MANAGING BY MEANSOR RESULTS

Companies that take this approach are practicing “management by means” (MBM). That is, they design production systems according to precepts that guide all living systems, including:

• self-organization, particularly an ability to identify “self by local rather than central control,
• an emphasis on the relationships among all parts of the organization, and
• the generation of diversity.

Managing by means contrasts sharply with the approach that most businesses follow, called “managing by results” (MBR). With MBR, firms use centralized decision-making to establish abstract quantitative targets for each part of the organization (for instance, “We’ll crank out 250 red widgets on this production line every hour, with zero flaws”). Moreover, decision-makers at these organizations attempt to control the company’s various parts as if the whole thing were a machine (see “Managing by Means or Results?”). Typical MBR control structures include:

• activity-based costing (ABC)
• activity-based management (ABM)
• performance measures to motivate individuals or teams, and
• material requirements planning (MRP) to control operations.

Compared to practices shaped by conventional cost-management thinking, management by means generates far less waste, higher efficiency, lower overhead costs, and more diverse outputs — all the qualities you find in natural, organic processes. In fact, if we look at a living ecosystem — a forest, for example — we see startling efficiency and diversity. Each part of every tree, such as the root system, consumes only the resources it needs to perform its function; in this case, delivering water and nutrients to the rest of the tree. Whatever waste is created, such as the oxygen that results from photosynthesis, is used by other systems connected to the tree within the same ecosystem. So, humans and animals take in the oxygen that trees produce as waste. And throughout evolution, nature has generated virtually unlimited varieties of shapes, sizes, colors, and textures in trees as well as in other living systems.

What does MBM look like in a business setting? Let’s take a closer look at one of the living-system principles that guide MBM — “local control” — to find out. Organizational learning expert Peter Seng explains local control by using a simple analogy: If you cut your finger, your body does not send messages to your brain for permission to act. Rather, your circulatory system generates coagulants near the injury, which flow immediately to the cut. Likewise, at Toyota, everyone who stamps, welds, paints, and assembles cars is guided not by a centralized scheduling system but by one aim: to meet the needs of their direct “customer” the person to whom their work flows next. Materials move smoothly from person to person, with minimal waste. And if workers encounter a problem, they immediately signal for consultation and assistance, never allowing a defect to pass on to the next worker.

MBM can pay big dividends for companies that adopt it. Consider Toyota’s experience: Since 1960, the company has never had a loss year, nor has it ever teetered on the brink of bankruptcy — unlike many of its competitors. Moreover, market capitalization data reveal that Toyota’s market value rivals — and sometimes surpasses — that of the American “Big Three” auto makers combined.

Clearly, MBM offers important advantages over MBR. Yet, most companies continue to organize work according to MBR principles. Why?

The Big Lie

Companies that use MBR have bought into the “big lie” a simple assumption that sounds reasonable on the surface but that makes little sense when you look at how it actually plays out. This big lie is this: You can change the total cost or total profit of your organization by a certain amount by changing the costs or profits of the company’s parts by the e amount. In other words, because the total cost or profit of an organization presumably equals the sum of the costs or profits in parts, the total can be changed in any amount simply by changing its parts in the same amount.

Let’s take a closer look at that last point. This idea — that you can change the magnitude of the whole simply by changing parts in the same magnitude — is everywhere. Open any management accounting, finance, or economics textbook currently in use in MBA programs, and you’ll see this assumption implicit in any discussion about cost management. People actually believe that if they want their company to show an increase in profits of $1 billion, then all they have to do is cut $1 billion from somewhere in the firm. Perhaps they should sell off a division or outsource a major function. The idea is that, by treating the company’s parts as pieces that you can move in or out of the system like game pieces, you can influence the overall organization’s performance in absolutely predictable ways.

