One of the reasons for this wide-spread institutional failure is that the knowledge-creating system, the method by which human beings collectively learn and by which society’s institutions improve and revitalize themselves, is deeply fragmented. This fragmentation has developed so gradually that few of us have noticed it; we take the disconnections between the branches of knowledge and between knowledge and practice as a given

A Knowledge-Creating System

Before we can address the issue of fragmentation, we need to establish what has been fragmented. In other words, what do we mean by a knowledge-creating system, and what does it mean to say it is fragmented?

THE CYCLE OF KNOWLEDGE-CREATION

Like theories, the tree’s roots are invisible, and yet the health of the root system determines the health of the tree. The branches are the methods and tools, which enable translation of theories into new capabilities and practical results. The fruit is that practical knowledge. The tree as a whole is a system.

We believe that human communities have always attempted to organize themselves to maximize the production, transmittal, and application of knowledge. In these activities, different individuals fulfill different roles, with varying degrees of success. For example, in indigenous cultures, elders articulate timeless principles grounded in their experience to guide their tribes’ future actions. “Doers, “whether warriors, growers, hunters, or nannies, try to learn how to do things better than before and continually improve their craft. And coaches and teachers help people develop their capacities to both perform their roles and grow as human beings. These three activities-which we can term theory-building, practice, and capacity-building-are intertwined and woven into the fabric of the community in a seamless process that restores and advances the knowledge of the tribe. One could argue that this interdependent knowledge-creating system is the only way that human beings collectively learn, generate new knowledge, and change their world.

We can view this system for producing knowledge as a cycle. People apply available knowledge to accomplish their goals. This practical application in turn provides experiential data from which new theories can be formulated to guide future action. New theories and principles then lead to new methods and tools that translate theory into practical know-how, the pursuit of new goals, and new experience-and the cycle continues.

Imagine that this cycle of knowledge-creation is a tree (see “The Cycle of Knowledge-Creation” on p.1). The tree’s roots are the theories. Like theories, the roots are invisible to most of the world, and yet the health of the root system to a large extent determines the health of the tree. The branches are the methods and tools, which enable translation of theories into new capabilities and practical results. The fruit is that practical knowledge. In a way, the whole system seems designed to produce the fruit. But, if you harvest and eat all the fruit from the tree, eventually there will be no more trees. So, some of the fruit must be used to provide the seeds for more trees. The tree as a whole is a system.

The tree is a wonderful metaphor, because it functions through a profound, amazing transformational process called photosynthesis. The roots absorb nutrients from the soil. Eventually, the nutrients flow through the trunk and into the branches and leaves. In the leaves, the nutrients interact with sunlight to create complex carbohydrates, which serve as the basis for development of the fruit.

So, what are the metaphorical equivalents that allow us to create fruits of practical knowledge in our organizations? We can view research activities as expanding the root system to build better and richer theories. Capacity-building activities extend the branches by translating the theories into usable methods and tools. The use of these methods and tools enhances people’s capabilities. The art of practice in a particular line of work transforms the theories, methods, and tools into usable knowledge as people apply their capabilities to practical tasks, much as the process of photosynthesis converts the nutrients into leaves, flowers, and fruit. In our society,

• Research represents any disciplined approach to discovery and understanding with a commitment to share what’s being learned. We’re not referring to white-coated scientists performing laboratory experiments; we mean research in the same way that a child asks, “What’s going on here?” By pursuing such questions, research-whether performed by academics or thoughtful managers or consultants reflecting on their experiences-continually generates new theories about how our world works.
• Practice is anything that a group of people does to produce a result. It’s the application of energy, tools, and effort to achieve something practical. An example is a product development team that wants to build a better product more quickly at a lower cost. By directly applying the available theory, tools, and methods in our work, we generate practical knowledge
• Capacity-building links research and practice. It is equally committed to discovery and understanding and to practical know-how and results. Every learning community includes coaches, mentors, and teachers – people who help others build skills and capabilities through developing new methods and tools that help make theories practical.

“The Stocks and Flows of Knowledge-Creation” shows how the various elements are linked together in a knowledge-creating system.

THE STOCKS AND FLOWS OF KNOWLEDGE-CREATION

Research activities build better and richer theories. Capacity-building functions translate the theories into usable methods and tools. The use of these methods and tools enhances people’s capabilities. The art of practice transforms the theories, methods, and tools into practical knowledge, as people apply their capabilities to practical tasks.

Institutionalized Fragmentation

If knowledge is best created by this type of integrated system, how did our current systems and institutions become so fragmented? To answer that question, we need to look at how research, practice, and capacity-building are institutionalized in our culture (see “The Fragmentation of Institutions”).

For example, what institution do we most associate with research Universities? What does the world of practice encompass? Corporations, schools, hospitals, and nonprofits. And what institution do we most associate with capacity-building-people helping people in the practical world? Consulting, or the HR function within an organization. Each of these institutions has made that particular activity its defining core. And, because research, practice, and capacity-building each operate within the walls of separate institutions, it is easy for the people within these institutions to feel cut off from each other, leading to suspicion, stereo typing, and an “us” versus “them” mindset.

