If you develop talent inside government, you know the gap. Competency models assume order; org charts assume authority flows in straight lines. But the real work — cross-agency coordination, policy implementation, workforce transformation — happens inside systems that are messy and impossible to fully control. Our training models assume stability in an environment that demands something else: the capacity to lead well inside instability.

Two Ideas Worth Knowing

Complex Adaptive Systems (CAS) thinking, drawing on scholars like John Holland, Margaret Wheatley, and Ilya Prigogine, describes how living systems generate order without anyone designing it. Agents are interdependent and responsive; the whole becomes more than the sum of its parts, and new conditions emerge unpredictably because human systems aren't engineered — they behave.

Complexity Leadership Theory (Uhl-Bien, Marion, Weick, Heifetz) asks a sharper question: what does effective leadership look like in non-linear, unpredictable systems? Not about adding control — tolerance for ambiguity, attention to informal networks, and adaptive capacity built through action, not planning alone.

Powerful theory, but talent development professionals need something practical to build capacity around. That's where our model comes in, and where the Habits of a Systems Thinker become essential.

Why the Habits Matter Here

The Habits of a Systems Thinker, developed by the Waters Center for Systems Thinking, are the dispositions that let leaders actually see a system's structure and patterns instead of reacting to what's in front of them. Without them, leaders default to treating disruption in isolation, chasing symptoms, and blaming people instead of structure. The Habits are what make a systems mindset practical — the muscle memory beneath the orientations below.

Five Orientations of Leadership for Emergence

Uncertainty.

Certainty is a leadership illusion; changing one relationship ripples through the whole system. The Habit of Seeing the Big Picture lets a leader expect that ripple and map it onto a vivid vision of the future.

Signals.

Important indicators are present long before a crisis, but we often dismiss quiet signals in favor of a comfortable status quo. The Habit of Identifying the Circular Nature of Feedback turns pattern-noticing into a practiced skill, so leaders recognize accumulations instead of dismissing it as noise.

Networks.

Real change moves through relationships, not org charts. Strong networks are held by shared purpose and view difference as a resource. This draws on Seeing how Structure Generates Behavior — leaders don't manage networks; they recognize and tend the ones already shaping the system.

Sense-Making.

Mental Models are the foundation of behavior within a system. An adept systems-leader weaves scattered information into a coherent picture that drives the mental models needed to enable the desired future of the system. 

Action & Learning.

The system acts whether or not the leader does. This applies the Habit of Considering Short- and Long-term Consequences alongside Successive Approximation: small, deliberate moves, checked against results, adjusted, repeated. We act to learn, and learn to act.

Where Talent Development Comes In

None of this is a competency to check off a list. It's a shift in how leaders — and the systems around them — make sense of change. The Habits of a Systems Thinker are the teachable building blocks that make the shift practical. Systems leadership is mission-critical for today’s leaders and requires intentional practice into how leaders sense, connect, narrate, and act every day.

Waters Center for Systems Thinking supports organizations thinking about what it takes to build this kind of leadership capacity. We work with government, private- and public-sector organizations to turn systems thinking into practical, sustained leadership development. Reach out to learn how we can help your leaders lead well inside complexity.

Here's a thought experiment: Imagine a new co-worker starts on your team. They're fast, remarkably well-read, and can produce a draft of almost anything in seconds. But they've never met your clients. They don't know your organization's history. They occasionally state things with total confidence that turn out to be wrong. And they have no stake in the outcome.

Would you hand them a complex project and use their first draft without question?

Of course not. You'd read it critically. You'd ask what they left out. You'd wonder whose perspective was missing. You'd think about what happens if this becomes the pattern — if over time, the quick draft becomes the finished product and no one notices the gradual shift.

Now extend the metaphor a little further. Imagine that same co-worker has been on your team for a year. They've made themselves indispensable. You've stopped reviewing their work quite as carefully. New team members who joined after them have started assuming that the way this co-worker does things is just the way things are done. Nobody remembers the decision to trust them — it just became the norm.

That thought experiment is exactly where we are with AI. Ethan Mollick of UPenn's Wharton School puts it plainly: "AI is not a tool that does what you tell it; it's more like a co-worker who requires clear direction but brings capabilities you don't have."

The question, then, isn't really whether to use AI. It's whether you have the thinking skills to work with it like a human — to be the kind of colleague who manages that co-worker well, rather than the one who delegates and disappears.

Why Technical Skills Aren't Enough

There's no shortage of guidance on how to prompt AI, which tools to use, or how to integrate AI workflows into your work. These are real and useful skills.

But they operate at what systems thinkers call the event level — the visible, moment-to-moment decisions. Should I use AI for this email? Did this summary capture what I needed? Is this output accurate enough?

What happens beneath those events — the patterns they create, the structures they reinforce, the mental models they reflect — is where the more consequential action lives. And most of us don't have a framework for seeing it.

Think about what your new co-worker can't tell you. They can't tell you that their summary of the strategy meeting left out the tension in the room between two senior leaders — because they weren't there for the last three years of context that gave the tension meaning. They can't tell you that their draft proposal is missing the organization's voice — because no one thought to teach them what that voice sounds like. They can produce; they cannot judge. That's your job. And it's a job that requires a very specific kind of thinking.

That's precisely what systems thinking provides — not just awareness that patterns exist, but a structured way to see them, name them, and interrogate what's driving them. Systems thinking enables you to look at AI use and ask: what productive habits are we building, and what unproductive ones are quietly taking root? What structures — the workflows, the norms, the unspoken rules about how we work — are being reinforced or reshaped by the choices we're making? And beneath those structures, what mental models are running the show? The Habits of a Systems Thinker give that inquiry even more traction, offering specific lenses for examining cause and effect, surfacing assumptions, and asking whose perspective might be missing. Together, they turn a vague sense that something is changing into a disciplined capacity to see what, why, and what to do about it.

We All Have Our Own Rhino Horns

Before we talk about what systems thinking asks of you, it's worth acknowledging something: we all approach AI differently, and all of those stances are valid.

