Complexity management

Complicated is not complex

Almost every executive management team knows the feeling that things have become “more complex”: more dependencies, more people involved, more speed, less predictability. The usual response is to analyze more, gather more data, plan more, and hand responsibility to experts. Whether that holds depends on what kind of situation you are dealing with.

A market, a change process, or the introduction of AI in a company are complex too. A common fallacy is that more people mean more complexity. What grows first with the number of people involved is the effort:

That makes coordination more complicated, but not yet complex. Complexity comes from the way people communicate: through feedback, ambiguity, and the need to create meaning together.

How to recognize complexity

Complex systems show typical effects. In everyday work, they are often read as individual mistakes.

Two basic operations

c2m comes from Gitta Peyn's FORMwelt approach and stands for “conTEXT to meaning”: the path by which meaning arises from context. The model rests on two moves that shape every engagement with a topic.

Together with the speed of thinking, the two operations give a picture of how a situation is currently being thought about and discussed.

The levels K0 to K5

The levels describe how a conversation, a decision, or a team is currently handling a topic, not who someone is. Depending on the topic, pressure, and environment, the same group moves between levels. It is not a ladder you climb and then stay at the top: Under pressure, a conversation falls back to an earlier level, K2 to K0, K3 to K1.

Each level has strengths that are exactly right in the matching situation, and a downside.

The model is an observation tool, not a test. It helps you recognize a conversation that has stalled: Is only one axis being negotiated? Is the group jumping around? Are two sides talking past each other because one holds two axes and the other holds one?

Complexity and mutual understanding

Three elements come together in communication: meaning, conveying, understanding (more on this in the topic Communication systems). Where a team at level K2 jumps between axes, conversation patterns emerge in which someone dictates something, someone else confirms it, and no one brings it together: actionism, control, isolated solutions, and shifting attempts at regulation without mutual understanding. Where conversations stay on one axis, the positions repeat themselves.

Anyone who wants to manage complexity therefore needs a communication architecture that can hold several axes without everyone juggling on their own. That includes being able to say what you mean and to mean what you say. It also includes thinking in relationships, observing how you form your own judgments, and keeping descriptions concrete enough for others to follow.

Nine common misconceptions

Management author Stephanie Borgert has compiled typical misconceptions about dealing with complexity. The list works as a touchstone for your own organization.

Which model asks what?

Several models work with the term “complexity.” They don't replace one another; they answer different questions.

Cynefin distinguishes situations by the relationship between cause and effect: obvious, complicated, complex, chaotic. In the complicated domain, the approach is sense, analyze, respond; in the complex domain, probe, sense, respond, with small, safe-to-fail experiments; in the chaotic domain, act first to establish order. The two models complement each other: First the situation is classified, then you check whether the conversations are up to it.

The MHC is a model of development and measurement. It builds on the developmental psychology of Piaget and Kohlberg and is considered precise. It explicitly does not describe a person's existential depth and has no moral ranking, but it remains a model with pigeonholes: The order reached counts as an ability you either have or don't have. c2m, by contrast, is a working model for conversations, decisions, and teams. Its focus is on agility, including falling back under pressure and switching deliberately.

The Viable System Model (VSM) and Ashby's law of requisite variety address the question of structure. In practice, the two interlock: The best structure is of little use if discussions within it run along a single axis. More on this in the topic Viable System Model (VSM).

What it looks like in a company

Where complexity management is needed, the occasions are usually concrete.

The ability itself can be practiced: noticing your own “reality tunnel,” deliberately changing the line of view, observing your own resistance to what is uncomfortable, leaving your specialist bubble, and checking empirically instead of assuming. Philosophy, logic, programming, and foreign languages train dimensioning.

Limits of the model

• A model, not a verdict. c2m maps patterns and replaces neither expert review nor responsibility for decisions.
• Not a diagnostic tool for people. Using it as a label (“he's a K1”) turns an observation tool into a pigeonhole and complexity management into know-it-all behavior at a higher level.
• Connection before lead. Thinking two levels ahead quickly comes across to others as vague or too complicated. Mutual understanding needs the ability to connect, not to overtake.
• Patterns, not measures. The model names possibilities. Translating them into decisions, structures, and processes is still work.