This isolation leads to severe communication breakdown. For example, many people have argued that the academic community has evolved into a private club. Nobody understands what’s going on but the club members. They talk in ways that only members can understand. And the members only let in others like themselves.

Consulting institutions have also undermined the knowledge-creating process, by making knowledge proprietary, and by not sharing what they’ve learned. Many senior consultants have an incredible amount of knowledge about organizational change, yet they have almost no incentive to share it, except at market prices.

Finally, corporations have contributed to the fragmentation by their bottom-line orientation, which places the greatest value on those things that produce immediate, practical results. They have little patience for investing in research that may have payoffs over the long term or where payoffs cannot be specifically quantified.

Technical Rationality: One Root of Fragmentation

How did we reach this state of fragmentation? Over hundreds of years, we have developed a notion that knowledge is the province of the expert, the researcher, the academic. Often, the very term science is used to connote this kind of knowledge, as if the words that come out of the mouths of scientists are somehow inherently more truthful than everyone else’s words.

Donald Schon has called this concept of knowledge “technical rationality.” First you develop the theory, then you apply it. Or, first the experts come in and figure out what’s wrong, and then you use their advice to fix the problem. Of course, although the advice may be brilliant, sometimes we just can’t figure out how to implement it.

But maybe the problem isn’t in the advice. Maybe it’s in the basic assumption that this method is how learning or knowledge-creation actually works. Maybe the problem is really in this very way of thinking: that first you must get “the answer,” then you must apply it.

THE FRAGMENTATION OF INSTITUTIONS

Because research, practice, and capacity-building each operate within the walls of separate institutions, the people within these institutions feel cut off from each other, leading to suspicion, stereotyping, and an “us” versus “them” mindset.

The implicit notion of technical rationality often leads to conflict between executives and the front-line people in organizations. Executives often operate by the notion of technical rationality: In Western culture, being a boss means having all the answers. However, front-line people know much more than they can ever say about their jobs and about the organization. They actually have the capability to do something, not just talk about something. Technical rationality is great if all you ever have to do is talk.

Organizing for Learning

If we let go of this notion of technical rationality, we can then start asking more valuable questions, such as:

• How does real learning occur?
• How do new capabilities develop?
• How do learning communities that interconnect theory and practice, concept and capability come into being?
• How do they sustain themselves and grow?
• What forces can destroy them, undermine them, or cause them to wither?

Clearly, we need a theory, method, and set of tools for organizing the learning efforts of groups of people.

Real learning is often far more complex and more interesting than the theory of technical rationality suggests. We often develop significant new capabilities with only an incomplete idea of how we do what we do. As in skiing or learning to ride a bicycle, we “do it” before we really understand the actual concept. Similarly, practical know how often precedes new principles and general methods in organizational learning. Yet, this pattern of learning can also be problematic.

For example, teams within a large institution can produce significant innovations, but this new knowledge often fails to spread. Modest improvements may spread quickly, but real breakthroughs are difficult to diffuse. Brilliant innovations won’t spread if there is no way for them to spread; in other words, if there is no way for an organization to extract the general lessons from such innovations and develop new methods and tools for sharing those lessons. The problem is that wide diffusion of learning requires the same commitment to research and capacity-building as it does to practical results. Yet few businesses foster such commitment. Put differently, organizational learning requires a community that enhances research, capacity-building, and practice (see “Society for Organizational Learning” on p. 4)

Learning Communities

We believe that the absence of effective learning communities limits our ability to learn from each other, from what goes on within the organization, and from our most clearly demonstrated breakthroughs. Imagine a learning community as a group of people that bridges the worlds of research, practice, and capacity-building to produce the kind of knowledge that has the power to transform the way we operate, not merely make incremental improvements. If we are interested in innovation and in the vitality of large institutions, then we are interested in creating learning communities that integrate knowledge instead of fragment it.

In a learning community, people view each of the three functions-research, capacity-building, practice-as vital to the whole (see “A Learning Community”). Practice is crucial because it produces tangible results that show that the community has learned something. Capacity-building is important because it makes improvement possible. Research is also key because it provides a way to share learning with people in other parts of the organization and with future generations within the organization. In a learning community, people assume responsibility for the knowledge creating process.

Learning Communities in Action

To commit to this knowledge-creating process, we must first understand what a learning community looks like in action in our organizations. Imagine a typical change initiative in an organization; for example, a product development team trying a new approach to the way they handle engineering changes. Traditionally, such a team would be primarily interested in improving the results on their own projects. Team members probably wouldn’t pay as much attention to deepening their understanding of why a new approach works better, or to creating new methods and tools for others to use. Nor would they necessarily attempt to share their learnings as widely as possible – they might well see disseminating the information as someone else’s responsibility.

In a learning community, however, from the outset, the team conceives of the initiative as a way to maximize learning for itself as well as for other teams in the organization. Those involved in the research process are integral members of the team, not outsiders who poke at the system from a disconnected and fragmented perspective. The knowledge creating process functions in real time within the organization, in a seamless cycle of practice, research, and capacity-building.