Early adopters experiment and innovate. Mid-curve adopters watch what others do and repeat it. Cautious slow-movers bring skepticism that often turns out to be wisdom. None of these is inherently right or wrong — each reflects a mental model shaped by experience, role, comfort with uncertainty, and a dozen other factors.

We all have mental models like these — lenses that simultaneously enable and constrain how we see the world. Over time, those lenses develop into something more fixed. Call them rhino horns. A rhino's horn isn't a flaw — it's a natural outgrowth, shaped by experience and environment, useful in the right circumstances. But it also sits directly in the animal's line of sight, narrowing the field of vision in ways the rhino can't easily perceive. And here's the important part: the horn doesn't just block a portion of reality. It becomes part of reality. The rhino doesn't question whether it's there.

Our mental models work the same way. The assumption that efficiency is always the goal, that AI output is probably accurate enough, that reviewing AI work too carefully signals something negative about your relationship with technology — these aren't conscious choices. They're horns we've grown. And because they're ours, they feel like clear-eyed perception rather than a constrained view of it.

What makes this especially worth paying attention to right now is the pace of AI development. When technology moves faster than our understanding of it — and AI is doing exactly that — our horns don't stay the same size. They grow larger and thicker. The gap between what AI can do and what we actually understand about how it works, what it gets wrong, and what it quietly changes about us creates the ideal conditions for mental models to calcify. We fill uncertainty with assumption, and assumption, left unexamined, becomes conviction.

The point isn't to flatten those differences across your organization — each disposition genuinely brings value. The point is to make the thinking underneath your disposition more visible, so you can work with it intentionally rather than be quietly governed by it. That's where the Habits of a Systems Thinker come in.

The Habits of a Systems Thinker: A Menu, Not a Checklist

The Habits of a Systems Thinker are fourteen thinking dispositions developed from decades of research. They're not a to-do list. Think of them more like a menu — different situations call for different Habits, and the skill is knowing which ones to reach for.

When it comes to AI, four Habits are especially generative.

Considers an issue fully and resists the urge to come to a quick conclusion

Speed is one of AI's most seductive qualities. It produces an output in seconds, and there's a powerful pull toward accepting it and moving on. Our hypothetical co-worker is always ready with a draft — and when the whole point is speed, pausing to question it carefully can feel like you're working against the very thing you brought them on to do.

But considering an issue fully means pausing to ask: What's the short-term benefit here? What might happen long-term? Who else is affected by this output? What unintended consequences — good or bad — might emerge over time?

Consider a real scenario: a team member uses AI to draft their year-end review. The output is polished, hits the right structure, and saves forty-five minutes. What got skipped, though, was the most valuable part — the reflection. The slow, sometimes uncomfortable act of sitting with a year's worth of work and asking: what did I actually accomplish? What did I learn? Where do I want to grow? That process isn't administrative overhead. It's how people develop. When the AI drafts the review, the employee doesn't have to think carefully about their year — and so they don't. The manager, reading a clean and competent document, doesn't have to probe or push — and so they don't. What could have been a genuine moment of learning for both of them becomes exactly what everyone already complains these reviews are: a performance. Efficient, frictionless, and mostly hollow.

This Habit is the antidote to event-level thinking. It asks you to look at the whole picture before you accept what's on the surface.

Considers how mental models affect current reality and the future

Every person brings a set of assumptions to AI — about its accuracy, its limitations, its appropriate uses, and what it means to use it at all. Early adopters experiment readily. Cautious skeptics hold back. Mid-curve adopters watch and repeat what others do. None of these stances is inherently right or wrong. Each is driven by a mental model.

The Habit asks: What am I assuming about what this AI can or should do? How might those assumptions be shaping my decisions — and what if they're wrong?

Practicing this Habit means turning toward your own rhino horns — the assumptions so embedded in how you work that they no longer feel like assumptions at all. One of the most common is that if an AI output sounds authoritative, it probably is. Another is that speed is always better. A third is that reviewing AI work carefully signals distrust of technology rather than responsibility. These don't announce themselves as beliefs. They operate quietly, shaping decisions before conscious thinking even begins.

And when mental models align across a team or organization, something subtler happens: they become common sense. Nobody questions the assumption because everyone shares it. The unexamined belief doesn't feel like a belief — it feels like the obvious way things work. But when mental models are in conflict — say, one person prioritizes efficiency and another prioritizes accuracy — and that conflict goes unnamed, the two people rarely experience it as a difference in values. They experience it as a disagreement about the work itself. They argue about the output when what they're actually disagreeing about is how the output should be produced, and what it's for. Surfacing the mental model underneath doesn't just improve thinking — it often resolves friction that seemed intractable.

Changes perspectives to increase understanding

AI outputs are not neutral. They reflect the data they were trained on, the framing of your prompt, and a synthesis of perspectives that may not include the ones that matter most for your context. Whose voice is missing from this output? is one of the most important questions a human can bring to AI-generated work.

When we write — really write — we are constantly taking perspective. We think about what will land with this particular audience, what will spark their attention, what might ruffle feathers and whether that's worth it. We make judgment calls, rooted in intuition built from relationships and experience. We don't always get it right. But the attempt itself — the act of genuinely considering another person's reality before putting words on a page — is what gives writing its power to connect. AI doesn't attempt this. It approximates it. And approximation, however polished, is not the same thing as being seen.

In the customer service automation scenario, for instance, the AI response may be technically accurate and professionally worded — and still miss the person entirely. The long-time client who's frustrated doesn't need a correct answer. They need to feel heard first. That's a perspective call, and it requires a human to make it.

This Habit extends beyond reviewing output to the decision to use AI in the first place. How does offloading this communication look from a customer's perspective? From a new team member who's learning what good work sounds like by watching how it's done? From someone with less institutional power who relies on the texture of human communication to navigate the organization? These are not questions AI will raise. They are questions only a human thinks to ask.

Identifies the circular nature of complex cause and effect relationships

This is the feedback loop Habit — and it may be the one most consistently underpracticed when it comes to AI. Every tool we adopt shapes the way we work. And the way we work shapes what we expect. And what we expect shapes what we reach for. These loops don't announce themselves — they simply become, over time, the way things are done.