To be sure, you can do that with most machines. But with living systems — and human organizations are living systems — trying to optimize the whole by optimizing the parts only leads to declining performance. Still not convinced? Imagine a top-notch basketball team. Now think about what would happen if each player tried to optimize her individual performance by scoring as many baskets as possible during a game. What would happen to the team’s ability to function as a smoothly running, coordinated team? If you envisioned a chaotic mess easily bested by the opposing team, you understand the danger inherent in this assumption about optimizing parts of a natural system.

Where did this mechanistic way of treating human systems come from? In the West, the idea has a long history. Galileo, the 16th century Italian astronomer and physicist, first introduced the concept of separating the idea of motion from a moving object itself — and then measuring that motion. He came up with this idea as a way to address anomalies in moving objects that existing theories inherited from Aristotle couldn’t explain. After the 16th century, Westerners began trying to quantify everything. As Galileo’s thinking was further developed by Rene Descartes and then Isaac Newton, Westerners began seeing the world as a set of independent objects. They defined the characteristics of these objects by absolute measures and believed that it was only external force or impact, not embodied patterns in a web of relationships, that moved these objects.

When Actions Backfire

Today, management science still draws from the mechanistic worldview. But when you treat organizations as machines, you behave in ways that ultimately keep you from achieving your original goal of improving company performance to its full potential.

“Working Harder.” Companies that manage by means achieve a simplicity that lets each step in the production process move forward cheaply, quickly, and with high quality. But when you believe the big lie, you “work harder” in each of the organization’s parts in order to “improve” performance in the whole. What does working harder look like? To force better performance in each part of the organization, you create imbalances among parts and systemic delays that cause you to build an elaborate infrastructure — processes for scheduling, expediting, controlling, reworking, and so forth. In other words, you make things complicated.

Thus, companies that manage by results create complication, which clogs up the workflow with waste, delays, and high costs. This degree of complication gets costly in terms of the people and other resources required to run this infrastructure. Indeed, accountants call this cost “overhead” or “indirect cost.” In many companies, this cost amounts to as much as half of all the costs incurred by doing business.

Higher costs in turn prompt you to produce a smaller variety of products or services in an effort to control those costs. After all, it takes enormous energy and effort to create variety. Companies that emphasize MBR often try to do things as homogeneously as possible; that is, they resort to mass production in order to streamline costs and processes. But in an age of increasingly complex customer demands, mass production isn’t the kind of response that’s going to endear a company to its external customers.

Complication increases the time required for work to move from one destination to another. And when work does move from stage to stage, it progresses intermittently. It lurches along rather than flowing smoothly and effortlessly. Quality also suffers when things get complicated. If you define quality as giving customers what they want, when they want it, and how they want it, it’s hard to achieve all that when you’re grappling with a complicated order-delivery system.

All told, performance drops rather than improves with MBR. If we compare the costs and benefits of MBM and MBR, the differences between the two approaches are striking (see “The Advantages of MBM”).

Working Separately. The big lie also causes you to treat each part of the company as a separate entity. Departments arise in which people work independently of each other. Indeed, people in the various departments, or functional “chimneys,” may even feel indifferent to what folks do in other departments.

In such an arrangement, work comes together only through the vast array of infrastructures that have been created to collect and combine materials or information. Although people in the various departments may all be doing useful, valuable work, the system itself — the organization — doesn’t help the work flow from stage to stage in a smooth, continuous way.

The Big Truth

In truth, you can’t optimize a whole organization by trying to optimize its parts. That’s because in natural systems, the whole doesn’t equal the sum of its parts. We hear that phrase often — but what does it really mean for human organizations?

Because organizations represent an individual human system writ large, let’s see what happens when we compare the value of a whole human being with the value of his or her individual parts. If you disassembled a person into all the molecules that make him up and removed the water that constitutes most of any human being’s cells, what you’d have left wouldn’t weigh more than a few pounds. And, it wouldn’t be worth more than about 50 cents on any market. If you took things one step further and broke those few pounds of molecules into the atomic particles that make them up, you’d have a pile of “stuff” so tiny that you couldn’t even see it with the naked eye.