Imagine if this were the way in which we approached learning and change in all of our major institutions. What impact might this approach have on the health of any of our institutions, and on society as a whole? Given the problems we face within our organizations and within the larger culture, do we have any choice but to seek new ways to work together to face the challenges of the future? We believe the time has come or us to begin the journey back from fragmentation to wholeness and integration. The time has come for true learning communities to emerge.

Peter M. Senge, best-selling author of The Fifth Discipline: The Art and Practice of the Learning Organization, is an international leader in the area of creating learning organizations. He is a senior lecturer in the Organizational Learning and Change Group at MIT. Peter has lectured throughout the world and written extensively on systems thinking, institutional learning, and leadership.

Daniel H. Kim is a co-founder of Pegasus Communications, Inc., and publisher of The Systems Thinker. He is a prolific author as well as an international public speaker, facilitator, and teacher of systems thinking and organizational learning

Editorial support for this article was provided by Janice Molloy and Lauren Johnson

A LEARNING COMMUNITY

In a learning community, people view each of the three functions—research, capacity-building,practice—as vital to the whole

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In the mid 1980s, “knowledge management” superseded “information management,” again building on the best aspects of the existing practice while representing an exponential leap forward. By recognizing the need to capture, store, and make accessible people’s operational knowledge, proponents of knowledge management tapped into a hidden source of competitive advantage.

Today, the field of “knowledge ecology” has emerged as a natural outgrowth of knowledge management. Whereas the target of knowledge management is to accumulate and leverage knowledge, knowledge ecology’s goal is to develop and mobilize collective intelligence and ultimately organizational wisdom. By acknowledging the social nature of learning and the key role that technology can play in bringing people together, knowledge ecology bridges the gap between the static data repositories of knowledge management and the dynamic, adaptive behavior of natural systems (see “The Virtuous Cycle of Knowledge Ecology”). To understand this new approach to “knowing what we know,” we first must understand the relationship among knowledge, intelligence, and wisdom.

The Knowledge, Intelligence, Wisdom Value Chain

Knowledge. Knowledge is the capacity to act. As Peter Drucker has said, “Knowledge is information that changes something or somebody — either by becoming grounds for actions, or by making an individual (or an institution) capable of different or more effective action.” Books, databases, lists of “best practices,” help desks, and so on are important in that they contribute to and influence our knowledge. However, these mainstays of traditional knowledge management implementation themselves do not have the capacity to act. They are repositories of information, not of knowledge.

Researchers from across the spectrum agree that learning is a social activity. We create, share, and utilize knowledge through our interactions with others. In this way, knowledge emerges through productive conversations — both face-to-face and through various media — and networks of relationships. These resources cannot be managed, only inspired by work systems that reward collaboration, learning, and innovation.

Intelligence. Designing knowledge-creation structures and practices requires a certain level of intelligence. Intelligence refers to our effective use of knowledge and our capacity to respond to specific opportunities and challenges as they emerge. In biological systems, an organism’s innate intelligence enables it to adapt to changing circumstances. The same is true of social systems. The main function of a workplace’s collective intelligence is to sustain the “social organism” by augmenting its ability to rapidly respond to conditions of accelerating complexity and chaos.

An organization develops collective intelligence the same way bodies do — through a nervous system. The nervous system in a social organism is the ongoing series of conversations and contacts that enable it to coordinate its actions and learn from its experience. It is embedded not in computers and hardware, but in the interactions among people that bring the organization into existence day after day and help it evolve. Collective intelligence continually emerges from the energy and information that move through this infrastructure.

To thrive, an organization must have both the wisdom to ask the right questions at the right time, and the infrastructure for tapping into its own collective intelligence for responses.

In both biological and social systems, the quality of the nervous system affects the quality of the intelligence that flows through it. Organizations have better chances to grow healthy and robust nervous systems — with requisite flexibility — if their members are connected and motivated to realize their full creative potential in support of the joint enterprise. If participants have easy and convenient ways to share what they know, the accelerated flow of knowledge lets the organization act with cat-like reflexes in the face of rapid changes in its environment.

The stage of evolution of a given nervous system — both in biological and social organisms — defines how effectively it can perform the following four functions.

• Communication: Facilitating the exchange and flow of information among the organizations’ subsystems and with its external environment.
• Coordination: Effectively coordinating the actions of the subsystems and of the whole.
• Memory/Knowledge Management: Storing, organizing, and recalling information as needed.
• Learning: Guiding and supporting the development of new competencies and effective behaviors.

Each of these activities is vital to the performance and evolution of the organism, be it biological or social. The biological ones have seamlessly integrated these functions in their repertoire of capabilities. Millions of years of trial-and-error have paid off!

Social organisms such as corporations don’t have the luxury to wait that long. If they are to survive and meet the challenges triggered by the current waves of epochal transition, they quickly need to enhance their nervous systems. Only then can they respond to their volatile strategic options with increased agility.

But having an adequate intelligence infrastructure isn’t enough to maintain a community at the leading edge over the long-term. The sustainability of social organisms also requires the exercise of systemic, collective wisdom.

Wisdom. Wisdom refers to our effective use of intelligence, as evidenced by our capacity to alleviate suffering and increase joy in human and organizational systems. As Verna Allee noted in Knowledge Evolution, “Wisdom is . . . a highly creative and connective way of processing knowledge that distills out essential principles and truths. Wisdom tells us what to pay attention to. Wisdom is the truth seeker and pattern finder that penetrates to the core of what really matters.” Systemic wisdom can help with intuiting the long view, understanding systems in the context of their larger whole, and anticipating future crises.

