‘Organizations need to significantly increase their focus on flexibility’

Our working lives are vastly different from those of our parents and grandparents. From a historical perspective, do you see a linear development or rather an acceleration?
‘I am a bit ambivalent about that. I think we sometimes overestimate the magnitude of current technological changes. In the second half of the nineteenth century, for example, increasing mobility due to rail connections had an enormous impact on where and how people could work and live. We tend to focus primarily on what has changed in the last ten years and forget that major technological leaps in the past were just as disruptive. That is one side of the coin.
At the same time, I observe that, due to far-reaching digitization, all kinds of technologies are building on each other, creating a cumulative effect. Thanks to platforms, you can roll out new services worldwide with relatively little effort. I expect this will accelerate over the next twenty to thirty years. The type of work people do will change more than we have ever seen before.’

This acceleration may be slowed down by the division of work into specializations, but at the same time, complex technology makes it illusory to think that you will find ‘all-rounders’ who can master a process from A to Z. Many organizations are therefore now embracing the so-called Spotify model, with small, multidisciplinary teams that work quickly and autonomously. What is your view on this?
‘I am quite critical of the belief in that model. I have never encountered anything more powerful than the matrix organization, where you try to manage from different perspectives. In that model, you can certainly implement a number of ‘Spotify elements’, such as a more bottom-up approach or combining functional roles with project-based roles.
But I strongly believe that the tension between different perspectives, even if it is sometimes complex and frustrating, brings out the best in an organization.’

How can organizations prepare themselves properly for this acceleration?
‘Traditionally, organizations have been very focused on improving efficiency and service, i.e. costs and quality. They will have to focus much more on flexibility. It depends on the organizations themselves where that flexibility needs to be found. It could be products or services, because this year's big box office hits may not be the next year's. It could be technologies, suppliers, or the type of employees you have or need to recruit. The key is to be open to the fact that your organization will change anyway. You have to be willing to say goodbye to fixed structures and experiment with new, flexible structures that allow you to respond quickly to the changes.
I would advocate for executives, in addition to a broad C-suite, to surround themselves with experts from a wide range of perspectives. Not every perspective will prove equally important in the long term, but that does not matter. If you gain better insight into the possibilities and risks of all these perspectives, you will hopefully be able to switch gears more easily when something does become relevant.’

AI plays a significant role in the acceleration we are already seeing or expecting. Should managers of large organizations fully commit to this or, on the contrary, not believe in it too strongly?
‘Actually, AI is far too broad an umbrella concept to answer that question meaningfully. It is a bit like debating whether or not to invest in 'transport' in a large distribution company. The technologies that fall under the AI ​​label differ enormously. Generative language models, for example, are completely different from rule-based AI, which allows us to perform highly complex analyses based on strict logic.
To make sound choices, managers will need to delve a level deeper so that they can better understand the different types of AI and their strengths and weaknesses. It is a waste of money to invest heavily in AI pilots that have no connection to or clear future for a company. In fact, it is more likely to be counterproductive. We saw this with AI chatbots. They were popular for a while. Pilots were run everywhere. Then it became clear that these bots could ‘hallucinate’ and give all kinds of wrong advice. Or that customers became frustrated because they could not get past the chatbot with their problem. Often, the technology was scaled back or the conclusion was reached that conversational AI did not work for us. Whereas the correct conclusion would be that this specific type of AI was unsuitable for this specific purpose. That is a shame.’

In America, an AI startup is advertising with large billboards that read: ‘Stop hiring humans.’ Are people indeed being replaced by AI agents, and will we soon only need managers who oversee like a 'conductor'?
‘Absolutely not. Such a vision reflects a limited, short-term view. Tasks such as analyzing case law or coding— which we traditionally assign to our junior staff — can indeed be performed perfectly well by AI as things stand today. I expect the next generation of AI to also become strong in devising solutions, designs, and advice drafting, but to evaluate and implement these, you need experienced people who can check the quality of the AI. We will lose those people in the long term if we cut off the learning path for young professionals to reach seniority. Junior programmers, for example, are now finding it very difficult to find work because the development of simple code is easy to automate.
But the experienced programmers we are crying out for, who are able to identify errors and problems in code because they have done a lot of programming themselves, will eventually disappear. If you automate the work of juniors, you will eventually lose the knowledge needed for tasks that are more advanced than repetitive, analytical work.’