Now imagine doing something similar with a business. Picture adding up the value of all the separate parts of the business — the equipment, the supplies and inventory, the cash, the building, even the human beings who work there. The dollar amount that you come up with won’t be anywhere near the actual value of the organization when it’s working as a system — that is, when the relationships among all those parts are functioning. The value of the overall organization comes not from its various parts but from the way in which those parts interact. Thus, it is because of those relationships that the whole is worth far more than the sum of its individual components.

Moving from Managing by Results to Managing by Means

So how can your organization avoid the pitfalls inherent in MBR and reap the benefits offered by MBM? It’s not easy. You have to look at work through a radically different lens. Put another way, this change requires you to stop trying to identify better answers and instead ask a new question: What would your organization be like if it ran according to the principles that guide natural systems?

Here are three provocative ideas to get you started:

Nurture Relationships.

If you ran your organization according to natural systemic principles, you would stop trying to optimize performance in the company’s individual parts in order to improve the overall organization’s performance. Rather, you would try to improve the quality of the relationships among the parts.

THE ADVANTAGES OF MBM

For example, you might take steps to channel the flow of information and material into direct pathways between employees whose work interconnects. Ideally, each worker would hand material directly to the next worker in response to a signal from that worker. Where distance in space or time makes direct flow impossible at the moment, workers might use indirect signals, such as empty slots in a rack or order cards. But the goal should be to replace such tools with ways to make it easier for the “upstream” employee to see what the “downstream” employee (his or her “internal customer”) needs.

By having work follow standardized procedures as well as having it flow along direct pathways from worker to worker, you ensure that any problems that arise are visible to people as soon as they occur. This instant, widespread feedback lets people respond immediately to problems and play a direct role in their resolution. In addition, you would make sure that all material flowed at the rate demanded by the customer (whether internal or external). Work should not lurch from stage to stage at varying rates. When it does, the company needs places to store backlog and processes to keep track of it. Expenses start mounting. And whenever material and information come to a standstill, the delay reverberates all the way along the rest of the work path. It’s impossible to deliver quality — giving customers what they want, when they want it — under conditions of uneven or intermittent flow.

Management expert Dr. W. Edwards Deming emphasized the importance to quality of building proper relationships in organizations including always knowing how every customer connects with every worker. Deming suggested a powerful exercise to demonstrate where you need to clarify and strengthen relationships in your organization: Ask everyone to stand up and grab hold of the hand of the person who supplies them with whatever it is they need to do their work. Now ask them to take their other hand and grab hold of the person who needs something from them to get their work done. According to Deming, if your workforce can’t do that, your company is suffering from serious disconnection.

Another management visionary and poet, Judy Brown, offered a compelling image of the importance of relationships in MBM. Brown describes building a log fire. The flame comes from the logs, she agrees, but simply jamming logs together won’t generate a flame. To get a good, strong fire, you have to pay attention to the spaces between the logs. If you stack the logs too tightly, the flame may start, but it’ll sputter out quickly owing to lack of sufficient oxygen. If you stack the logs too loosely, the flame will never get started. To get the flame just right, you have to stack the logs just right. That flame is like the performance an organization is able to achieve, and those spaces between the logs are like the relationships between the people and other components in an organizational system.

Take a Long-Term Focus. While MBR tactics can boost financial performance for short periods, they invariably lead to more unstable and inferior performance in the long run. A company that runs according to principles that guide natural systems will enjoy long-term results that are more stable and more satisfying than the results recorded by a company that runs according to MBR principles. This difference is portrayed in a graph of the performance of two hypothetical organizations — Company A (run based on MBR) and Company B (run based on MBM) (see “Stability Vs. Drama”). To evaluate the two companies’ performance, the graph plots performance over several business cycles, using traditional financial metrics, such as operating income, operating profit, return on investment, and so forth.

