As artificial intelligence transforms work, are organisations protecting the opportunities through which people develop knowledge, judgement and experience?

Artificial intelligence is changing not only how work is performed, but also how people acquire the knowledge and experience needed to perform it effectively. Much of the current debate focuses on productivity, automation and the potential displacement of jobs. These are important considerations, however, another issue is beginning to emerge…if AI increasingly performs the tasks through which employees traditionally develop their expertise, how will organisations prepare the next generation of experienced professionals?

This question has significant implications for workforce development, career progression and knowledge transfer. It also challenges some familiar assumptions about age and technological capability.

At the Age Diversity Forum (ADF), we believe this developing conversation reinforces an important principle: the relationship between AI and the workforce should not be understood primarily as a generational divide. It is fundamentally a question of how organisations design work, develop people and sustain human capability.

When experience becomes training data

In October 2026, the World Economic Forum (WEF) highlighted a growing development within the artificial intelligence industry, the use of experienced professionals to train, evaluate and improve AI systems.

Specialists in fields such as law, healthcare and finance are being engaged to assess AI-generated outputs, identify errors and contribute the professional judgement that makes these technologies more effective (Golin, 2026). This represents an interesting development in the relationship between human expertise and technology.

Knowledge accumulated through education, practical experience and professional judgement is becoming an increasingly valuable resource in the development of AI. Yet the implications extend beyond the immediate commercial value of that expertise. As AI systems become more capable of performing professional tasks, organisations may need to reconsider how they preserve, develop and transfer the human knowledge on which those systems depend.

There is a potential paradox…the more effectively AI uses accumulated human expertise, the more important it becomes to ensure that organisations continue developing that expertise in people.

The missing steps in professional development

Professional expertise rarely develops through formal education alone, it emerges through a combination of learning, practical experience, feedback, problem-solving and exposure to increasingly complex responsibilities. An early-career professional may begin by researching information, preparing initial analyses, drafting documents or undertaking routine technical work. Over time, these activities provide opportunities to understand context, recognise patterns, make judgements and develop confidence.

AI can now undertake many of these tasks, often with considerable speed, so creating opportunities to improve efficiency and free employees to concentrate on higher-value activities. However, if organisations simply remove routine tasks without considering their developmental value, they risk creating gaps in the pathways through which people acquire expertise.

A June 2026 World Economic Forum white paper, developed with PwC, examined the implications of AI for entry-level employment, identifying job design and talent development pathways as important areas requiring attention. The challenge is not necessarily to preserve every routine task, it is to ensure that the learning opportunities those tasks once provided are replaced with equally effective ways of developing capability.

Experience is not the same as age

This discussion also highlights an important distinction for age inclusion, as experience, organisational tenure and chronological age are not interchangeable, and an employee may possess extensive experience in one profession while being relatively new to another. A younger employee may have considerable expertise in an emerging technology, whilst an older colleague may be entering a new occupation or developing unfamiliar skills.

Equally, two employees of the same age may have very different career histories, capabilities and development needs. This matters because discussions about AI frequently rely on generational assumptions.

Younger employees may be described as naturally comfortable with new technologies, while older employees may be assumed to possess experience but require greater support to adapt. These generalisations can obscure the real factors influencing technological confidence and capability.

Access to training, occupational requirements, previous experience, organisational support and opportunities to practise may be more useful considerations than age alone. The objective should be to understand what individuals know, what they need to learn and how their capabilities can continue to develop.

AI adoption is not the same as organisational transformation

Recent research from McKinsey provides a further perspective. In October 2026, McKinsey reported that 80% of respondents to its 2026 AI survey said AI had improved individual productivity, while 37% reported a positive impact on earnings before interest and.

These findings suggest that improvements in individual productivity do not automatically translate into wider organisational performance. McKinsey argues that organisations need to reconsider how work is structured, how responsibilities are allocated and how employees develop the capabilities required within increasingly AI-enabled environments.

