Last Updated on 13. April 2026
Today, companies operate in an environment that changes faster than their own planning cycles. Most organizations respond to this with what they know: they rely on more expertise, more analysis, and more planning. The problem isn’t that these tools are ineffective. The problem is that they were designed for a world that no longer exists. The crucial question is therefore: How can leadership in turbulent times succeed so that direction translates into effectiveness without simplifying away the complexity?
Different situations require different approaches
To understand why traditional management is increasingly reaching its limits, a fundamental distinction is helpful: that between complicated and complex situations. In complicated situations, cause and effect can, in principle, be analyzed. The correct course of action is: identify, analyze, plan, act. An engine is complicated, but predictable with sufficient expertise.
In complex situations, cause and effect can only be recognized in hindsight. Here, the appropriate course of action is: try, evaluate, adapt, react. Markets, organizations, and most strategic issues are complex, yet we routinely treat them as complicated problems.
And this is where the real problem begins: How much of what companies must manage today is actually complicated, and how much is complex? When this question is asked honestly, the picture shifts significantly. Yet our brains and our organizations struggle to accept this shift.
Why we want to see everything as complicated
Experiences from consulting projects and training sessions reveal a consistent pattern: Most executives and experts systematically underestimate the level of complexity in the situations they manage. This is not an individual failure, but a human pattern.
Our brains love autopilot mode. We compare new situations to familiar patterns and simplify them until they seem manageable. We overestimate the impact of expertise and routines. Something feels familiar, even though objectively it remains dynamic and therefore uncertain. We systematically underestimate the duration of projects, even though we have experienced the opposite in similar projects in the past.
Furthermore: Our organizations reward predictability. Early commitments, high utilization, and optimistic forecasts are seen as signs that uncertainty is under control. This creates a systematic distortion away from reality, toward an illusion of controllability.
But if one is convinced that something is predictable, it will also be calculated—using the tools developed for predictable problems. And this is precisely where three typical management fallacies arise, which we observe time and again in practice.
Fallacy 1: The expert will fix it!
The first fallacy is the belief that expertise alone is sufficient to manage complex situations. Expertise is valuable, but it draws us into “complicated thinking.” We believe that the expert brings a blueprint, a pattern that can be applied. Precisely because experts have experienced situations twenty times before, there is a high risk that they will see patterns where none exist.
This is evident in many areas: We evaluate the suitability of candidates based on industry experience rather than on their ability to deal with the unknown. We overvalue accessible knowledge compared to the ability to manage under uncertainty. Expertise remains indispensable, but it leads to typical control errors in complex situations if it is not complemented by the ability to question one’s own patterns of experience.
Fallacy 2: More analysis!
The second fallacy follows from the first: If one believes a situation is predictable, then the answer to uncertainty lies in more analysis. More data, more studies, more discussion about the optimal approach before trying anything.
In practice, we see how companies think through, discuss, discard, and redesign control mechanisms—in the hope of finding the right solution on paper. But when something is complex, it remains complex, even if you analyze it thoroughly. The cause-and-effect relationship only becomes apparent through experimentation. This also has to do with a culture of error: People are reluctant to try something—even when the risks would be manageable. Instead of testing whether something works, they analyze whether it could work.
Mistake 3: More plans!
The third mistake is the belief that better planning is the answer to rising uncertainty. In practice, this leads to a paradoxical effect: uncertainty generates more planning, and more planning actually generates more uncertainty because it conveys a sense of control that does not correspond to reality.
A Gantt chart is like a sedative. It looks as though everything is under control. But it obscures the discussions that are actually necessary: What assumptions underlie the plan? What happens if one of them turns out to be wrong? Who then decides what changes? The more detailed the plan, the greater the illusion—and the more painful the correction when reality catches up with it.
Rethinking management in dynamic times: Three proposals
When the classic answers—more expertise, more analysis, more plans—reach their limits in complex situations, what should management look like? From our consulting practice and work with the mgm Flow Model, three principles emerge that are effective in practice.
- Dare to make fewer early commitments and more late decisions.
This sounds counterintuitive at first. Organizations demand early commitments because they suggest certainty. But an early decision also means taking alternatives out of the running too soon—at a time when we know the least about the situation. The core idea of the iterative-incremental approach is different: don’t postpone decisions, but make them as late as is responsible—at the “Last Responsible Moment.” The time in between is used to consciously test and eliminate options. This is not hesitation, but smart management under uncertainty. Specifically, this means: fewer rigid deadlines, more trigger points that prompt decisions when the information justifies it. - Less workload, more flow.
Most organizations manage through workload: the main thing is that everyone is busy. But high workload does not generate high effectiveness—quite the opposite. Like on a congested highway, maximum workload leads to gridlock. More lanes don’t help when traffic gets denser. We’re measuring the wrong thing: not how many projects are running, but how quickly work gets done. In the mgm Flow Model, the coordination level addresses precisely this question: How is the relationship between concurrent initiatives managed so that actual impact is created? Limitation—that is, consciously saying no to new projects until existing ones are completed—is not a punishment. It is control. - Less conductor, more coach.
The classic image of leadership resembles that of a conductor: one person sets the tempo, everyone follows the score. In a dynamic environment, this does not work because a single person’s perspective is too narrow—too slow, too localized, too limited. More effective is the image of the coach: He sets guidelines, builds capabilities, and trusts that the team on the field will make the right decisions independently. In practice, this means a shift from “We decide” to “We provide guidelines for decisions.”
Increasing complexity no longer demands central control, but rather more decentralized decision-making capacity – accompanied by consistent coaching rather than micromanagement.
Translating Orientation into Effectiveness
These three principles do not stand alone. They are interlinked and together address the question of how companies can shorten the path from strategic orientation to operational effectiveness without simplifying away complexity. In the mgm Flow Model, this interconnection is examined systematically: from market needs through strategic alignment and the coordination of initiatives to operational implementation and customer utilization. The impact does not arise at a single level, but through improved interaction between these levels.
Portfolio management serves as a particularly revealing litmus test in this regard. Many companies manage their portfolio based on the principle of maximum utilization: every initiative that seems sensible is launched. The result is an overloaded portfolio, increasing lead times, and teams jumping back and forth between too many initiatives. Effective management begins here with the willingness to do fewer things at once and, in return, get the right things done faster. Fewer deadlines, more trigger points. Fewer parallel projects, more consistent throughput.
My colleague Antje von Garrel describes what further exacerbates this management challenge in her article “What Happens When AI Works?”: In organizations, AI first acts as a mirror, revealing where decision-making authority is unclear and responsibilities are not defined. When governance doesn’t work, AI exacerbates the problem instead of solving it. Both perspectives complement each other: governance capability is the prerequisite for organizations to actually benefit from the growing capabilities of new technologies.
Next Monday would be a good time to start with four questions:
- Where are we dealing with true complexity, yet treating it as a mere complicated problem?
- What triggers should guide our decisions today?
- Which ongoing projects should we consciously halt to complete the remaining ones more quickly?
- And which decisions must my team make on their own today?
Don’t want to answer these questions on your own? My team and I would be happy to support you.
This post is based on a presentation by Antje von Garrel, Manager and Lead AI Consulting at mgm consulting partners, as part of the joint presentation and networking event “Effectively Connecting Spaces, Organization, and AI” with Steelcase on March 18, 2026, in Hamburg.