Consider what happens when a team member's AI-generated work consistently meets expectations. It gets accepted, distributed, built upon. Others begin to pattern their own work after it. What started as one person's output quietly becomes the organization's common sense — the unexamined standard against which new work is measured. Nobody decided this. The loop ran on its own. And that's precisely where it becomes problematic: when output is repeated without monitoring, the loop can carry an organization forward on assumptions that have quietly gone stale. Goals shift, strategy evolves, context changes — but the AI keeps producing what it was always asked to produce, and the organization keeps accepting it, until the day someone looks up and realizes the work no longer reflects where they're trying to go.

The question isn't just does this AI use serve me today? It's what am I creating over time by making this a habit? Reinforcing loops can be virtuous — efficiency compounds, quality improves, you get faster at prompting meaningfully. They can also erode things quietly: skill development, critical engagement, institutional memory. The loop doesn't care which direction it runs. That's the human's job to notice.

The Iceberg: Seeing What You Can't See

Most of us live at the waterline. Something happens — an AI output lands in our inbox, a summary gets distributed, a draft gets approved — and we respond to it and move on. The event is visible. Everything beneath it isn't.

The Iceberg model gives us a structure for going deeper. Below every event are the patterns that produced it. Below the patterns are the structures — the workflows, norms, incentives, and design choices — that made those patterns possible. And beneath the structures are the mental models: the assumptions and beliefs, often unexamined, that shaped how the whole system was built in the first place. Our rhino horns live here.

Take a team that starts using AI to generate summaries of their strategy meetings and distribute action items automatically. At the event level, this is a straightforward efficiency gain. Notes get taken faster. Action items are clear and shared immediately. That's real value, and it's worth naming — the goal is never to dismiss what AI does well.

But the patterns that form over time tell a more complicated story. People take fewer personal notes. Rich strategic discussions get distilled to bullet points. Newer team members begin relying on summaries rather than attending meetings. And yet — one pattern that surfaced in a workshop on this exact scenario — people actually paid more attention to each other on camera, because the cognitive task of note-taking was offloaded. The Iceberg holds both. Patterns are rarely all one thing.

The structures underneath — AI trained to capture decisions and tasks, not strategic reasoning; no human review step built into the workflow; the time pressure that made instant documentation feel like an obvious win — don't shift on their own. They compound quietly, reinforcing the patterns above them.

And the mental models at the base? "The summary IS the meeting." "Good notes equal decisions plus action items." "Faster documentation is always better." These weren't chosen consciously. They were already there, and the tool confirmed them.

None of this makes AI meeting summaries a bad idea. What it does is make the full picture visible. And visibility changes what's possible. Maybe you augment the AI summary with a brief human synthesis. Maybe you build in a review step. Maybe you recognize that the freed-up cognitive space is actually an invitation to be more present in the conversation itself — not less. The Iceberg doesn't tell you what to do. It tells you what to look at.

Three Moments for Systems Thinking With AI

The Habits and the Iceberg aren't just retrospective tools. They apply across the full arc of AI use — and the timing matters.

Before you use it, the Habits help you interrogate the decision itself: Am I bringing an unconsidered assumption to this? Who else will be affected by the output I'm about to produce? Is efficiency the right metric here, or is something else at stake?

While reviewing the output, the Habits give you the questions AI cannot generate for itself: Whose perspective is missing? What's the long view here? What am I not seeing? This is where the co-worker analogy is most useful — you wouldn't accept a first draft without reading it, and reading it well requires knowing what you're looking for.

After repeated use, the Iceberg helps you surface what's accumulating in the system: Is this pattern creating what we want? What structures have formed? Are the mental models beneath our AI use aligned with the organization we're trying to build?

The sequence is worth practicing deliberately. Pre-use, in-use, post-use. Before, during, after. It's not a long process — it's a thinking discipline.

What You Bring That AI Doesn't

Peter Senge, one of the foundational voices in systems thinking, wrote: "The organizations that will truly excel in the future will be the organizations that discover how to tap people's commitment and capacity to learn at all levels."

AI brings capabilities that most of us don't have. Pattern recognition at scale. Synthesis across enormous bodies of text. Speed that no human can match. Our co-worker is genuinely talented. These are real capabilities, and they're transformative.

But our co-worker has never felt what it costs an organization to lose institutional memory. They've never sat with a customer through a hard conversation and sensed when the approved script wasn't going to land. They've never had a flash of intuition that the strategy everyone agreed on was somehow wrong. They produce, but they don't care. They analyze, but they don't commit. And they cannot be held responsible. When something goes wrong — when the output misleads, when the summary omits what mattered most, when the pattern quietly drifts from the strategy — there is no one on the other side of that to answer for it. Responsibility is a human capacity, and it requires a human to carry it.

Creativity, passion, the capacity to inspire, and the willingness to ask hard questions about what we're building — these remain deeply human. The question "What are we creating over time?" is one that only a human can hold with real weight.

Organizations that develop the thinking capacity to work alongside AI thoughtfully, not just efficiently, will be the ones that differentiate. And that capacity isn't built by learning the best prompts. It's built by developing thinkers.

Developing This Practice

The Habits of a Systems Thinker and the Iceberg model aren't frameworks you absorb once and apply perfectly. They're practiced capacities — ways of thinking that develop over time, with use, and with guidance. Joi Ito of the MIT Media Lab captures the shift well: "Education in the past was about teaching people what to know. Education in the future is about teaching people what to ask."

The Waters Center for Systems Thinking has been developing systems thinkers for more than thirty-five years, across organizations, schools, and communities. If this post surfaces something you want to develop — for yourself, your team, or your organization — we'd welcome the conversation.

The questions AI can't ask are the ones that most need human attention right now. We can help you build the capacity to ask them.

The Waters Center for Systems Thinking supports individuals and organizations in developing the thinking skills that lead to deeper understanding and more effective action.

A decade ago, hospitals began to recognize that cigarette smoking by staff and patients contradicted their mission of promoting health. Programs were created to educate, encourage, and incentivize people to stop smoking. These programs helped some individuals—but the broader problem remained. Real change only occurred when hospital boards and policymakers decided that smoking would no longer be permitted. When policy changed, behavior followed, and the culture shifted.