This distinction is important, as providing employees with access to AI tools is not the same as designing a workforce capable of using those technologies effectively and sustainably. Organisations need to consider how knowledge is shared, how decisions are made, how employees develop expertise and where human judgement remains essential.

These considerations should form part of workforce strategy rather than being treated as secondary consequences of technological implementation.

Knowledge transfer must become intentional

For multigenerational organisations, these changes create both challenges and opportunities. Employees bring different combinations of technical knowledge, professional experience, organisational understanding and emerging capabilities. This diversity can be valuable when organisations create opportunities for people to learn from one another.

For example, an employee experienced in a particular profession may contribute valuable judgement about the reliability or limitations of AI-generated outputs. A colleague with specialist technological expertise may help others understand how new systems can be applied effectively. Such exchanges need not follow traditional assumptions about older employees mentoring younger colleagues.

Knowledge and expertise can flow in multiple directions, depending on the capabilities involve, however, organisations cannot assume that meaningful knowledge transfer will occur automatically simply because people of different ages work together. It requires deliberate opportunities for collaboration, appropriate leadership and working environments in which employees feel confident sharing knowledge and learning from others.

AI adds urgency to this consideration. If technology changes the tasks through which people gain practical experience, employers will need to create new opportunities for learning, feedback and professional development.

Designing the future workforce

The central question for employers should therefore extend beyond which tasks AI can perform, they should also consider how technological change affects the development of their future workforce.

Several considerations become important.

  • Are employees receiving appropriate opportunities to develop both technological and professional capabilities?

  • Are early-career roles being redesigned to preserve meaningful learning opportunities?

  • Can experienced employees adapt their knowledge to changing responsibilities?

  • Do organisations create opportunities for reciprocal learning across career stages?

  • And are leaders examining the implications of AI for recruitment, progression, retention and organisational knowledge?

These are workforce design questions, and they also have clear implications for age inclusion. An organisation that introduces AI without considering access to learning may unintentionally reinforce existing inequalities.

One that assumes technological adaptability depends on age risks overlooking individual capabilities and development potential. Conversely, an organisation that deliberately combines technological innovation with inclusive learning and career development may be better positioned to benefit from the capabilities of its entire workforce.

The ADF perspective

At the Age Diversity Forum, we have previously argued that AI should not be understood simply as a generational divide, and the emerging evidence strengthens that position. Technological change affects people through their roles, responsibilities, skills, career histories and opportunities to develop. Age may be relevant to understanding workforce experiences, but it is rarely a sufficient explanation on its own.

The challenge for organisations is to design employment practices that recognise both the contribution of existing expertise and the need to develop future capability. That includes creating opportunities for intergenerational learning, supporting career transitions and ensuring access to development throughout working life.

It also means reconsidering traditional assumptions about who possesses knowledge, who requires training and how expertise is acquired. AI offers considerable opportunities to improve productivity and transform the way organisations operate, however, those opportunities will be more sustainable if technological progress is accompanied by continued investment in human capability.

As AI changes how work is performed, organisations must also reconsider how people develop, share and sustain the expertise on which future performance depends. The future of work will not be determined by technology alone, it will also depend on how effectively organisations develop the people working alongside it.

References

Golin, M. (2026) ‘Should experts training AI own a stake in what it earns, and other trends in jobs and skills this month’, World Economic Forum, 28 October. Available at: https://www.weforum.org/stories/jobs-and-the-future-of-work/should-experts-training-ai-own-a-stake-in-what-it-earns-other-stories-in-jobs

McKinsey & Company (2026) ‘AI is changing work. Now it has to change the organization’, 5 October. Available at: https://www.mckinsey.com/capabilities/people-and-organization/our-insights/ai-is-changing-work-now-it-has-to-change-the-organization

World Economic Forum (WEF) and PwC (2026) Artificial Intelligence and the Future of Entry-Level Work: A Framework for Safeguarding and Reinventing Early Career Pathways. 22 June. Available at: https://www.weforum.org/publications/artificial-intelligence-and-the-future-of-entry-level-work-a-framework-for-safeguarding-and-reinventing-early-career-pathways