In this graph, Company A shows a variable, unstable performance pattern. Company B’s performance pattern varies much less; overall, this firm seems much more stable. At first glance, Company B’s performance looks kind of lukewarm. The firm never loses money, but it never achieves the kinds of peaks that Company A does. However, Company B always does reasonably well. Indeed, in the long run, its average results may prove better than its competitors’.

Toyota is an example of a Company B enterprise. Its long-term financial performance is less variable and, overall, less “exciting” than that of its competitors. In times of peak prosperity, its bottom-line returns seldom garner the attention the press often pays to its competitors’ soaring profits. But during recession periods, it never suffers negative returns.

Differences in accounting conventions make it difficult to unambiguously compare Toyota’s average long-term profitability with that of the American auto makers. However, stock market capitalization data indicate that Toyota earns a consistently higher average level of profit than any of its competitors. Indeed, annual data compiled since 1988 show that Toyota’s “market cap” exceeds the market cap of every one of the American “Big Three” auto makers in each year, and it equals or exceeds the combined market cap of the Big Three in three of those years (see “Toyota Vs. the Big Three”)!

Support a “Multicellular” Organization. In a “natural” organization, work follows a simple and straightforward path. Orders come in, and products go out. That’s it. How does this happen? Everyone in the company functions as an essential part of a multi cellular organization: They each figure out what they need to do to satisfy their customer — whether it’s someone within the company to whom their work flows next or someone outside. The flow of work through the entire system resembles that of the metabolic flow through the cells in a tree or in a human body. Moreover, the rate of that flow is dictated not by centralized control mechanisms, but simply by what the customer wants, in the time he or she wants it. As a result, work flows at the same rate among all the cells of the “organism.”

Thus, rather than looking to financial controllers, cost accounting procedures, and computers to tell them what to do next, employees in a natural organization look to the flow of work itself — at every step in the value stream — to determine what needs to be done. The work itself gives them all the information they need. To have the information that guides work be present in the work itself is not possible, of course, until the work flows more or less continuously from hand to hand. Connecting work in a continuous flow is how a company begins to free its operational information from bondage to computer control systems.

To run your organization according to the principles that guide living systems, you may well have to let go of old assumptions and adopt challenging new ones. But as Toyota has proven beyond question, the payoff makes the effort worthwhile. Indeed, Toyota’s example shows that treating the means as “ends-in-the-making” is a much surer route to stable and satisfactory financial performance than to continue, as most companies do, to chase targets as though the means do not matter.

TOYOTA VS. THE BIG THREE

Stock market capitalization data indicate that Toyota earns a consistently higher average level of profit than any of its competitors. Indeed, annual data compiled since 1988 show that Toyota’s “market cap” exceeds the market cap of every one of the American “Big Three” auto makers in each year, and it equals or exceeds the combined market cap of the Big Three in three of those years.

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In an attempt to capture learnings, we make our best efforts to take time out to reflect. For example, we may institutionalize project “postmortems,” or have an internal consultant study and document lessons learned. Or, we may focus on the “front end” by conducting training in balancing inquiry and advocacy, understanding systems archetypes, or engaging in dialogue.

All of these approaches have the potential to shift us out of our reactive ruts. But they do not automatically become part of an organization’s working habits; we must devote time, resources, and infrastructure to tend to and nurture them. More often than we care to admit, “lessons learned” collect dust on the shelf because we just don’t have the time to translate others’ hard-won insights into our next high-priority project. And sometimes our new reflection skills and techniques are just “out of sync” with our workflow — we don’t have time for them when we need them, and when we do have time, other priorities beckon us.

