In many organizations the word complexity is used as an explanation for why things cannot be changed. It is said that the problems are complex, that the outside world is complex or that the systems are too complex to touch. But often different types of complexity are mixed together. In order to work effectively with innovation, we need to distinguish between problem complexity and system complexity and understand how they interact.
What is really meant by complexity?
Complexity describes situations where cause and effect are not linear, where many parts affect each other simultaneously and where the outcome cannot be predicted in advance. A complex context is characterized by uncertainty, mutual dependencies and that small changes can have large consequences.
It is important to distinguish complexity from something just being complicated. Something complicated can be difficult but can be analyzed and broken down. Complexity means that the whole is more than the sum of the parts and that understanding often occurs only after the fact.
Problem complexity as something to be solved
Problem complexity is about the inherent complexity of the issue we are trying to address. Some problems are basically simple. Others are complex because they concern human behavior, values, power or interaction between many actors.
Climate change, social cohesion or digital transformation are examples of problems with high problem complexity. They cannot be solved with a single action or a clear manual. Here, innovation needs to be about learning, exploring and adapting over time.
Problem complexity is thus not something negative in itself. It describes reality as it is.
System complexity as something we create
System complexity is the complexity added by the systems we build around the problem. It can be organizations, processes, regulations, IT systems or control models. Often these are created with good intentions, but over time they grow into structures that are difficult to oversee and change.
When a fundamentally manageable problem is perceived as impenetrable, it often depends more on system complexity than problem complexity. Decisions must pass through many levels, responsibilities are unclear and solutions get stuck in culverts.
Essential and accidental complexity
To deepen the understanding, we can distinguish between essential complexity and accidental complexity. Essential complexity is the complexity inherent in the business problem or societal challenge itself. It cannot be removed but must be managed.
Accidental complexity is the complexity we ourselves have added through our solutions, structures and ways of working. It often occurs gradually and is rarely a conscious choice.
In innovation, a central task is to reduce the accidental complexity to free up energy to deal with the essential complexity. When organizations fail to innovate, it’s often because all energy is spent navigating internal systems rather than working on the real problem.
Innovation as work with the right kind of complexity
Innovation is not about eliminating complexity but about working with the right kind of complexity. In early phases, one often needs to accept and even increase complexity by exploring more perspectives, hypotheses and possibilities. Later, innovation is about creating simplicity and clarity in what is to be implemented.
Problems arise when you try to simplify too early or when the complexity of the system makes it impossible to experiment. Innovative processes therefore need both protected spaces for exploration and structures that do not stifle learning.
The role of the innovation leader
For an innovation leader, one of the most important tasks is to distinguish which complexity is necessary and which is self-created. It requires courage to question processes, control models and established ways of working.
The innovation leader also needs to help the organization accept that some problems cannot be solved quickly or fully. Instead, it’s about making progress, learning and adjusting direction over time.
By clarifying what the real problem is, you can often peel away layers of unnecessary system complexity.
Cynefin as support for the right approach
The Cynefin framework can play an important role in this work. It helps distinguish between simple, complicated, complex and chaotic contexts. By placing a problem in the right domain, it becomes clearer which methods are appropriate.
In complex contexts, Cynefin recommends trying things out, observing what happens and then adapting. This is in contrast to analyzing an optimal solution. For innovation work, this is crucial. Using the wrong approach to the wrong type of problem often creates more system complexity.
Cynefin can also help show when a problem perceived as complex is in fact complex but has been made unnecessarily difficult by over-control.
When complexity becomes an alibi
A recurring pattern is that complexity is used as an alibi for not changing. It is said that the world is too complex or that the system is too big. Often this hides a reluctance to remove structures that once created security but now hinder development.
Working with innovation means daring to see where the complexity actually comes from and taking responsibility for the part you yourself contribute to.
To create room for action in complex systems
By distinguishing between problem complexity and system complexity, organizations can create greater room for action. You can accept that some issues are complex while at the same time simplifying what can be simplified.
For innovation leaders, this involves continuously asking the question whether it is the problem or the system that is currently difficult. The answer to that question determines whether to invest in more exploration or in removing unnecessary friction.
When the right kind of complexity is allowed to take place and the wrong kind is reduced, the conditions for real innovation are created.
