Are we hiring for yesterday's problems?

Why relevant experience may not be enough to recognise the competence we need next.

In consulting, we often assess competence by looking for someone who has already done almost exactly what we need:

The right industry, the right title, a certain number of years in a similar role. References from nearly identical assignments.

It's understandable, because experience provides reassurance - particularly when organisations are making significant investments or managing complex risks.

But it also raises a question:

Are we selecting people for the problems we've already encountered, or for the problems we're actually going to face?

Experience is not the same as expertise.

Research suggests that the relationship between previous experience and future performance is less straightforward than we often assume.

A meta-analysis by Van Iddekinge and colleagues (2019) found only a weak relationship between experience before hiring and subsequent job performance. The study concerned employee recruitment rather than consultant procurement, but it challenges the assumption that years of experience are, by themselves, reliable evidence of capability.

That, however, doesn't make experience irrelevant.

Research on skilled judgement shows that expertise can be highly valuable when people have learned to recognise meaningful patterns through reliable feedback (Kahneman & Klein, 2009).

The distinction is between having encountered a situation before and understanding why a particular approach worked.

The ability to rethink expertise matters.

This is not a new idea.

Hatano and Inagaki (1986) distinguished between routine expertise, which enables proficient performance in familiar situations, and adaptive expertise, which involves applying and developing knowledge when circumstances change.

Argyris and Schön (1978) explored a related distinction in organisational learning: the difference between improving actions within existing assumptions and questioning the assumptions themselves.

Both perspectives point towards a capability that conventional CV matching may struggle to recognise.

Someone may have delivered ten successful transformations. But what happens when the eleventh requires a different approach?

Can they recognise when the conditions have changed? Can they question the assumptions behind their previous success And can they adapt without abandoning the expertise that still matters?

AI changes what competence looks like.

As AI becomes part of knowledge work, this distinction becomes increasingly important.

Research involving management consultants found that AI assistance improved performance on certain tasks but reduced correctness on others, depending on whether the task fell within the technology's capabilities (Dell'Acqua et al., 2026).

The implication is not that expertise matters less. It is that producing an answer and knowing whether to trust that answer are different capabilities.

AI may change which skills are most valuable, but it also increases the importance of judgement about when existing knowledge, new evidence or technological assistance should guide a decision.

The future is not a choice between specialists and generalists.

A non-linear career does not automatically make someone adaptable - nor does deep specialisation make someone inflexible.

Research on adaptive expertise suggests that strong domain knowledge and the ability to respond to unfamiliar conditions can develop together (Bohle Carbonell et al., 2014).

The question is whether we know how to recognise that combination.

A CV tells us where someone has worked, what roles they have held and which assignments they have completed. It tells us much less about how they responded when their assumptions proved wrong.

We need to rethink how we recognise competence.

At In The Arena, we believe organisations place too much confidence in what is easy to document and too little attention on what people are actually capable of doing.

A CV can demonstrate experience. It cannot, on its own, demonstrate judgement, the ability to learn or the willingness to question an approach that has worked before.

We believe this needs to change.

Not by lowering the value of expertise, but by raising our expectations of what expertise should include.

We want to see organisations evaluate not only what people know, but how they use that knowledge when circumstances change. How they respond to unfamiliar problems, integrate different perspectives and recognise when their own assumptions need to be challenged.

And we believe organisations have a responsibility beyond selecting the right individuals. They must create environments where questioning established thinking is possible, where learning is part of the work and where changing your mind in response to better evidence is recognised as good judgement rather than weakness.

Why this matters.

The way we define competence shapes who gets opportunities, whose perspectives are heard and ultimately how organisations approach their most difficult challenges.

If we continue to reward familiarity above all else, we risk overlooking people who could help us see problems differently.

This is not an argument for abandoning experience. It is an argument for examining what experience has actually taught someone, and whether they can use it when the familiar answers no longer work.

The question is not simply what someone has done before. It's whether they have the judgement to know what to do when the past no longer provides the answer.

References & Further Reading

This article draws on research in personnel selection, adaptive expertise, organisational learning and human–AI collaboration. The references below include research directly cited in the article and selected foundational works for further reading.

Argyris, C., & Schön, D. A. (1978). Organizational Learning: A Theory of Action Perspective. Addison-Wesley.

Bohle Carbonell, K., Stalmeijer, R. E., Könings, K. D., Segers, M., & van Merriënboer, J. J. G. (2014). How experts deal with novel situations: A review of adaptive expertise. Educational Research Review, 12, 14–29.

Dell'Acqua, F., McFowland, E., III, Mollick, E., Lifshitz, H., Kellogg, K. C., Rajendran, S., Krayer, L., Candelon, F., & Lakhani, K. R. (2026). Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of Artificial Intelligence on Knowledge Worker Productivity and Quality. Organization Science, 37(2), 403–423.

Hatano, G., & Inagaki, K. (1986). Two courses of expertise. In H. Stevenson, H. Azuma, & K. Hakuta (Eds.), Child Development and Education in Japan (pp. 262–272). W. H. Freeman.

Kahneman, D., & Klein, G. (2009). Conditions for intuitive expertise: A failure to disagree. American Psychologist, 64(6), 515–526.

Van Iddekinge, C. H., Arnold, J. D., Frieder, R. E., & Roth, P. L. (2019). A meta-analysis of the criterion-related validity of prehire work experience. Personnel Psychology, 72(4), 571–598.