7896. https://doi.org/10.1007/s12144-025-07434-6

Yoon, H., Lee, J., Kim, N., Ban, S., Shin, H., & Kim, Y. (2026). Technostress in older healthcare workers: Latent profiles and implications for care robot acceptance. Digital Health, 12, Article 20552076261451213. https://doi.org/10.1177/20552076261451213

Zhao, S., Wu, C., Wang, Z., Guo, M., & Zhao, X. (2026). Older adults’ conceptions of AI-supported learning: An epistemic network analysis based on AI-generated drawings. Smart Learning Environments, 13, Article 20. https://doi.org/10.1186/s40561-026-00443-3

Zhao, Y., Wang, L., Zhang, X., & Liu, M. (2025). Digital technology adoption and anxiety among older adults: A qualitative investigation. JMIR Aging, 8, Article e54321. https://doi.org/10.2196/54321

Zhou, W., & Fan, H. (2026). Towards sustainable age-inclusive workplaces: A systematic review on how technological tools support or hinder work participation for older workers (2014–2024). Work, 85(2), 316–344. https://doi.org/10.1177/10519815261435566