From Predicting To Probing: How Leaders Succeed in Complexity Through Sensemaking and Experimentation

As I explored in a previous article, many of the leadership behaviors we rely on under pressure—more thinking, more data analysis, more detailed planning—are forms of contraction that tend to fail in complex, what I call VUCA++ systems.

They emerge from nervous systems that narrow to reduce uncertainty and regain control. The drive to understand, to model, and to decide more precisely feels intelligent. It feels responsible. And yet, in complex systems, it often produces the opposite effect. I call this The Intelligence Paradox.

The alternative is not to abandon thinking, data, or planning. It is to recognize that they may not be the ideal first response to complex and uncertain realities. So what is the ideal response and why? How do we shift our nervous systems to expand, and then translate that into better strategic decisions, more adaptive actions, and more effective leadership in real-world conditions?

The answer is not a single move. It is a sequence.

1. The first shift is in how we perceive. From analyzing to sensemaking.

2. The second is in how we act. From execution to experimentation.

3. The third is in how we move forward. From fixed strategy to emergent strategy.

   
   

Throughout, planning and delivering do not disappear. They change. They become adaptive and alive. Responsive rather than predictive. A living process of learning and becoming rather than a completed artifact that needs relatively mindless operationalizing.

This is how leaders move from contraction to expansion, from control to coherence, and from the Intelligence Paradox to intelligent action in complex systems.

1. From AnalyZING to Sensemaking

Most leadership models begin with the assumption that analysis is the primary act. Gather the data, analyze the situation, choose the best course of action, and then execute with discipline. This sequence has been reinforced over decades because it works well in simpler, clearer, less complex environments where the world and how it operates can be understood in advance.

In complex systems, however, this sequence begins to break down. The system is not static enough to be fully analyzed before action. Variables are interacting in ways that cannot be fully predicted. There are interdependencies and interrelationships between agents and parts that minds and even algorithms cannot hold at once. The act of deciding and executing alters the conditions themselves.

What appears to be a rational, well-structured decision and plans can quickly become disconnected from reality because the reality on which it was based has already shifted.

This is where the shift from analysis to sensemaking becomes essential. Rather than assuming that the answer exists and can be identified through sufficient analysis, sensemaking recognizes that the answer is still forming. It must be discovered through engagement with the system as it unfolds.

This changes the posture of leadership. Instead of moving quickly to closure, leaders learn to remain in contact with what is actually happening. They pay attention to weak signals, to emerging patterns, to information that does not fit their existing model.

They allow multiple interpretations to coexist long enough for something more accurate to emerge. They resist the strong temptation to close down options prematurely. This is not about delaying decisions indefinitely.

It is about ensuring that decisions are grounded in reality, and that interventions go beyond solutions designed to quickly eliminate problems to experiments that build more knowledge of the system and refine our understanding of the problems and how they should be defined and approached.

Why Nervous System States Determine Sensemaking Skills

The quality of sensemaking is not primarily determined by the amount of information available. It is determined by the state from which that information is perceived.

In what I call F-states—fixing, fussing, fixating, fawning, freaking out, etc.—perception contracts. Leaders filter what they see, often unconsciously, in order to reduce ambiguity and regain a sense of control. Information that confirms the emerging narrative is amplified. Information that challenges it is dismissed or minimized. The system appears clearer than it actually is, but that clarity comes at the cost of accuracy.

In what I call C-states—curiosity, calm, courage, collaboration, etc.—perception expands. Leaders can take in more of what is happening, notice nuance, and hold multiple perspectives without collapsing them into a single conclusion.

This does not remove uncertainty, but it changes the way we relate to it. Uncertainty becomes something that can be worked with, rather than something that must be eliminated.

This is why brain states come first. Without a shift in state, attempts to improve strategy or decision-making will simply reinforce the same patterns at a higher level of sophistication.

Sensing COMPLEX SystemS Collectively as THey Unfold

Effective sensemaking requires more than individual awareness. No single leader, however capable, can perceive enough on their own to navigate complexity accurately. Intelligence must be distributed.