Every disruptive technology leads to change. Yet, historically, we always seem to adapt. What might that look like in this case?
‘The tide will turn. Organizations are now trying to attract senior staff with high salaries, but that strategy is finite. As the talent pool dries up, they will eventually have to invest in training people again. The question then becomes whether the costs of deploying AI outweigh the investment in training people or the ever-increasing salaries you have to offer seniors. Organizations that are unaware of this long-term effect really need to think about it, because this is a predictable development.’

A topical issue, especially given geopolitical developments, is digital sovereignty. What does this mean for organizations in concrete terms? Should they make different choices than they do now?
‘You should never surrender to one large supplier or – at the other extreme – develop lots of small automation islands yourself. It is much better to continuously consider which solution best suits which need and thus arrive at a good mix. Organizations are certainly capable of distinguishing between data that they absolutely do not want to share and data that they have no problem sharing in bulk with any partner. Ideally, you develop highly hybrid landscapes, leveraging the strengths of the various technologies. This brings more complexity into an organization and requires more expertise and manpower to manage that complexity effectively. That is the price we pay for sovereignty, but I firmly believe it will pay off in the long run.’

Should the Netherlands – or Europe – take more control in this regard, for example, by banning certain technologies?
‘I would not want to ban or restrict technologies at the European or national level. I wonder more why we are unable to come up with attractive alternatives. It is not because the technologies themselves are so complex. You will have to take steps towards bundling demand, because alternatives that align with our European value system only really have a chance with more cooperation across national borders. But even then, getting those alternatives adopted is difficult. With Big Tech, you are in a kind of ecosystem. Then it is very easy to use something that fits well with that ecosystem for new functions. People often choose the easiest and cheapest option.’

Back to the organizational level. How could you use AI to support the work of individual employees?
‘That is still a relatively unexplored area. The technologies we currently use to monitor how people perform and how they experience their work were developed from a managerial perspective. Employees are monitored in all kinds of ways to gain insights. This has two disadvantages: first, no one likes to be observed. Second, the results of that monitoring are often fed back at the team or department level, so that it is not really that useful to the individual employee.
Within our Future of Work platform, we are now working on tools that allow employees to analyze their own work behavior without having to share that data with management or colleagues. For example, you could indicate in the system that you want to work on a report for an hour and a half every day during a given week. If your AI tool then sees that you are constantly interrupting your work during those ninety minutes to check your email or respond to Teams messages, it can suggest temporarily disabling those external signals. This is individual feedback without anyone else watching and is based on the goals you have entered yourself. Incidentally, the same applies here. You may be able to choose to share some data so that you can also achieve goals at the team level, while you want to and can keep other data to yourself. Being able to manage the data yourself is the key.’

Returning to the acceleration of working life. If you could slow down one development and accelerate another, which would you choose and why?
‘I would like to slow down the expansionism and influence of Big Tech, because we still understand so little about what we are giving away and legislation is lagging behind. I would like to accelerate the awareness that good decisions can only be made by weighing up different interests. Due to uncertainty or ignorance among policymakers, who themselves are often not tech-oriented, a single dominant perspective is often chosen. Take privacy legislation, for example. Of course, privacy is important but so is saving lives. And so, for example, in healthcare or youth care, you sometimes want to be able to share data. In short, I want to move away from the absolutism of viewing issues related to data use, IT deployment, and AI from a single perspective. It is better to have experts with different perspectives and expertise work together to find wise compromises. Otherwise, we are missing out on a tremendous number of opportunities.’

This interview was published in Management Scope 02 2026.