Moving beyond assumptions about age and technological capability

The relationship between age and technology has generated some remarkably persistent stereotypes…younger workers are described as digital natives, and older workers are assumed to struggle with technological change. And now, as artificial intelligence becomes embedded within working life, those assumptions risk being reproduced in a new form. But perhaps we are asking the wrong question, instead of asking which generation is best equipped for AI, organisations might ask – Under what conditions does AI enable people to work effectively?

New academic research provides an interesting contribution to that discussion. Published in the August 2026 edition of the Journal of Vocational Behavior, research by Mingyan Han and Jingyou Zhao examines the relationship between employee-AI collaboration and what researchers describe as successful ageing at work.

Their findings challenge the assumption that more interaction with AI necessarily produces greater benefit, instead, they identify an inverted U-shaped relationship. Employee-AI collaboration can support successful ageing at work up to a point, but beyond that point the benefits may diminish.

The research also identifies job crafting towards strengths as an important mechanism in that relationship, while anxiety about the ethical implications of AI can weaken positive effects and exacerbate negative ones.  The message is not that AI is good or bad for older workers, it is that the relationship is more complicated, and that complexity matters.

Technology does not arrive in a vacuum

When new technology is introduced into an organisation, its impact depends partly upon how work is designed around it.

  • What tasks does the technology perform?

  • What remains the responsibility of the employee?

  • How much autonomy does the individual retain?

  • What training is provided?

  • How are existing strengths incorporated?

  • Does the technology augment judgement or displace it?

  • Do employees understand why the technology is being introduced?

  • And do they trust the systems with which they are expected to collaborate?

These are workforce-design questions, and they cannot be answered by knowing somebody’s age.

The danger of the generational AI narrative

There is already a temptation to frame AI adoption through generational comparison.

  • Who uses it most?

  • Which generation is most confident?

  • Who is most resistant?

Those questions may produce interesting statistics, but they can also reinforce the idea that technological capability is somehow inherent within an age group…it isn’t:

  • Access matters.

  • Training matters.

  • Role matters.

  • Previous experience matters.

  • Confidence matters.

  • Job design matters.

  • Opportunity to experiment matters.

  • Organisational culture matters.

  • And increasingly, trust matters.

Someone who has spent years working with a particular technology may be more confident with it than a younger colleague encountering it for the first time. Equally, somebody entering the workforce may bring habits and expectations shaped by technologies that did not exist when an older colleague began their career. Neither observation tells us what either individual is capable of learning next.

AI may make human capability more important, not less

There is another important dimension…AI can perform tasks, but organisations still require judgement, contextual understanding, relationships, creativity, ethical reasoning and experience.

The challenge is therefore not simply to teach people how to use AI, it is to determine how human and technological capabilities combine. That may create interesting possibilities within multigenerational teams. Employees with extensive professional experience may bring judgement, contextual knowledge and pattern recognition developed over many years, and people newer to a profession may bring different perspectives, recent knowledge and fewer assumptions about how work has traditionally been done.

AI adds another form of capability to that environment, the opportunity lies not in deciding which is superior, it lies in designing work so that these different forms of capability complement one another.

From technology adoption to workforce design

This changes the conversation, rather than – Can older workers adapt to AI? we might ask – How do we design AI-enabled work in which people of different ages, experiences and career stages can contribute effectively?

That is a much more useful organisational question, and it also shifts responsibility. If one group struggles with technological change, the immediate assumption should not be that something is deficient in that group. Organisations should examine the environment around adoption,  training, participation, communication, job design, accessibility, leadership and opportunity.

Technology changes quickly, human capability develops throughout life. The organisations most likely to benefit from AI may therefore be those that understand both.

Key Takeaway: AI adoption should not be treated as a contest between generations. Its organisational value will depend partly upon how effectively work, learning and technology are designed around the capabilities of people throughout the working life course.