Policy is where long-term, systemic change happens. It transforms regulations, laws, and even social norms—though the process often takes time. Effective policy work includes five components: research, outreach, education, advocacy, and policy change. Policy changes systems.

Programs, by contrast, are often more visible and appealing because they produce faster, more tangible results. But their impact is typically short-term and limited to the individual level. Programs can also drain available resources and attention, sometimes distracting from deeper structural solutions. Consider the difference between distributing food boxes versus advocating to increase SNAP benefits or raising the minimum wage. The first alleviates immediate need; the second addresses the conditions that created hunger in the first place.

A useful metaphor: If you find a few dead fish in your lake, you might study the fish. If half the fish are dead, you study the lake. If fish in five neighboring lakes are dying, you examine the groundwater. Policy and systems change are about fixing the groundwater.

Strategic Policy and Advocacy Projects

Strategic Policy and Advocacy Projects aim to align people, data, and strategy to drive systemic change. These projects:

• Generate data (quantitative and qualitative) to surface and test assumptions.

• Inform new laws, regulations, and policies.

• Identify leverage points and track shifts within systems over time.

• Engage people with lived experience in shaping and advancing policy.

• Focus on effective implementation of new or revised policies.

Examples of Policy in Action

1. PEEPS: Pima Early Education Program Scholarships (Arizona) A decade-long effort to increase access to quality early education led to a $10 million Pima County investment, alongside contributions from local governments. Nearly 2,000 parents annually—earning under 300% of the poverty level—can now enroll their three- to five-year-olds in quality preschool. The program not only expanded access but also strengthened the childcare sector, improved working conditions for educators, and elevated cultural responsiveness in early education. None of this came from a single “program” alone—it resulted from persistent policy advocacy and coalition-building.

2. Santa Ana, California: Environmental Justice through Policy Change 

Yvette Cabrera, a senior reporter at the Center for Public Integrity, an investigative news nonprofit, where she covers inequality in economic and social well-being with a focus on environmental justice issues gives us another example of the need to emphasize policy over program.  She describes it this way:

I began reporting on soil lead contamination in Santa Ana in 2015 after I had an epiphany while reporting a story about California probation agencies that were turning minors in juvenile detention over to immigration authorities. As I interviewed mothers across Southern California, documenting the history of how their sons ended up in juvenile hall, I noticed these mothers had similar stories — their sons had trouble learning in school, couldn’t sit still or focus, and had been diagnosed with attention deficit hyperactivity disorder.  At the same time, I serendipitously came across a magazine article featuring a hypothesis about the link between lead and crime — how the rise and fall of leaded gasoline in cars may have contributed to the rise and fall of violent crime in America — and, intriguingly for my work, a link between lead exposure and ADHD.

I came to the realization that I was only covering half the story. I was writing about the environment of these young boys in terms of the violence, poverty, and high police presence in their neighborhoods, but I wasn’t considering how their physical environment — neighborhoods next to freeways, surrounded by polluting industries — might be contributing to their behavioral issues. Many of the minors in Orange County’s juvenile hall are from neighborhoods in Santa Ana where young Latinos face many of these issues.

 I wondered, “Is there a way to address this issue by digging deeper for a root cause?” I discovered that the number of children in Santa Ana with elevated blood lead levels was much higher than in any other city in the county. I wanted to know why.  In 2015, I tested the soil to see if the ground on which the city’s children were playing was a potential source of lead exposure… …This was much more than just a story about soil contamination. As I listened to the stories of the residents, I saw how families in barrios like Logan cared deeply about providing their children with a healthy place to live. At a time when Americans are more acutely aware of the impacts of structural and systemic inequities, I saw the importance of showing how historic patterns of permitting, land-use decisions, zoning, and regulatory policies continue to determine who in America gets to breathe healthy air and live in a safe environment.

Cabrera’s investigation into root causes created these changes to the system. Her investigation exposed systemic inequities in zoning, permitting, and land use that perpetuated environmental hazards in low-income Latino neighborhoods. Her work catalyzed major policy shifts:

• A community-led coalition, ¡Plo-NO! Santa Ana!, conducted independent soil testing confirming widespread contamination.

• The city council approved a comprehensive general plan update to address lead hazards (2022).

• UC Irvine received a $2.7 million federal grant to study lead exposure impacts on children’s health and learning.

• In 2024, Santa Ana imposed a moratorium on new industrial development near affected neighborhoods to investigate long-term health impacts.

This was not a “program.” It was systemic reform—policy-level change rooted in community advocacy and data.

3. Arizona Grandparents Ambassadors: Policy for Kinship Families When grandparents raising grandchildren realized they received fewer supports than licensed foster parents, they organized. With support from the Southwest Center for Economic Integrity, they trained, advocated, and pushed for legislative action. Twelve years later, their persistence led to:

• Monthly stipends for kinship caregivers,

• Expansion of the Kinship Family Fund,

• Automatic eligibility for TANF child-only benefits,

• Official recognition of September as Kinship Family Month

• Streamlined licensing and training processes.

As Kelly Griffith, CEI’s Executive Director, put it: “Policy work takes time, and in the end, it is still about relationships.”

What It Takes to Make Policy Work

• Cultivate relationships with decision-makers, nonprofits, business and faith groups, and those most affected.

• Build coalitions and align interests across sectors.

• Support advocates through training and strategic coordination.

• Stay adaptable to shifting political and economic contexts.

• Keep focus on the root causes, even amid complexity and setbacks.

Policy work is not quick, easy, or glamorous. It requires patience, long-term vision, and the ability to navigate complexity. But when successful, policy creates the conditions where programs can thrive sustainably—and where people no longer need programs at all.

A food box feeds a family for a day; a fair wage feeds generations. A preschool scholarship opens one classroom; public investment opens a pathway for thousands. When we choose policy over program, we choose solutions that endure—solutions that shift the “groundwater” of our communities.  Real change happens not when we add another program to the mix, but when we reshape the system so that the problem no longer exists. Policy is the most powerful program of all.