This means creating conditions where people can speak honestly about what they are seeing and experiencing. It means allowing dissent and difference to surface without being treated as a threat, and without collapsing into complaints and conflict. It means paying attention to signals from the edges of the organization, where change is often first visible, rather than relying only on centralized or aggregated data.

In practice, this often requires leaders to tolerate a level of ambiguity and tension that feels uncomfortable. The impulse to resolve that tension quickly is strong. It shows up in meetings where silence is filled too quickly, where questions are answered before they are fully explored, where emerging challenges and crises are glossed over rather than fully engaged in, where problems are defined too rapidly in conventional TLAs and company/industry jargon, and where alignment is reached before assumptions have even been surfaced.

Resisting that impulse, even briefly, creates space for something more accurate to emerge.

Investing In Trust, Safety & Stability

This capacity to sense collectively does not suddenly appear when it is needed. It has to be built in advance. Trust, psychological safety, and an internal sense of stability function much like a savings account. They accumulate slowly through repeated behaviors, consistent modeling, and everyday ways of working long before any crisis or challenge arrives.

Culture eats strategic sense-making for brunch.

When leaders invest regularly in openness, grounding, honesty, receptivity, listening, learning, and relational health, they are building reserves within their own nervous system and the collective nervous system (culture).

Then, when the organization encounters something difficult to interpret—a weak signal that challenges assumptions, conflicting data, or a moment of real disruption—there is something to draw on.

Without those reserves, the system contracts. It falls back into F-states, closing down precisely when it needs to open. We fall backward onto outdated but comfortable patterns of thought and action when we need to fall forward, and fail forward, through agile, adaptable, and imaginative insights and ideas.

In complexity, uncertainty & crisis We lose our minds just as we need them most.

With trusting, strong, reciprocal relationships—that require calm, curious, and connected individuals—it is possible to stay in C-states together even under pressure, allowing the group to remain capable of making sense of what is actually unfolding rather than reacting to it.

2. From Executing to Experimenting

Once leaders begin to sense more effectively, a second shift becomes necessary. It is not enough to see more clearly. The system must also be engaged in a way that generates new information, faster learning, and quicker adaptation.

This is where the move from planning to experimenting comes in. Planning seeks to define the future in advance, to map a path from the present to a desired outcome. In stable environments, this is both efficient and effective. In complex environments, it often creates rigidity because the plan is based on assumptions that will inevitably change as the system evolves.

Experimentation takes a different approach. Rather than committing fully to a single path, leaders take small, intentional actions designed to learn from the system. These actions are not random. They are structured, contained, and purposeful. Each is a way to test an assumption, explore a possibility, or probe the system to see how it responds.

Over time, these experiments generate feedback, even if they don't solve a problem or do so only partially. Patterns begin to emerge. Some approaches show promise, others reveal constraints, and some fail quickly. This is not a problem. It is the mechanism through which intelligence is generated.

Failing Forward

For experimentation to work in practice, something deeper has to shift in how failure is experienced, not just how it is described. In many organizations, the word failure still carries a strong emotional charge. It signals risk, exposure, and potential loss of status. It is one of the fastest ways to trigger contraction into F-states. Even when leaders say that failure is acceptable, the nervous system often tells a different story.

This is why psychological safety cannot be declared. It has to be built, and more importantly, felt.

People need repeated, consistent experiences that show it is genuinely safe to try something that might not work. That their credibility is not at risk if an experiment produces an unexpected result. That learning is valued as much as the outcome. Without this, experimentation becomes performative. People either avoid taking real risks or design “safe” experiments that confirm what is already known.

Failing forward reframes failure entirely.

An experiment that does not produce the expected outcome is not a failure of performance. It is a success of learning, provided it was designed with learning in mind. The key is intentionality. Before the experiment begins, there needs to be clarity about what it is intended to reveal. What assumption is being tested? What insight would be valuable, regardless of outcome?

When this is clear, the emotional experience shifts.

The result is no longer judged simply as success or failure. It is evaluated based on what it has made visible. This creates a different kind of safety. Not the absence of standards, but the presence of intelligent intent. People are not free to do anything without consequence, but they are supported in taking both smart and wise risks that contribute to collective understanding.

