Human judgement is the scarce resource of the AI era
If you do not understand the problem, you will not find the solution
Organizational consultants Lotte Hart and Nena Roes of SeederDeBoer describe in ‘Why AI projects fail’ how AI initiatives typically begin in practice. Not from a clearly defined problem, but from the fear of falling behind the competition.
“When an organization states that it wants to do ‘something with AI,’ it is rarely a choice driven by business strategy, but more often a reaction to the fear of falling behind.”
That fear may well be the most expensive business strategy of the moment. If you do not know which problem you want to solve, you cannot determine whether you are actually solving it. Hart and Roes therefore argue: start with the problem. Ask the “W” questions. Why this? Why now? Why AI? Only after those questions are answered will the rest be meaningful. In practice, genuine reflection appears to happen too rarely.
GenAI smooths out what human judgement is meant to keep sharp
In this interview, philosopher and ethicist Roos Slegers, an assistant professor at Tilburg University, poses a question that goes beyond implementation. How does this technology affect our ability to think for ourselves? She believes that generative AI (GenAI) is fundamentally unsuitable for producing truly new insights.
“GenAI does not offer anything surprising or niche. For that you need old-fashioned human virtues.”
By “old-fashioned human virtues,” she means courage, friction, and critical thinking. The willingness to resist must be an attitude you cultivate every day. A world smoothed over by AI is gradually losing more and more of those qualities.
Slowing down is not a weakness but a prerequisite for good governance
Board member Nienke Meijer makes a broader case for a human virtue: truly listening. In this interview, she discusses what she calls “collective wisdom”: the combined knowledge and perspectives of a diverse board, which can only come into their own when people truly listen to one another.
Slowing down feels like a weakness in the boardroom. But Meijer turns that reasoning on its head. Those who do not slow down miss the information they need. And those who make decisions based on incomplete knowledge, no matter how quickly they act, are too late.
The scarce resource organizations need to invest in
The question raised by these three conversations is the same: How do you maintain the quality of human judgement, especially when the pressure to speed things up is at its greatest? We have identified the following best practices:
Practice slowing down. In meetings, explicitly give space to those who have not yet spoken. Probe further into objections that are hesitantly voiced. Collective wisdom sometimes needs to be elicited.
Make critical thinking an organizational value. A culture in which people question AI’s output, where “but is this really correct?” is a valued contribution, protects the organization from its own blind spots. Without such a culture, you run the risk of slowly handing over your judgment to a system that does not know what you actually want.
Start with the problem. Clearly define the specific question you want to answer before spending even a single euro on technology. If you cannot explain it in two sentences, it is not clear enough yet.
The workplace knows more than the boardroom realizes. Involve the people who will eventually be working with AI in the decision to implement it—not as executors of a plan that has already been set in stone, but as a source of knowledge you cannot get anywhere else.
AI amplifies what already exists, weaknesses included. Organizations that handle this well do not start with the tool, but with the people.