This is part 4 of a six-part series exploring AI, professional identity and the changing value of human expertise.
This article was originally written in English. If your browser is set to Dutch, it may be translated automatically. Please note that automated translations may contain grammatical errors or inaccuracies.
The enduring value of experience in the age of AI
What is professional experience really for?
For a long time, we have recognised experience through visible results. An experienced communication professional can write a strong message quickly. An experienced consultant can bring structure to a complex organisational challenge. An experienced manager knows how to turn information into a convincing presentation.
These capabilities remain valuable.
But as AI becomes increasingly capable of supporting similar tasks, we have an opportunity to look more closely at what experience contributes. Perhaps its greatest value was never simply the ability to produce excellent work but in the understanding that made excellent work possible.
Experience changes how we see
Years in a profession do not automatically create expertise. But meaningful experience changes how people interpret situations.
A novice may focus on the most visible problem. An experienced professional may recognise a familiar pattern, notice an inconsistency or identify a question that has not yet been asked. They understand that two situations that appear similar may require very different approaches.
This ability develops through practice, reflection, feedback and exposure to different circumstances. It is often difficult to capture in a document or formal procedure. Yet it can profoundly influence the quality of a decision.
Making expertise more visible
Consider two professionals using AI to prepare a report.
One asks for a draft and makes a few adjustments. The other explores several interpretations, compares the recommendations with previous organisational experiences and identifies an assumption that needs further investigation. Both may produce polished documents. But the second has used experience to shape the process and strengthen the result.
This creates an interesting opportunity for organisations.
When polished outputs become easier to produce, we can pay more attention to the thinking behind them. What questions did the professional ask? How did they evaluate the evidence? Which alternatives did they consider? What did they learn from previous experiences? How did they adapt the recommendation to the circumstances?
These questions help make professional expertise more visible. They also provide opportunities for learning and mentoring.
AI as a way to share expertise
AI can help make knowledge more accessible.
Research by Brynjolfsson et al. (2025) examined the introduction of generative AI assistance in customer service. The researchers found that less experienced and lower-skilled workers experienced particularly substantial improvements in productivity.
One explanation is that AI helped make effective practices more widely available. This suggests an important opportunity. Knowledge that once depended heavily on access to experienced colleagues may become easier to share. New professionals may receive more timely guidance. Teams may benefit from better access to organisational knowledge. Experienced employees may have more opportunities to mentor others in the reasoning behind good practice.
Of course, learning still requires opportunities to practise, reflect and make decisions. AI can support that process, particularly when organisations design learning around understanding rather than simply completing tasks.
Redesigning work around people’s strengths
IKEA offers an interesting example. After introducing its AI chatbot Billie to handle routine customer enquiries, IKEA reskilled thousands of call-centre employees for work involving remote interior design, digital sales and more complex customer interactions (Ingka Group, 2023).
The initiative illustrates how organisations can approach automation as an opportunity to reconsider where employees’ knowledge and experience create value. Instead of focusing exclusively on which tasks can be automated, organisations can ask: Where could our people’s experience make a greater difference? Which capabilities should we develop? What new responsibilities might become possible? How can technology support more meaningful work?
These questions connect technological innovation with professional development. They also recognise that employees bring capabilities that extend beyond their current job descriptions.
Recognising what experience makes possible
Perhaps experience has always been about more than becoming faster or more proficient at familiar tasks. It helps us recognise patterns, interpret ambiguity, understand consequences and make thoughtful trade-offs. It can give us the confidence to challenge a convincing recommendation or the wisdom to recognise when a different approach is needed.
AI creates new ways to apply these capabilities. An experienced professional may use it to explore more possibilities, test assumptions, share knowledge or devote more time to complex human challenges. That is an encouraging prospect.
Rather than measuring experience only through what someone can produce independently, we can begin to recognise what their experience makes possible. In hindsight, AI doesn’t diminish experience. It reveals what experience was actually for.
And that leaves us with a question worth exploring:
What could your experience help you see, understand or achieve differently with AI?#
Sources
- Brynjolfsson, E., Li, D., & Raymond, L. R. (2025). Generative AI at work. The Quarterly Journal of Economics, 140(2), 889–942.
- Ingka Group. (2023, June 29). AI and remote selling bring IKEA design expertise to the many.