To thrive, an organization must have both the wisdom to ask the right questions at the right time, and the infrastructure for tapping into its own collective intelligence for responses. Organizational wisdom thus plays a key role in dealing with two essential aspects of the new marketplace: the “attention economy” and the “experience economy.” The concept of the “attention economy” derives from the fact that we’re living in an age in which we are continually inundated by information. The competition for people’s attention has reached a fevered pitch. Time and attention are scarce resources; learning to use them wisely has become a valuable personal competence.

In the current conditions of galloping “complexity multiplied by urgency” (as described by Douglas Engelbart, the pioneer of augmenting human intellect with computers), only wisdom can effectively guide our decisions on how to invest our attention, both individual and organizational. Wisdom helps us find a balance between focusing on current tasks and on long-term priorities by offering the power of perspective. It provides us with the ability to take a step back, view the larger picture, and determine what is really important and what is really at stake.

We can also view today’s working world through the lens of the “experience economy.” Customers are looking for more than products/services; they want to have a memorable experience of buying and using those commodities for achieving their aspirations. B. Joseph Pine II and James H. Gilmore, authors of The Experience Economy (Harvard Business School Publishing, 1999), describe this phenomenon: “No matter how acute an experience, one’s memory of it fades over time. Transformations, on the other hand, guide the individual [and the organization] towards realizing some aspiration and then help to sustain that change over time. There is no earthly value more concrete, more palpable, or more worthwhile than achieving an aspiration. Nothing is more important, more abiding, or more wealth-creating than the wisdom required to transform customers. And nothing will command as high a price.”

Where does the wisdom to create this kind of transforming experience reside in an enterprise? How can we notice it and cultivate it? We used to think of wisdom as a hard-earned quality of elderly, white-haired men and women. The emerging field of knowledge ecology opens the possibility of nurturing wisdom as a distributed quality of human communities.

What Is Knowledge Ecology?

What is “knowledge ecology” (KE) and how can it help us to boost organizational knowledge, intelligence, and wisdom? KE is a field of theory and practice that focuses on discovering better social, organizational, behavioral, and technical conditions for knowledge creation and utilization. It is an interdisciplinary discipline that draws on the best of current thought and action, including knowledge management; communities of practice; businesses as complex, adaptive systems; organizational learning; and the hypertext organization. By integrating these and other ideas, KE seeks to help organizations achieve unprecedented breakthroughs in performance while nurturing and enhancing people’s capacity to reach their highest aspirations.

KE operates on the principle that the best models we have for designing systems that create, sustain, and foster organizational learning and development are natural “learning organizations,” like a rainforest or the human brain. KE’s primary area of study and domain of action are the design and support of self-organizing knowledge “ecosystems,” in which information, ideas, insights, and inspiration cross-fertilize and feed one another, free from the constraints of geography and schedule.

According to the 10th edition of Merriam-Webster’s Collegiate Dictionary, an ecosystem is “the complex of a community of organisms and its environment functioning as an ecological unit in nature.” The simplest form of a knowledge ecosystem consists of

• a network of conversations — face-to-face or virtual — contributing to and informed by
• rich knowledge repositories.

Knowledge ecosystems, just like biological ones, are self-sustaining, self-regulating, and self-organizing. They have permeable boundaries through which they can interact with other ecosystems. In a natural ecosystem, the higher the diversity of species, the more robust the community and the more fit for longevity. The same applies to organizational ecosystems.

To visualize a knowledge ecosystem, picture the waves of ideas, requests, and offers that move through your awareness each day as bundles of color-coded lights that link you with your coworkers, customers, and coaches. Play with the colors, if you wish. Then imagine an animated flowchart with small circles representing all the employees in your organization. Arrows of different sizes and colors link the circles to indicate the length and form of each contact — phone calls, memos, reports, meetings — in a single day. Finally, think of this network of contacts as a web of distributed intelligence, comprised of all the members of the enterprise and all the ways in which they create value for other members, the enterprise as a whole, and its stakeholders.

For any organization to have all of its members share what they know with other stakeholders in a limited timeframe, it must “electrify” its network of conversations; that is, link its people networks and computer networks. This kind of “electrified” nervous system can then serve as the infrastructure necessary for a community to self-organize and improve its collective intelligence effectively and consistently.

Practitioners of KE maintain that in knowledge ecosystems, people networks create knowledge networks supported by technology networks (see “The Triple Network”). By “people network,” we mean the members of the organization, their communities of practice, and their company’s customers and other stakeholders, as well as the ways in which they organize their collaboration. By “knowledge network,” we mean the web-like connections between productive ideas that people in organizations generate in the normal course of work.

Unprecedented synergies and creative breakthroughs occur when enabling technologies offer new ways of forming meaningful connections. “Technology network” in this context includes all of the technological means that support communication and collaboration for knowledge creation, sharing, and utilization, ranging from e-mail to video and web conferencing to virtual worlds.

