The Approach
Understanding before action is not a delay.
It is a commitment to responsibility in complex situations.
The Approach
You may recognize this.
A situation feels urgent. There is pressure to respond. Ideas emerge quickly. Options are weighed. A solution is chosen. Action follows.
It feels productive. It feels responsible. It feels like problem solving.
And yet, sometimes, after all the effort, something remains unsettled. The issue returns. Or it shifts. Or it reveals that what was addressed was not the problem itself, but only one of its visible symptoms.
In our experience, many persistent difficulties do not arise from a lack of solutions. They arise from acting before we have fully understood what we are dealing with.
This approach begins with a simple but demanding commitment: understanding before action.
Slowing Down Without Standing Still
Understanding before action does not mean hesitation. It does not mean endless analysis or postponing responsibility.
It means creating space to ask different kinds of questions.
What exactly is happening here?
Who is involved, and how do they see the situation?
What assumptions are we already making?
What might we be overlooking because we are eager to fix something?
In complex situations, clarity rarely emerges from speed alone. It emerges from perspective, from conversation, and from the willingness to examine the situation before intervening in it.
This is not a rejection of action. It is a reordering. Action becomes more deliberate, more proportionate, and often more effective.
What We Mean by Complexity
When we speak of complexity, we do not mean mathematical difficulty or technical sophistication.
We are not referring to problems that can be solved by refining a model, collecting more data, or identifying the optimal variable.
In many professional environments, there is still an implicit assumption that a single correct answer exists — if only we think hard enough, analyze thoroughly enough, or consult the right experts. More information, better models, sharper simulations. The expectation is that the “best solution” will eventually reveal itself.
Sometimes it does.
But in many of the situations we encounter, the difficulty does not lie in calculation. It lies in interpretation.
Different stakeholders see different realities. Assumptions remain unspoken. Goals are partially aligned, partially conflicting. What counts as success for one group may create new tensions for another.
In such contexts, searching for the one best answer can be misleading. Not because rigor is unimportant, but because the situation itself does not lend itself to a single, stable solution.
Complexity, as we use the term, refers to situations where perspectives interact, consequences unfold over time, and interventions reshape the very system they seek to improve.
The task is not to extract the right answer from the situation, but to engage with it carefully enough that meaningful action becomes possible.
Working Together in Complexity
In groups, additional dynamics come into play.
Sometimes clarity appears to arrive through authority. A confident leader offers direction, reduces ambiguity, and accelerates decision-making. This can be valuable. Yet speed and certainty do not guarantee alignment with the situation itself.
At other times, the focus shifts toward harmony. The group works hard to ensure that everyone feels heard and included. This, too, can be valuable. Yet consensus does not automatically produce a sound intervention.
Both tendencies, decisive leadership and relational harmony, can contribute to progress. But in complex situations, neither replaces the need for shared inquiry.
The challenge is not to choose between authority and consensus. It is to create a space in which multiple perspectives can inform action without being prematurely collapsed into a single narrative.
Methods and Frameworks
Over the years, many frameworks, models and step-by-step approaches have been developed to support problem solving. Analytical tools help structure information. Visual models clarify relationships. Structured processes provide guidance when situations feel overwhelming.
These methods can be immensely helpful. They create language where there was confusion. They introduce discipline where there was drift. They make collaboration more deliberate.
Yet no method, however refined, can substitute for judgment.
A model does not decide what matters.
A framework does not determine which perspective deserves attention.
A step-by-step approach cannot resolve tensions that are rooted in conflicting interpretations or values.
Tools shape how we look at a situation. They highlight certain elements and downplay others. Used thoughtfully, they deepen understanding. Used prematurely, they can reinforce assumptions that were never examined.
In our work, methods are not answers. They are lenses. They help us see more clearly, but they do not see for us.
Serious Gaming as Experiential Inquiry
Serious games offer a different kind of lens.
Whether digital or played around a table, a serious game creates a temporary world in which patterns become visible. Participants act, observe consequences, reflect together and try again. Assumptions surface not because they are discussed abstractly, but because they are experienced.