What If…

Many years ago, as the principal of a large elementary school, I walked into a kindergarten classroom. A young learner looked up from her center work and said with authority, “Go put on one of those aprons because I need some help and center helpers wear those aprons.” Needless to say, I stopped what I was doing and put on a center apron. While I no longer recall the precise question this eager learner had, I vividly remember the confidence with which she spoke, her engagement in her task, and—perhaps most of all—the personal responsibility she assumed for her own learning.

This kindergarten class was a thinking classroom. Evidence of student thinking was everywhere: children engaged in purposeful conversation with each other and with adults. The teachers who had designed this learning space trusted in their students’ ability to make meaningful connections that led to deeper learning. These students got a solid start as independent, self-directed learners. As their principal, I later watched many of them progress through elementary school confident in their ability to make meaning from their reading, eager to communicate their ideas in writing, and ready to apply mathematical thinking to real contexts. What if all classrooms were thinking classrooms?

The 2024 NAEP results, released earlier this month, reveal troubling trends: just over a third of 12th graders are proficient in reading, fewer than one in four demonstrate strong math skills, and less than a third of 8th graders reach proficiency in science. In some subjects, achievement has fallen back to levels not seen in three decades. Not a single state registered gains in 4th or 8th grade reading, and national reading scores for seniors declined three points since 2019. Often described as the “Nation’s Report Card,” NAEP underscores a critical question: how well is the U.S. education system preparing young people with the knowledge and thinking skills they need for college, careers, and civic life in a rapidly changing world? The trends suggest we are falling short of meeting the needs of students in our school.  But what if…?

What if we abandoned the conventional ways we have tried to solve this problem? What if we stopped relying on commercially produced programs that are big on promises but low on results? What if we listened to students and teachers, and created thinking classrooms where learners engage in self-directed, challenging, and relevant experiences that lead to deep learning?

This would be a radically different approach from what we have been doing—rigidly imposing prescriptive programs on schools without considering the context or the needs of the learners. It would allow teachers to diagnose students’ level of learning and design experiences that move them forward on a continuum of growth. Long before I stepped into that kindergarten classroom, I imagined what such teaching and learning could be. It was only when I became immersed in the pedagogy of systems thinking that I found the tools and capacities to translate those ideas into practice.

The Waters Center has spent more than 30 years engaged in research and development on how to create thinking classrooms. A systems thinking approach to learning offers concrete strategies and methods to help students learn how to think. In these classrooms, learning is not about rote memorization of information but about thinking, problem-solving, and applying knowledge in ways that matter for work, civic life, and lifelong learning. Better test scores become a natural byproduct—not because the focus is on tests, but because students are equipped to reason, question, and apply their learning.

In a thinking classroom, students are encouraged to ask questions, not just answer them. Mistakes are welcomed as part of learning. Student voice is central: learners regularly explain and justify their ideas. I once received a call from a parent whose child had moved on to the next school. She shared that her child was struggling because the teacher no longer asked what he was thinking. This was not a case of a student being disrespectful or disregarding authority, but of a child who had grown accustomed to his ideas being valued as part of the teaching learning process.

A systems thinking classroom also includes specific teacher behaviors that promote thinking. Teachers regularly use open-ended prompts like, “How do you know?” or “What caused you to ask that question?” Students engage in purposeful conversations in pairs and small groups, focused on reasoning. Teachers introduce habits of thinking that allow students to be metacognitive about their own learning. Students reflect on how they approached tasks, not just on the outcomes. Teachers model thinking aloud, demonstrating how to test assumptions and consider alternative perspectives.

Instruction in a thinking classroom is not confined to predetermined lessons or scripted units. Instead, teachers skillfully incorporate student ideas, questions, and experiences into the learning process. They act as facilitators, creating conditions for exploration and discovery. Here, the emphasis shifts from content coverage to cultivating thinking itself. After all, knowledge is expanding so rapidly that trying to teach all the content will always leave us behind—but teaching students how to think will prepare them to keep up.

The environment of a thinking classroom reflects this philosophy. Walls are filled with student work, maps, questions, and evidence of learning. Spaces are organized to support collaboration and independence, stocked with books, manipulatives, and materials that allow students to explore and demonstrate understanding.

Finally, the work students are asked to do reflects a deep commitment to thinking. Assignments require reasoning, analysis, and synthesis—not just recall. Tasks are designed to allow for multiple approaches and solutions, often tied to authentic, real-world contexts. Subjects are integrated when appropriate, and performance-based tasks are common. Teachers assess both the product and the process, providing feedback that highlights quality of reasoning, not just correctness. Peer and self-assessment are routine.

Perhaps before we can answer the question, “What if,?”  we need to ask “How?”  How can we ask over burdened schools, facing severe teacher shortages, increased public scrutiny and decreasing resources to adopt  significant changes to their programming.  While teaching students how to think may seem like a daunting task, our years of research and development in this area have produced some very specific principles for making a transition to a systems thinking focused school quite workable.

A change in approach starts with belief.  First a belief that teachers are capable of the kind of complex thought needed to truly teach students.  We can’t expect teachers to demonstrate a true trust in their learning when they have ample evidence that they are trusted to make important instructional decisions. Having observed educators in thousands of schools, we know that when teachers, of any age, trust their learners to make meaningful, relevant connections that learning increases.  The best way to enable teachers to demonstrate that trust is by trusting them.

The second key to this kind of radical transformation in education is to recognize that deep thinking and learning can be part of any curriculum.  Teaching thinking skills and Habits across disciplines allow students to learn materials and transfer that learning to a variety of content areas.  Transfer is essential to learning. Thinking strategies transfer between subjects.  When subjects become more integrated there are more opportunities for skill transfer.  

The other important part of  how to develop thinking classrooms is that when applying a thinning pedagogy, doing any part of it can help accelerate student learning.  Systems thinking has a variety of entry points for teachers and learners.  It’s not necessary to master all the nuances.  Using a behavior-over-tiime graph to increase retelling scores will enhance student comprehension.  Applying a few Habits to help students be more metacognitive about their problem solving will make a difference.  Using a graph to examine the level of difficulty of various steps in an academic task, will make a difference.  Clearly the more embedded these routines become, the more impact they will have, but student engagement has been shown to increase with even the smallest infusion of a systems thinking pedagogy to a regular lesson.  In other words, a simple introductory training, an activity embedded in a written curriculum or even a one-hour PD with appropriate follow up will equip teachers with skills needed to enhance student thinking and hence student achievement.