THE EMERGENT LEARNING PROCESS

“Learning from experience” is mostly done retrospectively. Engaging in emergent learning means taking an intentional, evolutionary approach to learning “through” experience — by conducting iterative experiments using a group’s real work as the experimental field. Taking this approach often produces new and powerful learning simultaneously to making headway on key business issues

Emergent learning practices offer us a pragmatic, low-overhead approach to making the time and space for organizational learning habits to grow. In the process, they help teams and business units develop “islands of mastery,” or growing areas of expertise in their increasingly complex working environments. And the practices help sponsors identify incremental wins and build a business case for the value of organizational learning.

What Is Emergent Learning?

Emergent learning is the ongoing exploration of a locally defined arena of action through intentional, iterative learning experiments. The goal of emergent learning is for a group of people — perhaps a team or business unit — to master performance in arenas of key importance to their business. The focus of these learning experiments might be improving the organization’s ability to fulfill its basic mission (such as, for a police department, reducing crime), managing escalating costs, creating successful strategic alliances, or bringing projects in on time and under budget. An experiment might involve comparing two recent strategic alliances, forming conclusions about these experiences, and testing the conclusions on a new project. Or for a group of project managers, an experiment might mean getting clients involved in projects at different times and in different ways to see how these variables affect the decision-making process But in each case, the two characteristics that distinguish emergent learning from how we usually approach simply “learning from experience” are that it is iterative and intentional. Teams repeat emergent learning experiments in parallel or in close enough succession to be able to compare and contrast performance from instance to instance. They purposefully define experiments in advance of the experience, not in retrospect, as in a “post-mortem.” These intentional iterations make learning from experience active and evolutionary, rather than a static, one-time review.

Simply put, today’s working environments are often too complex and fast moving to give us the time and space we need to focus our full attention on learning. Consequently, the practical reality for many of us is that only those learning practices that require little time will actually take root (see “Rethinking Time” by Peter M. Senge in The Dance of Change, Doubleday/Currency, 1999). By weaving learning into the real-time priorities and real work challenges of a business unit or team, an emergent learning approach bypasses the need to stop what we’re doing in order to learn

In fact, a team may develop extraordinary emergent learning practices without ever thinking of it as “learning.” Emergent learning often looks a lot more like locally driven strategic planning or problem-solving than like what we usually think of as training. Groups self-organize to focus on improving their performance, rather than stepping into a classroom setting where the attention centers on the instructor’s expertise. On the other hand, because of its iterative nature, it differs from what we traditionally think of as planning or problem-solving by focusing on mastery (performance over time), rather than on accomplishment (performance today) (see “Comparing Training, Planning, and Emergent Learning” on p. 3).

Emergent Learning in Practice

Here’s an example of an emergent learning process based on a group’s real work needs and conducted in real time: The executive team of a large regional vocational school expressed its frustration at once again needing to downsize because of escalating costs. In years past, members had rolled up their sleeves and done the painful work of identifying possible staffing and program cuts. When all was said and done, they had at least felt a sense of accomplishment at having taken hard but necessary steps to solve the problem.

After the third downsizing this decade, they made a determined effort to escape from what they had come to see as a vicious cycle by taking steps to shift their focus from short-term crisis resolution to developing long-term solutions through emergent learning.

The team defined an arena on which to focus: its cost structure. Facing obvious and painful failures in trying to solve recurring financial problems, members recognized how little they really understood their costs. They made a commitment to “master” the cost arena — to develop a richer, shared understanding of what drives costs, and to be able to consistently manage them. They had a discussion to articulate the key variables or criteria that would indicate success in this arena.

The team then identified a few repeatable contexts that could easily provide opportunities for reflection: weekly staff meetings and executive reporting. Because these activities were already on their plates, they provided a relatively quick and easy way for team members to test their mental models about what was driving costs. Because they were recurring, the group could easily review the results of experiments that they planned to conduct on a regular basis, and gradually evolve a real mastery of the issue.