Knowledge communities co-evolve with their shared body of knowledge, and with the protocols and tools for upgrading that knowledge. Organizations pass an evolutionary test when they learn to adapt to and drive innovations in technologies, markets, and organizational design by increasing individual and collective intelligence. The smarter we become as individuals in managing our personal learning processes, the more we can enhance our organization’s collective intelligence. The smarter we become as learning communities, the deeper will be the pool of the collective intelligence that each of us can tap into, thus enhancing our individual intelligence. The two interconnected spirals drive each other ever higher. Companies like Hewlett-Packard and Lucent have found that the “triple network” of people, knowledge, and technology is vital to this process.

The Practice of KE

At the heart of knowledge ecology is the art and science of gleaning meaning and value from productive conversations. This practice represents an art in that it involves the sensitive, spontaneous realm of human relations. It is a science in that it relies on the best of today’s new technologies for bringing people and their ideas together across time and space. KE offers a framework for enhancing an organization’s capacity to learn faster by linking these two seemingly disparate facets of organizational effectiveness (see “The Duality of Conversations and Knowledge Bases”).

How does an organization begin to incorporate KE into its daily activities? Perhaps the first step is to ensure that the corporate culture recognizes the synergy between personal growth and expression and organizational productivity. Management must encourage listening, dialogue, participation, openness, inquiry, reflection, sharing, collaboration, and learning as expressed through mission statements, reward systems, and actions. Every member of the community — whether an organization as a whole, department, community of practice, or team — must feel included in the process and have the means to contribute as an equal participant.

Restricting any member’s contribution to and use of the ecosystem reduces its vitality and capacity to support emergent action. Fortunately, technology offers myriad options for enabling community members in different locations to conduct effective, efficient, and enjoyable knowledge-sharing, collaboration, and coordination of action. “Virtual space” technologies — such as conference calls and videoconferencing, which allow “same time/different-space” meetings — fill the need when speed of action is important and the community needs to process and evaluate simultaneous input from multiple sources.

When conflicting schedules prevent concurrent participation or when continuous access to the community’s shared intellect is crucial, the “virtual time” technologies of e-mail, electronic bulletin boards, and computer conferencing support “different-time/different-space” meetings. In this case, the host computer receives and holds everyone’s input, allowing community members to access the documents at any time of their choice and convenience — , “anytime/anywhere” communication.

The seamless integration of real-time conversations held in a meeting room and those held in the virtual realm is another crucial need. Teams that meet both in-person and on the electronic network need to discover and agree on the best mix of these and other media — telephone, fax, email, videotape, and so on — for completing each of the major tasks that they need to collaborate on.

A computerized system for managing the community’s knowledge assets and memory must provide easy access to shared documents and lessons learned from the past. A well designed system is not merely a repository of files and archives; it also includes the rationale and assumptions upon which actions were based so they can be examined and improved for more effective future action. The system should also indicate where specific organizational memories are located and should be indexed in a logical and easy-to-use manner.

A system has yet to be created that provides all the features that good gardeners of corporate knowledge ecologies would want to have. However, there are many knowledge systems vendors with helpful products. The challenge is to anticipate the set of features that you’ll need in six months or a year. Collaborative scenario-planning, “future conference,” and other processes for anticipating and co-inventing the future can help you design systems to meet upcoming needs.

KE’s Value Proposition

Dysfunctional knowledge ecologies cost organizations much more then well-functioning ones. When information is placed in a database where it is seldom accessed because the details have been separated from the context in which they occurred, companies lose time, money, and valuable insights. When employees hoard working knowledge —  either intentionally or because the organization doesn’t have an infrastructure for individuals to share what they know — it results in lost productivity by triggering the “reinventing the wheel” syndrome, “The same result is produced by hoarding failures. As long as a culture makes people hide their ‘mistakes,’ it pushes others to fall into similar erroneous experiments” (as my colleague Holly Blue Hawkins put it). In each case, the organization’s collective intelligence is squandered and stunted, leaving the company more vulnerable to the whims of the marketplace.

KE is a perspective that responds to the need to nurture systemic wisdom with emerging interdisciplinary insights into the organization and operation of living systems. Corporate knowledge ecosystems are complex adaptive systems. Their power exists in the flexible and evolving relationships among the elements of the system, which interact in complex and often surprising ways. KE provides a framework, tools, and practices for crafting and sustaining evolving webs of relationship in which we can embed and preserve the knowledge that emerges from social activity. In today’s knowledge-driven economy, the highest payoff investment that any business can make is in improving its practices, tools, and methods for creating and sharing new knowledge (see “Improving Organizational Performance”).

Think about your organization. Does it have a collective intelligence, or is it merely a collection of individual intelligences? Organizations that succeed in these times of accelerating change will be social organisms with the collective intelligence to guide them through turbulence and transformation — and the wisdom to take the long view and let it inform the strategic choices of the present. The companies that succeed in achieving repeatable wins in fast-shifting market conditions will be those that have learned to increase value to all stakeholders by leveraging the power of people, knowledge, and technology. These wisdom-driven businesses will easily provide the highest quality products, the highest quality work experience for their members, and an energizing context for societal evolution in the new economy.