The latest NAEP scores show us where we are. But imagining what could be is the first step toward change. If every classroom embraced the principles of systems thinking and fostered environments where students learn how to think, not just what to remember, we would see not only stronger test scores, but also young people better prepared for the complexity of the world they are inheriting. The question is not whether we can afford to make this shift—it’s whether we can afford not to.

“We can’t impose our will on a system.  We can listen to what the system tells us, and discover how its properties and our values can work together to bring forth something much better than could ever be produced by our will alone.”

Donella Meadows, “Dancing with System”

At the end of 2020 in an attempt to listen to the systems in which we were living, the Waters Center team sought new ways to achieve its mission of building systems thinking capacity across generations and geographies.  Our team determined it was time to launch a credential program for people passionate about furthering their own systems thinking learning and also nurturing their ability to build the capacity of others to navigate complexity.  

On June 17, 2021, we launched our first cohort of the Advanced Facilitator Program,  committed to delivering benefit to each person who chose to become a co-learner with us. From the first cohort meeting we began to experience the power of bringing people together from multiple sectors, countries and career paths, who were committed to using systems thinking tools, Habits and concepts in their life and work.  The AFC Community of Practice quickly became a key strategy in our mission to make the world a better place  

A community of practice (CoP) is defined as a group of people who share a passion for learning and engage in regular interaction to deepen their expertise in that area. They learn from each other, share experiences, and develop a shared repertoire of resources and practices.  

As of May 2025, that initial cohort of 26 people grew to a community of practice of 150 people representing 15 countries.  The contexts in which they seek to apply systems thinking are diverse.  There are educators, executives, lawyers, physicians, and entrepreneurs, representing public, private, for-profit, non-profit sectors.  Together they embody the definition of a community of practice:

Virtual learning made this exquisite diversity of background and experience possible among the cohort.  Nevertheless, the desire for in person connection led to the first AFC reunion.  So on June 23, 2025, 50 members representing all nine cohorts of learners came together in person to learn from each other, share experiences and deepen their expertise.  The three days were filled with conversation, learning and laughter.  We were able to engage in kinesthetic learning experiences  not possible online.  People met in groups to discuss points of commonality and stretched themselves to learn about how this work can be applied in different fields. This is a passionate group with deep expertise, a true community of practice.

One participant described the experience like this. “The depth of expertise in the group was amazing.  To witness all the people applying Systems Thinking in so many different, complex ways. Each person I connected with now feels like a treasure to me!”

The level of conversation and learning was incredible. The days we spent together were filled with synergistic conversation, deep learning and thoughtful reflection.  New ideas were born, concepts clarified and personal relationships, many of which will be lifelong friendships, were strengthened. 

So what is the role of a community of practice in learning about systems thinking?  There are plenty of great resources in print and online that capture the basic principles of systems thinking and even the deep wisdom that a system thinking lens brings to the world.  Many of us have taken courses, attended lectures, read journal articles and even conducted research into the theories, practices and applications of systems thinking.  Yet it seems there is something different about creating a community that commits to engaging regularly for an extended period of time and learning from one another.

There is support that comes from regular engagement with other people.  CoPs create an exchange of ideas that reinforce the understanding that there are multiple ways to see the exact same thing.  

Systems thinking is more than a trendy idea, a set of charts and graphs, or a series of steps that can be utilized to produce a desired outcome.  It isn’t a program.  It isn’t a formula.  It is a nuanced practice that helps us deal with complexity. 

In describing the reunion another participant described the role of the CoP this way.  “Systems thinkers have ‘capacity for complexity.’ Each person who shared out, expressed complex intricacies that were not simple insights, but rich and multifaceted. As I listened to ‘advanced’ systems thinkers in a world of ‘simple’ answers to complex problems, I was struck with how the group shared very deep and complex answers. I think it is interesting as you listen to folks who are on the more experienced end of the ST scale, you hear more and more complexity.”

The capacity for dealing with complexity is more than knowing.  It includes tolerating uncertainty, seeing contradictions and sometimes admitting that your original assumptions were wrong.  Capacity for complexity grows with practice.  Like a muscle it is strengthened through practice, discomfort and the art of listening deeply.  Dealing with complexity shuns reductionism.  Where some leaders may want a checklist, a systems thinker might want a map or a story.

When was the last time you and your community sat with a question long enough to let the complexity grow?  How willing are you to not know the answers?  Learning to be a systems thinker requires letting go of the knowns.  Accepting that there is more to learn and being willing to connect with a trusted community that can help navigate the new learning is essential for going beyond a knowledge about systems thinking  and instead engaging in the practice of systems thinking.

Our AFC community of practice is about developing a shared repertoire of resources and practices that will inform a growing body of work about systems thinking.  These credentialed facilitators serve as a model for the importance of community in learning. They will continue  to practice systems thinking; surfaces and tests their mental models; consider issues fully; iterate ideas;  seek a more nuanced view of the system; focus on systems structure;  use feedback to tell the story of the system.  They will intentionally spend  time together and support one another in learning.   They are a community of practice that shares a deep purpose–to make the world a better place.  This community of practice is inclusive of individuals who were not able to be part of this in person gathering, but participate in this cohort program and share in a commitment to continue to passionately engage in learning.  

We are filled with gratitude for those 50 individuals who were able to join in person, some traveling thousands of miles, to build the body of systems thinking knowledge and to model what it means to be a community of learners.  Together we will work to bring forth a vision of our systems much deeper than could ever be produced alone.

Periods of uncertainty test the resolve, adaptability, and creativity of leaders across every sector. While it's natural for organizations to focus on stability and continuity during turbulent times, systems thinking offers a valuable perspective that can help leaders not only survive uncertainty but use it as an opportunity to evolve. Systems thinking is a way of understanding complex problems by looking at the whole system rather than isolated parts. It involves identifying patterns, recognizing relationships and interdependencies, and being attentive to feedback loops that influence behavior over time. For example, rather than simply cutting a program that appears underperforming, systems thinking would ask how that program connects with other parts of the organization and whether its challenges stem from issues elsewhere in the system. Additionally, a systems thinker might ask what will happen to the other elements of the system if one is downsized or removed.