This process may look like nothing more than good problem-solving. But it demonstrates a subtle shift from accomplishment to mastery

To get started, team members shared their beliefs and understanding about what contributed to the school’s cost structure. Then they very deliberately turned these statements into hypotheses to test in learning experiments. Each member considered what projects he or she was involved in or what data he or she had that would serve as the basis for conducting experiments. For example, the head of programs was curious about whether his assumptions about the direct relationship between class size, perceived program quality, and costs would hold up. The head of facilities had questions about whether previous cuts in headcount might have actually resulted in increased maintenance costs.

Initially, they simply added brief reviews of cost trends (such as compensation, legal fees, and supplies) to their weekly meetings, and a discussion of 12-month cost patterns to the monthly and quarterly executive reports. Over time, through several iterations, they began to see new relationships and investigate such dynamics as the relationship between facilities maintenance, compensation, and legal costs. In staff meetings, they reflected on the potential causes of changes in costs and described experiments that they had tried. (At one meeting, the head of facilities reported about asking his team what they would do if he went on sabbatical for a year. The creative responses that he got inspired some of his peers to try the same experiment.)

At each iteration, the results of just-completed learning experiments became the “ground truth” on which they reflected in order to plan for the next learning experiments (see “The Emergent Learning Process” on p. 1). With the benefit of their peers’ perspectives, team members teased out unspoken assumptions, lessons learned, and so on. They began to question the measures that they had relied on in the past and realized that they needed more powerful and timely cost indicators. They acknowledged how delays in feedback — in the form of unanticipated cost increases — affected their ability to manage expenses. These sessions inevitably led to new questions and new experiments.

Beyond Problem-Solving

This process may look like nothing more than good problem-solving. But it demonstrates a subtle shift from accomplishment to mastery. With this new mindset, everyone on the school’s executive team worked under the assumption that they would run through the learning cycle at least several times. Over time, as they cycled through iterations of this process, their learning experiments got more specific and they asked better and better questions. They also developed finer distinctions about costs and the dynamics that cause them to rise. In addition, they identified early indicators that a problem was brewing. As a result, their sense of confidence in being able to tackle something as complex as escalating cost structures grew.

On the other hand, if the team had continued to focus on problem-solving rather than on learning, they might have replaced downsizing with another, perhaps equally short-term, “solution.” By simply abandoning their first approach to the problem, they may have failed to develop a true understanding of why downsizing did not solve the problem. Or they might have chosen to “downsize harder,” triggering even steeper cost problems as the school struggled with the loss of skilled personnel

By taking an emergent learning approach, the team also created a compelling context for drawing on the tools of organizational learning. For example, they began to see that they had fallen into a “siege” mentality regarding saving their favorite function from the chopping block. So the group sought training in balancing inquiry and advocacy, recognizing that their ineffective communication habits were affecting their ability to explore alternative theories and solutions. They also studied systems thinking to begin to grasp the drivers of costs and to understand the behavior of reinforcing processes. In this way, they developed expertise as they needed it and as it made sense for addressing their current business challenges — not as it was deemed necessary by a training department or corporate mandate.

Simplicity and Localness

The best emergent learning practices track a few simple variables within an experimental field that is as local as possible. In the example above, the executive team initially tracked operating costs (variables) within the different departments (experimental fields). Each participant made a series of small changes to the work that they were already doing in these areas.

Over the long term, these intentional, iterative experiments at the operational level often generate new and unpredicted, but remarkably powerful, changes in behavior. For example, the Boston Police Department uses simple three-month charts of major crimes, district-by-district, to understand and influence crime trends, such as a rise in burglaries in a particular neighborhood. Over time,

this disciplined approach to managing crime has inspired district police to go out of their way to meet local teens and attend community meetings, not because it’s their job, but because they see that making a personal connection is critical to grasping what is fundamentally driving trends in crimes.

