What Are We Still Paying For?

In most of the companies I work with, compensation is tied to two things: expertise and tenure. Both are carefully documented, both are negotiable, and both appear in every salary band.

And both are currently losing their value.

The question that follows does not appear on any transformation agenda. Yet it is the most important one: What are we still paying for?

The currency that is no longer a currency

For two hundred years, the value of work followed a clear logic. It came from knowledge, experience and craft. Acquired through education and training, deepened over the years, and rewarded through seniority. Salary bands, career levels and the quiet assumption that those who have been around longer know better still rest on this calculation.

That calculation no longer adds up.

The half-life of professional knowledge has fallen from around twenty-five years to six. Someone starting today will see half of their initial professional capital lose its value before reaching their first promotion. And seniority was never a true measure of competence. It was a proxy: those who had been around longer had seen more and therefore probably knew more. That proxy no longer works when knowledge is available to everyone at any time.

This is the real rupture. It is not that work is becoming less important. It is that the things that once made it valuable are losing their scarcity.

The wrong response

Most organisations treat this as an efficiency issue. They automate whatever can be automated, save hours and call it transformation.

In the short term, that works. Structurally, it accelerates the problem. Those who optimise the old model of value creation become faster within it. They do not become more relevant. What remains is an organisation that efficiently produces something fewer and fewer people need exclusively from it.

And something else happens that never appears in an efficiency calculation. When knowledge loses value and nothing replaces it, people do not merely lose tasks. They lose the foundation of their professional identity. That is why resistance to AI in organisations is almost never technical. It is a response to a perceived threat to one's role. And it rarely appears as open rejection. More often, it shows up as quiet non-use. The most revealing figure in an AI programme is not in the business case. It is the adoption rate after six months.

Where value now lies

When the answer is available at any time, value shifts to what comes before and after it. To the question. And to judgement.

Judgement is not a soft skill. It is the ability to reach a decision under uncertainty and in the face of conflicting evidence, and then stand behind it. It requires a point of view, meaning clarity about what an organisation stands for. It requires purpose, meaning clarity about the real objective behind what can be measured. And it requires empathy, meaning an understanding of what the people affected by the decision need.

The crucial point is this: individual judgement cannot be improved without limit. Anyone can expand their own knowledge. Judgement only becomes better when it meets other perspectives that test it and disrupt it.

The quality of judgement is a collective capability. The bottleneck in organisations is therefore not the amount of knowledge they possess. It is their ability to turn the knowledge of their people into shared judgement.

What this means for leadership

The traditional leadership role was the final decision-making authority. Leaders knew more, so they decided. That role depended on an information advantage that no longer exists.

The new task is more demanding: designing decision spaces. Creating conditions in which different perspectives genuinely meet, in which dissent is possible without damaging a career, and in which a decision is still reached in the end.

This is precise work, not a matter of creating a positive atmosphere. It is about decision logic, roles and cadence. Without deliberate design, collective intelligence does not emerge. What emerges are endless coordination loops.

It can be measured

This may sound like a question of mindset. But it can be tested.

Together with Prof. Dr. Katharina-Maria Rehfeld of International University Berlin, I examined transformation projects across the DACH region to determine whether they systematically use collective intelligence, artificial intelligence, both or neither.

The result is clear. Projects using collective intelligence are significantly more successful than average. The same is true for projects using AI. And projects that combine both have up to five times the probability of success compared with the standard approach.

The combination is the point. Technology alone is not enough. Participation alone is not enough either.

No need to invent something new

The good news is less spectacular than the scale of the problem might suggest.

This is not about inventing a new capability. It is about making a very old one available again: reaching a shared judgement. Two hundred years of work organisation pushed this capability into the background because it was dispensable in a model of value creation built around standardisation.

Now it is the bottleneck. And it can be designed.

Which brings us back to the opening question. What are we still paying for? Probably no longer for someone to know the answer. Rather, for their ability to reach a judgement with others that none of them would have found alone.

That does not appear in any salary band. Not yet.

Ricardo dos Santos Miquelino is Co-CEO of ... and dos Santos and a partner at the Swiss Future Institute. For more than fifteen years, he has worked at the intersection of collective and artificial intelligence with organisations including ZEISS, Deutsche Bahn and Coca-Cola.