In moments of uncertainty, when it feels safer to narrow our focus and conserve resources, the impulse is often to shelter and hunker down. This is a natural extension of the survival strategies organizations employ. Yet, as we've seen, systems thinking challenges this instinct. It asks us not only to respond to the present but to actively engage with the system as a whole—to understand its dynamics, tensions, and leverage points. Rather than pulling inward, systems thinking encourages us to lean into complexity with curiosity and intentionality. It enables us to:

• Understand the current state of our systems more fully
• Clarify the future we desire to create
• Think holistically and relationally about how to navigate toward that future

In short, we can approach our challenges and opportunities in a more reliable and productive manner, even when the environment feels anything but stable.

The Pull of Reductionism

Grounded in our reflex for fight-or-flight, when stress levels rise and the hormones that correspond with stress increase in our bodily systems, we tend to explain that we feel threatened. This physiological and psychological state often drives us to reduce complexity wherever we can. It encourages us to make quick judgments, focus narrowly, and seek control over what feels unpredictable or overwhelming. When we feel threatened, it's natural to seek out what feels discrete and manageable. This tendency is rooted in reductionism—a way of thinking that breaks down complex issues into smaller, individual parts in an attempt to simplify and control them. While reductionism can provide clarity and immediate focus, it often comes at the expense of understanding the system as a whole.

In operational terms, this mindset often shows up in specific organizational responses:

• Cutting budgets
• Pausing investments
• Doubling down on outputs and efficiencies

These responses are not inherently wrong. They are rational and often effective short-term strategies. After all, the goal of any organization in crisis is survival. These moves align with that goal, especially when quick stabilization is needed to avoid collapse. However, relying solely on these actions can inadvertently suppress innovation, learning, and proactive exploration. Without a complementary approach that accounts for long-term impact and interconnected consequences, these responses can entrench reactive cycles and limit an organization’s ability to adapt once the crisis has passed.

The trouble with this course of action is that it anchors us to the past and the immediate present, making it difficult to envision or prepare for what's ahead. Typically, the strategic plan is put on hold, and the energy once directed toward innovation and growth is diverted to maintaining operations at a minimal level. Aspirational goals that once motivated teams are replaced by metrics aimed at sustaining only what is deemed essential. This shift often limits creativity, reduces morale, and narrows the organization’s capacity to adapt. The future becomes an afterthought—a luxury that will be addressed only when resources feel more certain and the environment more predictable. 

Leadership for Survival

When reductionist responses dominate, leadership often takes on a survival-oriented posture. Leadership in survival mode is about managing risk, reducing exposure, and making sure the organization lives to fight another day. This type of leadership often requires tough calls, clear communication, and the shelving of long-term goals in favor of near-term viability.

It is difficult. It is often necessary. And it is not inherently flawed.

Yet it can create a pattern where learning halts, vision narrows, and the organization loses touch with its future orientation. When leaders are focused solely on navigating immediate threats, they may deprioritize experimentation, reflection, and strategic foresight. Over time, this can lead to organizational rigidity, where the ability to anticipate or respond to emerging trends is diminished. Without an intentional effort to reconnect with the future, the organization risks becoming trapped in a cycle of reacting rather than evolving.

Leadership for Tomorrow

If we accept that survival leadership can be a default in times of uncertainty, we must also ask: How do we escape a focus on survival in favor of leadership for tomorrow? 

While volatility may persist, leaders must find ways to move from merely surviving to evolving, even amid ongoing uncertainty. This requires more than returning to business as usual. In fact, 'business as usual' during times of volatility often reinforces a survival mindset—focused on maintaining the status quo, minimizing risk, and preserving limited resources. The unusual leader is the one who can hold the tension between survival and growth, balancing stability with learning, innovation, adaptability, and a forward-looking perspective. It demands a deliberate shift toward curiosity, reflection, and long-term vision—qualities that help organizations not just withstand uncertainty, but grow through it.

Here is where systems thinking proves invaluable. Leadership for tomorrow requires a mindset that looks beyond the present moment into the emerging future. It demands that leaders understand the complex relationships and interdependencies that have created current conditions and anticipate how those relationships might evolve. Crucially, it also requires recognizing that organizational learning is not a solitary activity—it is collective and systemic. When one person learns, that insight can ripple through the system, strengthening the whole. This shared learning becomes the propellant for resilience and relevance in the future. Moreover, while goals focused on survival may generate short-term commitment and compliance, the work of growth, innovation, and transformation is a lifelong endeavor—one that calls for courage, collaboration, and vision.

Systems thinking does not replace other leadership theories or frameworks; it enhances them. It provides a way of thinking that:

• Recognizes patterns and interdependencies

• Encourages feedback and reflection

• Bridges current realities with future aspirations

These systems thinking principles can be further understood through key Habits of a Systems Thinker developed by The Waters Center for Systems Thinking:

• Systems thinking helps leaders see beyond isolated events to the broader patterns and connections that shape organizational behavior over time. For example, a drop in employee engagement might not stem from a single event but from ongoing misalignment between workload and recognition. This perspective aligns with the habit to Make Meaningful Connections Within and Between Systems, which emphasizes seeing how things influence one another within a whole.
• Instead of assuming linear cause-and-effect solutions, systems thinkers pay attention to feedback loops that reinforce or balance behaviors within the system. For instance, a policy change intended to boost productivity may unintentionally lead to burnout, which in turn reduces productivity—a reinforcing feedback loop. This reflects the habit Identify the Circular Nature of Complex Cause and Effect Relationships, which encourages leaders to consider how actions create ripple effects across time.
• Systems thinkers also understand that the structures within a system often determine the behaviors and outcomes it produces. A leader committed to long-term innovation must consider whether current policies, resources, and norms support or inhibit creative problem-solving. This links to the habit Consider How Mental Models and Structures Generate Behavior, which calls for reflection on the deeper forces that shape what people do.