George Pór is a pioneer of Knowledge Ecology and the founder of Community Intelligence Labs, a network of change agents dedicated to eliciting transformation by mobilizing the intelligence and wisdom of the whole organization. George is also the convener of the Attention Leadership Circle, an intercorporate research alliance focused on developing better practices and environments for augmenting attention resources of organizations, their leaders, and free agents. Meet him at http://www.co-i-l.com/coil/who/george.shtml.

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Two Generations of Knowledge Management

While many of us were just grasping what the term knowledge management means, innovators at the Knowledge Management Consortium International (KMCI), the organization that McElroy heads and for which I serve as a board member, were already creating a new and improved iteration of the concept. Although some people may be tempted to dismiss this advance as being simply a case of old wine in new bottles, McElroy draws a bold line in the sand between these two distinctly different versions of knowledge management (KM). He explains how first-generation KM approaches are largely based on the notion that organizations are machines; from this perspective, knowledge and information are close cousins in that both are effectively managed through the use of technology. Practitioners of second-generation KM, on the other hand, adopt a more organic view; they regard information as a distant precursor to knowledge and view social processes as more critical than technology for creating new knowledge.

First-generation KM is based on the assumption that knowledge is a well-defined commodity that can be easily used by people throughout a company and that the main task of KM initiatives is to leverage the use of existing knowledge by sharing it freely throughout an organization. Technology becomes valued as an efficient means to accomplish this goal. Therefore, first-generation KM approaches typically focus on the use of technology to collect, analyze, and store data — especially best practices — that organizations can use to improve performance. For instance, a company’s sales force may use wireless systems to capture insights and lessons learned about customer buying patterns and competitor strategies. They then channel this information to someone within the organization who will organize it, conduct meta-data analyses to draw overarching conclusions, and place the results into a computer database. Such databases are then made available to employees through corporate intranets. Employees may access information such as lists of handy selling tips for approaching customers with certain profiles and strategies for increasing sales that have been developed and used successfully by other members of the sales force. Some of these database systems use “Yellow Pages” directories and expertise profiling to help practitioners connect with those colleagues who have demonstrated successes.

Although such tools are technologically impressive, they tend to focus on identifying isolated elements of knowledge, out of their natural context, and fail to address the fundamental process by which knowledge is created in individuals and groups. Second-generation KM seeks to address this shortcoming. The notion that sharing tips about how a colleague successfully achieved a sale presumes that others can effectively use a similar strategy without changing what they believe, how they think, or how they perceive selling situations. Such an approach reduces selling from an art that is developed over years of experience to a form of behavioral mimicry.

The Knowledge Life Cycle

Whether or not you subscribe to the increasingly popular view that first-generation KM has already proven to be ineffective, McElroy gives compelling reasons to consider switching to second-generation KM. He addresses how (1) organizational learning is linked to KM, (2) knowledge drives innovation, (3) complexity and systems thinking are related to KM, and (4) corporate policies can be an important lever for creating knowledge and innovation (see “10 Key Principles of Second-Generation Knowledge Management” on p. 8). For example, in first-generation KM schemes, such as those that focus on creating formal mechanisms for sharing best practices, knowledge is driven by what we might call “supply-side considerations.” That is, the mere availability of new knowledge is assumed to be sufficient reason to distribute it to employees throughout the organization — regardless of whether they are satisfied with the knowledge they are currently using or even have the capability to use this new material. According to McElroy, second-generation KM approaches are primarily demand-driven. A good example is what human resource professionals call “just-in-time” training (JITT). Through JITT, employees can access training when they believe they need it to solve problems that concern them, rather than attend management-mandated workshops that may or may not provide them with timely information.

In addition, according to the KMCI knowledge life-cycle model that McElroy presents, high-quality knowledge evolves over time through dialogue within communities of practitioners who are committed to understanding what works best. Technological fixes, such as the one described above, are not a substitute for nurturing the essential social processes that contribute to developing new knowledge — they are an adjunct. It is this idea that McElroy tries to impress upon advocates of first-generation KM, who portray computer-based fixes as a main feature of KM rather than as a tool for facilitating it. Because of this limited view of KM’s applicability, it is not surprising that many executives have become skeptical of the discipline’s promise for delivering sustainable competitive advantage.

Knowledge-Friendly Policies

In its essence, The New Knowledge Management espouses the perspective that managers cannot directly manage many critical organizational processes, such as knowledge creation, but they can influence them by judiciously altering certain factors. Xerox’s Palo Alto Research Center (PARC) is one enterprise that has organized knowledge management processes around people’s natural behaviors. For example, because workers tend to congregate around coffee pots, the company has installed white boards and markers in those areas to assist people in capturing the knowledge that emerges through informal conversations. In addition, because studies at Xerox revealed that people also tend to engage in conversations in stairways, the company facilitated this process by widening those areas so coworkers can remain on the stairs and chat while others still have room to pass by.

Likewise, McElroy argues that corporate policies often unintentionally stifle knowledge creation by favoring efficiency, and that managers should scrutinize and modify processes to be “knowledge-friendly.” In the latter portion of the book, in his description of the Policy Synchronization Method (PSM), he alludes to some key policy levers for systematically redesigning organizations to facilitate knowledge processing and innovation. PSM helps managers do a baseline diagnostic assessment of the effectiveness of current knowledge-processing systems and then alter policies and processes to yield greater innovation in how knowledge is created.