In such settings, people can experiment with different strategies, test alternative interpretations and explore the meaning of “better” in a safe environment. Action and reflection become intertwined.
Serious gaming does not replace analysis. It complements it. It allows groups to experience complexity rather than merely describe it. And through that experience, understanding often deepens in ways that purely conceptual discussion cannot achieve.
Technology and Artificial Intelligence
Technological tools, including artificial intelligence, add yet another dimension.
Analytical capacity has expanded dramatically. Patterns can be detected at scale. Scenarios can be simulated rapidly. Options can be generated in seconds.
These developments are powerful. But they do not change the fundamental question: what are we actually trying to understand?
Technology accelerates processes. It expands possibilities. It makes action easier and sometimes cheaper. But acceleration does not replace interpretation.
If we begin with unclear assumptions, AI can optimize the wrong objective with remarkable efficiency. If we frame the situation narrowly, simulations will faithfully explore only that narrow frame.
In that sense, technology amplifies our starting point. It strengthens clarity and it strengthens confusion.
For that reason, understanding before action becomes not less important, but more.
Judgment Remains Human
No situation defines itself.
What counts as “better,” “necessary,” or even “the problem” depends on values. On priorities. On what we are willing to protect, and what we are willing to risk.
Data can inform these decisions. Models can clarify trade-offs. AI can generate options and expose patterns. Serious games can reveal dynamics that remain hidden in discussion. But none of these can decide what ought to matter.
Judgment remains human. And with judgment comes responsibility.
Artificial intelligence may outperform humans in speed, pattern recognition and analytical breadth. It can generate alternatives, simulate outcomes and optimize objectives at remarkable scale. But it will never take responsibility.
Responsibility belongs to those who define the objective, who interpret the results, and who decide which consequences are acceptable.
In complex situations, problem solving is therefore never only technical. It is interpretive, normative and ultimately accountable.
Understanding before action is not only about clarity. It is about owning the consequences of intervention.
Foundations of the Approach
No problem defines itself.
Every problem formulation reflects assumptions, perspectives and values.
The “best solution” is often an illusion.
In complex situations, outcomes depend on perspective, timing and values. What appears optimal from one standpoint may create new tensions from another.
Optimization requires interpretation.
Before improving performance, we must examine what counts as improvement — and for whom.
Tools support judgment, they do not replace it.
Models, frameworks, serious games and AI systems are lenses. They shape perception but do not determine responsibility.
Judgment remains human.
Technology can analyze and recommend. It cannot assume accountability for consequences.
Responsibility is part of problem solving.
To intervene in complex situations is to privilege certain outcomes over others.
Understanding precedes intervention.
Clarity about the situation comes before attempts to change it.
“This is not a call for slower action. It is a call for clearer action.”
Two Complementary Paths
If this way of working resonates with you, you can explore it in two complementary forms.
The book The New Art of Problem Solving offers a reflective journey into the foundations of this approach. It provides context, language and conceptual depth for those who want to understand the thinking behind responsible problem solving before applying it.
The book Solving the Right Problem translates the same ideas into structured exercises and practical applications. It invites you to engage directly, to experiment, to observe, and to develop skill through practice.
The underlying commitment is the same. The form differs.
You can choose the path that best supports how you prefer to work and learn.
The Problem Manager
As artificial intelligence becomes more capable and more embedded in professional life, organizations gain unprecedented power to analyze, simulate and optimize.
What does not increase automatically is responsibility.
AI can generate insights. It can recommend actions. It can optimize according to defined criteria. But it cannot assume accountability for the criteria chosen, the trade-offs accepted, or the consequences that follow.
For that reason, we believe organizations will increasingly need a new kind of role: the problem manager.
Not someone who “has the answer.”
Not a hierarchical authority who decides alone.
And not merely a facilitator of discussion.
The problem manager safeguards the quality and the responsibility of problem solving itself.
This role ensures that assumptions are surfaced before solutions are selected. That values are articulated before optimization begins. That tools, whether analytical models, serious games or AI systems, are used to deepen understanding rather than to bypass it.
In an environment where action becomes easier and faster, stewardship of responsibility becomes essential.