The U. S. Army’s After Action Reviews (AARs), which emerged from its intensive two-week training simulations in the Mojave Desert, are another example of a practice that is so simple and local in design that it spread on its own, without being mandated from above. In an AAR, soldiers take an hour after a military encounter (simulated or real) to analyze what caused any differences between what they intended to accomplish and what actually happened. In addition, they identify strengths to sustain and weaknesses to improve in the next encounter. AARs have become so ingrained in the organization’s culture that almost anything is now seen as a learning opportunity—, “Let’s AAR that.”

Committing to Learning Experiments

As shown in these examples, opportunities for emergent learning are everywhere. The seeds for it can be found in what Barry Dym calls “forays” — small, local initiatives that are exceptions to the more established patterns of working together (see “Forays: The Power of Small Changes” by Barry Dym, V9N7). They can also spring up in “communities of practice” — informal groups that join together to develop a shared repertoire of resources. To reap the benefits of emergent learning, members of these groups must shift from following the traditional professional association model — holding abstract conversations based on expert presentations — to making the commitment to study their own performance in a concretely defined field of experiments (see “Communities of Practice: Learning as a Social System” by Etienne Wenger, V9N5).

Nortel Networks’ Competitive Analysis Guild (CA Guild) is an apt example of a self-organized community of practice that has been able to make that shift. The CA Guild gathers members from across organizational boundaries to share knowledge about Nortel’s competitors and build their competitive intelligence skills. Guild membership outlives project assignments and creates a “virtual neighborhood” of likeminded individuals.

Some Guild practices look like those of traditional professional associations: monthly meetings with formal presentations and a Web site with announcements of upcoming events. But the Guild has also created some activities that are developing emergent qualities. For example, any Nortel Networks employee can use the Guild Web site to seek information about competitors from members. The sharing of questions and answers through the network is an iterative process. Participants have reported that they have become more sensitive to early indicators of important actions by competitors.

The Guild also views industry trade shows as a natural experimental field. At any given industry trade show, there may be 30 or more Nortel Networks employees wandering the floor. The Guild developed a procedure to focus these employees on a learning agenda. After each show, not only does the Guild take away good data, but it also reflects on and refines its trade-show practices. Over time and with iterations, this approach turns good intelligence-gathering into emergent learning.

Islands of Mastery

Peter Senge has commented that, “I have never seen a successful organizational learning program rolled out from the top. Not a single one. Conversely, every change process that I’ve seen that was sustained and that spread has started small. Usually these programs start with just one team” (Fast Company, May 1999). Emergent learning builds the organizational learning “habit” from the bottom up, by focusing a team on mastering performance in an arena that is important to them. The venue may be big and “strategic,” such as demonstrating leadership during a merger, or it may be small and “tactical,” such as planning food for faculty meetings. Whatever the level, as the team disciplines itself to focus its attention on its performance in this one arena in an iterative way, a lot of what previously seemed like erratic, unpredictable results can begin to make sense (see “Conducting Learning Experiments”).

Emergent learning builds the organizational learning “habit” from the bottom up

And so, an island of mastery begins to emerge from the sea of complexity. And as one arena of action starts to make sense, the group naturally expands its field of inquiry into other arenas. In turn, team members’ confidence in being able to master their business challenges grows. They become better able to clarify their priorities, articulate their own theory of success, test their hypotheses, and make a strong case in support of their thinking.

This self-reinforcing cycle of curiosity and growing competence can have an almost addictive quality — it makes people thirsty to learn more. As people develop a learning discipline and begin to search for fundamental solutions, they almost automatically take a systems perspective, collaborate more effectively with others, and challenge their existing mental models.

In this way, pairing emergent learning practices with traditional training can help the tools and techniques of organizational learning find a natural home. As internal and external practitioners, we can look for opportunities to turn events and projects that we are currently working on into learning experiments. We can do more to identify and support naturally occurring emergent learning practices, and make it a priority to notice and publicize results. And we can also help business units, teams, and communities of practice create new emergent learning practices. In the process, we will build natural advocates for organizational learning, complete with their own compelling stories to tell.

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