Over time, this repatterns the system.

Failure loses its charge. Learning becomes the currency. And the organization develops the capacity to stay open, even when things do not go to plan. This is what makes experimentation sustainable, and it sets the foundation for the discipline that follows.

The most effective experiments are designed to be safe enough that failure does not create significant harm, but real enough that the feedback they generate is meaningful. They are small enough to adjust quickly, but substantial enough to engage with the system in a genuine way. They are diverse, exploring multiple pathways rather than reinforcing a single hypothesis.

This creates a "portfolio of learning" rather than a single point of failure on a binary go/no-go solution.

Each experiment is, in effect, a question posed to the system. The response is often not what was expected. That is precisely what makes it valuable. It reveals something that could not have been known through analysis alone.

Creative rigor: The Discipline of Experimentation

There is a persistent belief in many organizations that creativity and rigor sit at opposite ends of a spectrum. That imagination requires looseness, and that discipline requires constraint. In practice, the opposite is true. The most effective leaders, and the most eminent creators, move fluidly between the two.

Research into high-level creative performance consistently shows that breakthrough thinking is not a single mode, but a rapid oscillation between generating ideas and evaluating them, between openness and discernment, between expansion and refinement.

This is what I call "creative rigor." I wrote my first-ever white paper with this title in 2000, when I was running a strategic creative agency.

It begins with the capacity to generate bold, even provocative ideas without prematurely filtering them. We are seeking breakthroughs, transformative concepts that challenge the status quo and the received rules of the game. This is the creative side of the equation, and it requires C-states that enable openness, curiosity, creativity, imagination, and connection (for customer/employee insights). Without that expansion, the system simply recycles what it already knows.

But creative rigor does not end there.

As soon as ideas move toward action, a different discipline is required. The temptation at this point is to test too many things at once, bundle multiple assumptions, or design experiments so complex that they cannot yield clear learning.

Rigor, in this context, means doing less, but doing it more precisely. It means isolating a single hypothesis, a specific belief about how the system might respond, and designing an experiment that can genuinely test it.

This is where clarity becomes essential. What exactly are we trying to learn? What assumption are we testing? What would count as meaningful feedback?

These questions are often left implicit. Bringing them into the open, in simple written form, changes the quality of the experiment. It allows others to engage with the thinking. It creates alignment around the action's purpose. And it ensures that whatever the outcome, something useful is generated. I call this practice "smart experimentation", and have developed tools and protocols so leaders can get it right.

In complex systems, experiments rarely “work” as originally imagined. That is not failure. It is information.

The discipline of creative rigor ensures that every experiment, whether it succeeds or not, contributes to a growing body of insight about the system. It connects action back to sensemaking, and sensemaking forward to more intelligent action.

Failing forward, then, is not only an emotional capability. It is a structural one.

It depends not just on staying open, but on knowing what each experiment is designed to reveal, so that every outcome, even an unexpected one, builds the system’s capacity to understand and respond more effectively.

For experimentation to be effective, it must be approached with discipline. Not every action qualifies as an experiment, and not every experiment produces useful learning.

3. From FIXED TO Emergent StrategIES

   
   

From Experimentation to Adaptation

As experiments accumulate, the system begins to reveal emerging patterns. These patterns do not provide a complete or final answer, but they do offer direction. Leaders can begin to see which pathways are opening up into opportunities for innovation and improvements, which constraints are real, and where further exploration is needed.

This is where adaptation comes in.

Rather than executing a fixed plan, the organization adjusts its direction in response to what it is learning. Some initiatives are scaled up. Others are refined or discontinued. Resources are reallocated. The system moves forward while remaining responsive to change.

This is not a lack of strategy. It is a strategy in motion. Direction and vision exist, but they are not rigid, ossified, or friable. Commitment is real, but it is not brittle and breakable.

Planning still happens, but it is continuously updated based on new information.

Adaptive Planning, emergent strategies

Planning does not disappear in this model. It evolves. Instead of being a one-time activity that produces a fixed roadmap every year or 5 years, planning becomes an ongoing process: a co-creative dialogue between agents in the complex system. It is informed by sensing and experimentation, and it changes as that understanding develops.

