The DNA of skills: the value of human skill (and training) in the age of intelligent machines

If we look at how organisations are structured and the budget they allocate to Learning & Development initiatives, the answer is "very few". And for two reasons. The first: these funds are generally limited and subject to cuts before many other budget items. The second, and more importantly: these budgets are earmarked for training and other formal learning mechanisms. However, as I show in my book, we know from research that most of the really useful skills are acquired by doing one's job under certain conditions, and in particular, by working closely with an expert. If managers took this evidence seriously, they would allocate a much larger part - and perhaps the majority - of the training budget to the design and enrichment of the work itself, so that employees can learn more simply by doing their jobs.

Of the three key components that drive the development of the most valuable skills (challenge, complexity and connection) which one can be considered essential or prioritised over the others?

None is a priority over the others, they all have equal weight in a healthy and organic development of competences. Of course, it is also possible for learning to take place if one or two of these components are not at an ideal level, but in that case competence development will also be less effective.

In the book, he describes how smart technologies can be an integral part of this process: can he explain this with a concrete example?

I bring to the attention of readers an example related to the defusing of ordnance. In the old approaches, the operator who had to defuse an improvised explosive device had to move a few hundred metres away from the apprentice, making it difficult for the latter to gather knowledge from the expert, given the distance. With the introduction of robots, however, the apprentice could control the robot with his mentor beside him. In this way, both the apprentice and the expert were together in an armoured vehicle 300 metres away from the device, and it became much, much easier for the apprentice to learn. This example demonstrates how technology can play an active role in the development of new strategies to achieve greater productivity and skills growth at the same time. Developing approaches that aim at both goals is a fundamental step.

To close, let's try to imagine an organisation, from any sector, five or ten years from now: what 'weight' will the skills of a talented young person have, and how will they compare to those of a more experienced employee?

It is a fascinating question! I think that some skills will remain the same, and if we talk about soft skills for example, I imagine that these will still be important and present. The same goes for discipline, organisation, planning and many other skills that are crucial for the success of any worker. At the same time, we will interact much more often with highly capable and autonomous artificial intelligence agents, who in turn will create and manage other agents to complete certain tasks. As I explained in an article published in the Harvard Business Review in July 2024, I believe that 'agent supervision' will be a new skill category, with some similarities to managing people, but also with surprising differences. Similarly, new methods will be needed to check the quality of AI-generated content: it will not be enough for an expert to slowly examine the work of algorithms line by line, but new interfaces, new models and new approaches will be needed to cope with the dramatic increase in output. These will be new skills required in many professions. However, we don't really know the answer to this question, which is why we must continue to learn, to adapt and build the new skills needed to live in this era of transformation.