The decision to write this blog was not automated. Rarely in my life have I made a truly automatic decision. Yet I make many of them, every single day. I have made enough wrong ones to know that learning exclusively from failure is an expensive strategy.
Every decision has a pattern. Something pulls it: a vision, a goal, a desire. Data makes that pull clearer. Science makes it sharper.
We all want our decisions to be as objective as possible. That is precisely why decision science exists, to remove as much subjectivity as we can. Of course, as long as humans make decisions, there will always be a subjective element. But that is also what makes us human. Perhaps that is why football still has human referees, even though every decision on the pitch could have been made by an electronic system years ago.
What I learned about my own decisions, I later recognized at a much larger scale, inside organizations where decision-making is not a personal habit but an operational system. And like any system, it has a cost.
When we are in a position to make decisions, that position carries real power. The power to control and direct a system. Sometimes that power becomes the point itself, disconnected from any rational justification for why this particular person must make this particular decision, or whether they are genuinely the most qualified to do so.
We assume that because someone sits at the head of a particular organizational group, they possess the highest level of relevant competence. Perhaps. But as long as we rely solely on experience, without methodology, without science, without understanding the process or collecting data that competence does not scale.
Experience without structure is just pattern recognition with a confidence problem.
What is critical here is understanding the true cost of the time consumed in making a decision. We almost never see organizations actually calculate this. In knowledge-driven organizations, decision time is the most expensive process on the books. It just never appears on them.
McKinsey research across more than 1,200 global business leaders found that inefficient decision-making costs a typical Fortune 500 company 530,000 days of managers’ time each year, equivalent to roughly $250 million in wasted labor. And that figure does not include opportunity costs: lost market share, failed products, strategic misses. (McKinsey, Decision Making in the Age of Urgency)
If we calculate the true cost of a decision-making process, it will likely rank among the most expensive processes in the entire organization, and I am speaking primarily about non-production processes. A Bain & Company study calculated that a single weekly executive meeting at one Fortune 500 company cost $15 million a year, once preparation time and downstream meetings were included.
Consider a board meeting that runs three hours. The cost of those three hours, when you account for the hourly rate of every person in the room, easily reaches several thousand euros. And that is the optimistic scenario, where a decision is actually made. When the meeting ends without one, that cost becomes pure waste. The same people will reconvene, restart, and repeat.
I once witnessed a large global company organize a gathering of directors from 120 countries in one city. The event cost several million euros. If they managed to move the company forward by more than that investment, it was worth it. I am not saying the meeting should not have happened. I am saying most of what was decided in that room had a pattern. And patterns can be designed.
When decision-making is slow, it is rarely because the decision is genuinely complex. More often, the process behind it is undefined. Roles are unclear. Criteria do not exist. Data is missing or ignored. And so the decision becomes expensive, not because it needed to be, but because no one designed it not to be.
The first step toward decision automation is not technology. It is recognizing that decision-making is a process. And like any process, it can be measured, improved, and eventually automated.
The cost is already there. The only question is whether you are aware of it.
What would it mean for your organization if you could calculate the true cost of every decision made last year?
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