Together, these ways of thinking help leaders create organizations that adapt, learn, and thrive—no matter the circumstances.

In doing so, systems thinking provides leaders with a dependable lens to navigate ambiguity and unpredictability. It supports leadership that is not only resilient in moments of crisis, but also capable of guiding organizations through periods of recovery, adaptation, and reinvention. Importantly, engaging with systems thinking as a core aspect of leadership development is not a critique of a leader's intellect or capability. Rather, it is an invitation to expand one’s thinking—to reframe challenges, surface new possibilities, and ask different questions. Systems thinking enriches the decision-making process by fostering a deeper awareness of relationships, structures, and dynamics that shape outcomes. This mindset cultivates a future-oriented leadership capacity that is essential for sustained growth and transformation. 

In uncertain times, we don’t need to choose between managing today and envisioning tomorrow. Systems thinking shows us how to do both—how to see the whole, move with purpose, and lead with a mindset of evolution rather than mere preservation.

Why use systems thinking?  Oh how I wish I had the proverbial nickel for every time I have been asked that question over the last twenty years.  It is a good question and there are many great answers:  It is a proven, researched method for helping individuals and organizations achieve their desired results.  It helps surface multiple points of view to determine how those mental models will impact the outcome of decisions. Fundamentally systems thinking offers a lens to see how a system behaves so that we can begin to identify ways to make changes and achieve something new.

Recently I was at a convening where the speaker asked a different question.  She said, “why don’t we use systems thinking?  What are the barriers that keep us from applying these proven methods consistently?  As is the barrier with so many things we want to do,  almost all of the responses had some connection to a lack of time:  Systems thinking takes a lot of time to learn.  It takes more time to assemble people to consider their varying perspectives. We know what needs to be done, so why not just do it.  The clear consensus as to why not systems thinking:  a lack of time.

All of these references to time made me think about the Habit, a systems thinker considers an issue fully and resists the urge to come to a quick conclusion.  Teaching that Habit is filled with discussion about those times when a quick decision is necessary and appropriate.  Clearly there is no consideration needed when preventing a toddler from wandering into traffic or evacuating a burning building. Further exploration of this Habit reveals that too often we take our most important decisions and prioritize efficiency over thoughtfulness. As I walked about that meeting room and read the reasons why we don’t use systems thinking, I was struck by the idea that we are being duped by the “myth of efficiency.”

Myth is defined as “a widely held, but false belief or idea.”  Efficiency is “the ability to achieve an end goal with little or no waste, effort, or energy.”  Perhaps we are sacrificing our opportunities to create those desired results based upon the false belief that we can do so with little to no waste, effort or energy.  I am not suggesting that we intentionally make poor decisions because we don't want to put forth effort, but rather that we are caught up in a time where the value of efficiency takes precedence over the need for thoughtful consideration, aided by the proven tools and Habits of systems thinking.

To varying degrees in varying cultures we like fast:  fast food, fast tech, fast cars.  Drive throughs and microwaves serve up meals in minutes.  Faster computers mean more information at our fingertips without the need to head out to the library for a few hours of research. Fast cars, while fun, represent a mechanization that has moved us from a three mile an hour walking pace to a  go-anywhere-at-any-time pace. Mechanization has changed food production from small farms to large-scale, fully automated production operations.  None of these things are inherently bad or evil, but they each fuel our myth of efficiency.  They reinforce the belief that faster is better. 

Thinking about food and specifically bread, I think there are some lessons we can learn about why efficiency really is a myth.  Take a minute to conjure in your mind the image of a freshly baked loaf of sourdough bread, the taste, the smell, the texture with a nice golden crust on the outside and a soft chewy inside.  Solidify that image.  Having recently begun my own personal experiment with sourdough starter, I can assure you that a perfect loaf of bread is not produced quickly nor efficiently. One of my first realizations was that part of the process of achieving a really healthy sourdough starter is that you have to discard part of the starter daily.  Having been taught, “waste not want not” I tried several ways to skip that step, which one morning resulted in a kitchen counter covered with mounds of sticky, smelly starter.  Enough about my bread making learning curve, the point is that working toward a desired result can be worth the extra time.  Sometimes we have to let go of ideas, just like I need to put some starter in the trash.  One of my favorite quotes from our founder Jim Waters is “never fall in love with your first best idea.” I must confess I am often enamored with a new idea that comes to me early in the morning or perhaps in the shower.  I can get a little giddy when I think of an ideal solution, a new way to approach a conflict  or decide it is time to try something new (yes like baking my own bread.)  Sometimes those ideas work out great, but often when I venture off into that new idea without due consideration, I end up causing undesirable, unintended consequences.  Or sometimes I am unsuccessful at something that I could have done well because I didn’t take the time necessary to consider the bigger picture.

Another axiom I have heard often during my work in systems thinking is “slower is faster.” It is definitely true in learning.  When we take time to go a bit more deeply, to establish a foundation, we can then become increasingly proficient.  When young children have a well developed sense of story, they can master the intricacies of written language.  As I learn to “read” my sourdough starter, I am confident I will someday achieve that perfect loaf of bread.  As we seek to develop our systems thinking expertise, the more thoroughly we understand the basic tools, Habits and principles, the more readily we are able to  apply systems thinking in multiple contexts and for multiple purposes. An initial investment of time and effort up front, can lead to greater efficiency down the road.

Applying a systems thinking approach to complex problems will require some time, energy and effort.  Yet a strong case can be made that that is not a waste, but rather a means to get to a better outcome.  And perhaps with practice that better outcome will eventually come more quickly, with greater efficiency.  Madeline Hunter, educational theorist, said, “practice doesn’t make perfect, practice makes permanent.  Systems are complex and solving problems in a complex world is difficult, but with appropriate time, energy and effort, or practice, systems thinking can become part of the way we approach those problems.  We will more often consider multiple perspectives, recognize conflicting beliefs and arrive at high leverage solutions.  System thinking is an investment which nets a big return.  It can indeed make the world a better place, but it will require us not to fall in love with our first best idea, but rather to consider issues fully.