The importance of this naturalistic view of husbanding organizational processes, as opposed to managing them, cannot be overstated. The simplistic industrial engineering notions of Fredrick W. Taylor and others once served the prevailing Newtonian/ Cartesian mental models of managers well, but that era is over. Today, managers are killing organizations by sacrificing innovation to the god of efficiency. We shouldn’t be surprised to learn that stagnant, ineffective processes are traceable to an organization’s failure to create new knowledge, or that the solution lies in finding innovative ways to harness people’s talents, or intellectual capital, rather than in installing new hardware and software. Historically, tools and technology have always worked best when used to augment people’s know-how and understanding. While technologies can often replace people in simple, routine situations, they can’t generate innovation in complex, dynamic environments — that’s where the real value of second-generation KM is most apparent.

Does McElroy find the ultimate answer for achieving high organizational performance? Probably not. But in this writer’s opinion, he convincingly points toward a direction where it may be found, when many other so called knowledge management gurus remain bewitched by the lure of first-generation KM solutions. Second-generation KM — and McElroy’s book — provide a viable conceptual framework for effectively linking KM to systems thinking and organizational learning. In doing so, it offers a promising way for us to create and sustain organizational success.

Steven Cavaleri, Ph. D., (cavaleri@ccsu.edu) is professor of management at Central Connecticut State University in New Britain, Connecticut. He also serves as editor of the journal The Learning Organization.

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Knowledge management — understood and practiced the right way — promises to make it all possible.

Not Information But Know-How

Yet something unfortunate has happened to the phrase “knowledge management.” Too many IT-bedazzled managers believe that the expression refers to the latest information-handling technology. Worse, they assume that the technology will do all the work of knowledge management for them.

Knowledge management is much more than the latest software, but beyond that caveat, it has proved difficult to define.

In Learning to Fly (Capstone Publishing, Inc., 2001), Chris Collison and Geoff Parcell maintain that knowledge management is about capturing, creating, distilling, sharing, and using not information but know-how—the internal knowledge that each member of an organization has accumulated over time and the documented knowledge the organization has compiled. And it’s not just know-how that matters — it’s also know-what, know-who, know-why, and know-when.

Imagine what might happen if we could get inside one another’s minds and tap the knowledge therein!

Arian Ward of Work Frontiers International offers the following perspective: “[Knowledge management’s] not about creating an encyclopedia that captures everything that anybody ever knew. Rather, it’s about keeping track of those who know the recipe, and nurturing the culture and the technology that will get them talking.” As Collison and Parcell point out, such definitions shift the emphasis away from the creation of vast knowledge repositories and toward strategies for increasing the mobility of the knowledge that’s inside people’s heads. That mobility enables teams, departments, and entire organizations to constantly learn, innovate, surmount new challenges, and achieve new successes. Thus true knowledge management is more about people than anything else. It entails a range of activities, all of which organizations can practice and master.

Put another way, knowledge management is about learning how we learn. Information technology may indeed be fast, powerful, and impressive, but at most it merely supports true knowledge management. Companies that fail to grasp this concept risk investing in expensive IT systems — only to suffer disappointment and unnecessary expenses when the technology proves unable to “manage knowledge” for them.

BP’s Road Map

Telling stories — of successes, failures, elation, and even despair — is among the most powerful of knowledge management tools. “Here’s what happened when I did X.” “Here’s what we learned when Y happened.” “Well, it’s settled: We’ll never do Z again!” Learning to Fly tells the story of BP’s journey toward learning how to “capture the knowledge of 100,000 of the world’s brightest people.”

But the book is more than just an interesting narration of what BP did to become a knowledge-management juggernaut. And it’s far from a brag-fest. Instead, Collison and Parcell offer it as a road map for other organizations that wish to embark on the same journey.

Two longtime knowledge management practitioners at BP, the authors not only share behind-the-scenes details from BP’s experience; they also provide a wealth of hints, tips, tools, and techniques that any company can apply. As they themselves admit, “You won’t find too much theory here.” Rather, this is a book about what the folks at BP have practiced and what they have learned from practicing it.

In addition, Collison and Parcell set out to structure the book in a way that would help readers navigate the information in it, plot their own course through it, and follow whatever line of thinking they found interesting. To that end, the authors instructed their publisher to lay out the text in ways that emulate web pages. There are links between pages to allow readers to follow their train of thought to the pages containing relevant material. Alternatively, readers can work their way through the book in the more conventional, linear manner.

Finally, the authors have liberally sprinkled “facilitator’s notes” throughout the book: advice from seasoned professionals on how to introduce knowledge-management practices into their own organization. They’ve also inserted “action zones,” in which they offer readers ideas for applying their new learning to their own situations.

Clearly, this book is meant to be manhandled, scribbled in, dog-eared, and, well, used. At its heart, however, lie two enduring truths:

1. Successful knowledge management means learning before, during, and after everything you do.

2. Successful knowledge management hinges on a company’s ability to create the kind of environment that enables people to get in touch with “those who know” and to “develop communities [of individuals] who act as guardians of the company’s knowledge.”

