There is a point where more thinking no longer helps. Where more analysis does not create more clarity but rather more delay. That point occurs more often than we think.
When the complexity of a system increases, the world changes faster than our plans. The conditions shift. Actors react. New information emerges. And while we sit and discuss the best solution, the problem has already changed.
That is when we must increase the pace.
When complexity becomes chaos
The Cynefin model distinguishes between simple, complicated, complex and chaotic situations. In the simple there are clear connections. In the complicated, expertise is required but solutions can be calculated. In the complex, we must test our way forward because cause and effect can only be understood in retrospect. In the chaotic, there is no time for analysis. Then you have to act to create stability.
When complexity increases, you approach chaos. This means that before you have the time to formulate a solution, the world around you has already changed. The solution that felt right on Monday is irrelevant on Wednesday.
In such situations, it is dangerous to chase perfection. What is needed is movement.
The natural disaster as a metaphor
Think of a natural disaster. A forest fire spreads quickly. A hurricane changes direction. A flood grows hour by hour.
The emergency services do not sit in a room to analyze all possible scenarios before they act. They act immediately to create some form of control. They block off. They evacuate. They cool down. They improvise.
The plans are often made in advance precisely because they know that there is no time to think when it happens. The ability to act is built in.
This does not mean that analysis is unimportant. It means that the analysis must be prepared and action-oriented.
When minor crises behave like major ones
We often think that this only applies to acute disasters. But the same dynamics arise in less dramatic situations.
A technological shift takes hold faster than expected. A new digital platform changes customer behavior in a few months. A rumor on social media spreads faster than the organization’s communication can react.
If the organization is stuck in long decision-making processes, the reality passes by.
They say they were overtaken. In reality, it was complexity that increased faster than their pace and ability to adapt.
When talking makes you miss the opportunity
There are many examples where the pace of change is so high that discussion becomes a form of delay.
When smartphones took hold, several established companies discussed how the market might change. Meanwhile, others built entirely new business models around apps, platforms, and digital ecosystems. Those who analyzed for too long lost ground.
When generative AI became available to the public, the same pattern emerged. Some organizations appointed studies and began talking about what it could mean. Others immediately began testing use cases on a small scale. The difference in learning speed was enormous.
During the first months of the pandemic, many organizations faced the same choice. Wait and see. Or experiment quickly with remote work, digital customer meetings, and new delivery models. Those who acted early gained an advantage even when the situation stabilized.
In all of these cases, it wasn’t the perfect plan that made the difference. It was the pace of testing.
Understanding by doing
In complex systems, you can’t understand everything before you act. Understanding comes through action. When you test a prototype, you get data. When you release a beta version, you get feedback. When you conduct a pilot project, unexpected obstacles become visible. Doing becomes a way to gather information.
It’s like navigating in fog. If you stand still, you don’t see anything anymore. If you move forward slowly, the perspective changes. New contours emerge.
In complexity, movement is a knowledge strategy.
Mental shift from control to pace
As complexity increases, you have to change your mental state. From trying to control every variable to increasing the pace of learning. It means collecting data faster. Shortening decision paths. Accepting that some decisions are made with incomplete information.
It also means letting go of the illusion of the final solution. In complex systems, there is rarely an optimal point. There are temporarily working solutions that must be adjusted continuously.
Anyone who thinks they know or waits for certainty instead of continuously evaluating data and re-evaluating decisions risks becoming irrelevant.
Building capacity for pace
It is not about running aimlessly. It is about building structures that enable rapid action. Prepared experimental formats. Clear mandates. Budget for rapid tests. A culture where mistakes in small-scale experiments are accepted.
Just as the emergency services train on scenarios before a crisis occurs, organizations need to train on acting in uncertainty before the pressure becomes acute.
Otherwise, you get stuck in meetings when you should actually be moving.
When the system is not keeping up
Sometimes the pace of change increases so quickly that the existing system cannot handle it. Decision-making processes take too long. Budget cycles are too slow. Regulations are adapted to a slower reality. Then it is not just the pace of the projects that must increase. It is the system’s responsiveness that must be strengthened.
Otherwise, the organization becomes a heavy boat in a sea that has suddenly started to storm.
Innovation in high complexity
For an innovation leader, this is central. When you feel that complexity is increasing, it is a signal. Not that you should analyze more, but that you should test more. Smaller pilot projects. More iterations. Shorter feedback cycles. It also means that you must create a sense of urgency without panic. Urgency. Pace without chaos.
The great danger is to become passive while waiting for clarity. In complex situations, clarity arises through action.
Not being overtaken
Being overtaken is rarely a coincidence. It is the result of someone else learning faster. When the world is changing rapidly, the most important competitive advantage is not capital or resources. It is the ability to translate uncertainty into experimentation.
Complexity is not a signal to stop. It is a signal to move faster. And in a world where change is the only constant, it is perhaps the most important capability of all.