This is an emergent strategy. The organization has a sense of direction, but it remains open to revision. It commits resources, but in a way that preserves the ability to adjust. It moves forward with intent, but without assuming that the path is fully known.

This allows the system to remain aligned without becoming rigid, and to remain adaptive without becoming chaotic.

Conclusion: Leadership In Vuca++ Becomes Orchestration

This sequence—sense, experiment, learn, adapt—fundamentally changes the role of leadership. Leadership becomes less about deciding the answer and more about orchestrating the process and people through which answers emerge. It involves creating the conditions in which sensing is possible, experimentation is encouraged, and learning is integrated into action.

This requires a different kind of authority. Not the authority that comes from having the answer, but the authority that comes from holding the system in a way that allows it to function intelligently under pressure.

At the heart of this shift is a rebalancing of control. To lead through probing rather than predicting requires letting go of the illusion of absolute control and allowing other people, perspectives, and the system itself to have a voice in shaping direction. This can feel like a loss. In practice, it is an exchange.

In relinquishing rigid control, leaders gain something more valuable: greater accuracy, deeper insight, and more resilient outcomes. The posture shifts from imposing answers on the system to developing them with it, from trying to win through prediction to learning through interaction.

Leadership becomes less about controlling the future and more about co-creating it with the VUCA++ reality itself. And in complex environments, that shift—from control to co-creativity, from prediction to probing—is what ultimately separates those who struggle from those who succeed.

This requires leaders to maintain coherence without forcing closure, to support movement without collapsing exploration, and to stay present to the unfolding nature of the system even when it challenges their expectations.

In complex environments, the question is no longer what the plan is. It is what the system is showing us, and how we are responding to it. The organizations that succeed are not those that predict the future most accurately. They are those who remain open long enough to learn from it and disciplined enough to act on what they learn.

They sense more clearly. They experiment more intelligently. They adapt more effectively.

And in doing so, they move beyond the Intelligence Paradox.

Because leadership, in the end, is not about getting it right at the start. It is about staying in relationship with a reality that is still unfolding, and allowing understanding, direction, and action to evolve together.

About the author: leadership keynote speaker Nick Jankel

Nick Jankel is a globally recognized leadership keynote speaker, innovation strategist, and transformation expert with nearly 30 years of experience helping organizations navigate complexity, disruption, and the AI-driven future. A Cambridge-educated medic and pioneering futurist, he is the creator of the neuroscience-based Bio-Transformation® (BTX) methodology, as well as proprietary sense-making, experimentation, and adaptation tools used by leaders and teams worldwide.

Nick has worked with over 100 Fortune 500 companies and coached more than 100,000 leaders to become more adaptive, innovative, and effective in complex systems, helping them shift how they sense, think, act, and collaborate to unlock transformation and sustained performance.

FAQs: Leading In Complexity With Sensemaking, Experimentation & Adaptation

What does “probe over predict” mean in leadership?

Probe over predict means that in complex systems, leaders should test and explore the future through experiments rather than trying to predict it in advance. Insight emerges through interaction, not analysis alone.

Why is prediction unreliable in complex systems?

Complex systems are dynamic and constantly evolving. Variables interact in unpredictable ways, making long-term predictions unreliable and often misleading.

What is the difference between executing plans and experimentation?

Planning assumes the future can be mapped in advance. Experimentation accepts uncertainty and uses small, controlled actions to generate learning and guide decisions.

What is emergent strategy?

Emergent strategy is an adaptive approach where direction evolves based on real-time learning and feedback, rather than being fixed upfront.

Why is psychological safety important for experimentation?

Psychological safety allows people to take intelligent risks, share insights, and learn from failure. Without it, experimentation becomes superficial and innovation stalls.

What is “failing forward”?

Failing forward reframes failure as a source of learning. Experiments that don’t work still provide valuable insights that improve future decisions.

What is creative rigor in leadership?

Creative rigor is the ability to combine bold, imaginative thinking with disciplined experimentation, ensuring that ideas are both innovative and testable.