What follows is a sampling of the tools and techniques BP has used in service of these truths.

Learning Before: “Somebody Has Already Done It”

Learning before doing centers on what the authors call a “peer assist”: a structured, facilitated meeting or workshop with a specific purpose, to which you ask people from other teams to come and share their experiences, insights, and knowledge with your own team.

A peer assist can have one of any number of purposes, including targeting a specific technical or commercial challenge, identifying new lines of inquiry, or simply strengthening staff networks. The key, however, is to look across the company’s hierarchy — not up or down — to ensure the participants really are equals. As John Browne maintains, “People are much more open with their peers.”

How do you select participants? Look for people who will challenge your mental models and offer fresh options and new lines of inquiry. Consider people from other disciplines, businesses, and even companies. The broader range of experiences you gain access to, the more insights into your problem you’ll generate.

And how do you find these folks? Use these techniques:

• Look for people down the hall who are working on different projects.
• In a large company, use the firm’s intranet Yellow Pages.
• In a small company, look for interested and potentially valuable outsiders — a supplier, a customer, a fellow member of a professional association.
• Post an announcement well ahead of the scheduled time for your peer assist.

Learning During: “Let’s Stop and Reflect”

Learning during doing centers on holding what are normally called After Action Reviews (AARs) while you’re conducting the work process or effort in question (for more information about the AAR process, see “Emergent Learning in Action: The After Action Review” by Charles S. Parry and Marilyn Darling, V12N8). At BP, AARs consist of an open, short (20-minute), facilitated meeting during which participants answer four simple questions:

1. What was supposed to happen? 2. What actually happened? 3. Why were there differences between our intent and reality? 4. What did we learn? The keys to AARs? Hold them while all the participants are still present and their memories of the situation are fresh. And don’t forget to record the responses to the four questions, as well as any agreed-upon actions.

Learning After: “What Happened and How Will We Apply It?”

Learning after doing focuses your attention on ways to capture and (more important) transfer lessons from a project to new challenges. To do so, the authors recommend a simple, facilitated meeting that they call a “retrospect.”

More in-depth than an AAR, a retrospect is akin to conducting an analysis after a war, rather than after one of the battles. During the meeting (which should last anywhere from an hour for a simple project to two days for a complex one), you do the following:

• Revisit the objectives and deliverables of your project.
• Ask “What went well?” Then ask “Why?” several times.
• Ask “What could have gone better?” Then ask “Why?” several times.

Who should attend a retrospect? The authors recommend the project leader; key project-team members; and the customer, client, or sponsor. Most important, make sure everyone understands that the meeting’s purpose is not to assign blame or praise, but to ensure that future projects go even better than this one. See that everyone has a fair share of “airtime.”

Creating a Knowledge Management Environment

Practicing learning before, during, and after a project lies at the core of knowledge management. By building the right organizational environment, you can make these practice sessions even more potent.

As a first step, make sure everyone has the tools needed to share documents and knowledge. Then, focus on encouraging new behaviors: specifically, asking for help, listening actively, challenging one another, nurturing relationships, and building trust.

These behaviors reflect new beliefs — such as “It’s okay to request assistance” — that can create a sense of discomfort within some organizational climates. But by putting new behaviors into action, people can begin gradually reshaping even their deepest beliefs.

BP knows this first-hand — and has the success to prove it.

Lauren Keller Johnson is a freelance writer living in Lincoln, MA.

The post Capturing the Knowledge of “100,000 of the World’s Brightest People” appeared first on The Systems Thinker.

The advent of second-generation KM, then, can be seen as a convergence in thinking between the organizational learning and knowledge management communities. In effect, second-generation KM has emerged as an implementation strategy for organizational learning – a practitioner’s model for how to help organizations increase their capacity to learn, innovate, and adapt to change. Unlike its first-generation ancestry, second generation thinking is more concerned with the evolution of knowledge, not just its mechanical application in practice.

Two Levels of Learning

In a breakthrough article entitled Teaching Smart People How to Learn (Harvard Business Review, May/June 1991), Harvard Business School professor Chris Argyris explained the difference between what he called single-loop and double-loop learning in the following way: “To give a simple analogy: a thermostat that automatically turns on the heat whenever the temperature in the room drops below 68 degrees is a good example of single-loop learning. A thermostat that could ask, ‘Why am I set at 68 degrees?’ and then explore whether or not some other temperature might more economically achieve the goal of heating the room would be engaging in double-loop learning.”

During the course of normal experience, we invoke internally maintained rules to decide how to respond to events. When the traffic light turns green, we go; when it’s red, we stop. In this context, the term rules means knowledge, in that all knowledge can be expressed in the form of if/then statements. Conditions that satisfy the if side of a rule trigger the then side (if the traffic light turns green, then release the brake, depress the accelerator, and proceed carefully ahead).

Organizational knowledge is similarly configured. Rules inform workers of what to do in defined situations, such as if the customer wants x, then do y followed by a, b, and c.

By contrast, in double-loop learning, people not only reference these rules but constructively challenge such rote responses. In the human mind, this kind of double-loop thinking leads us to construct alternative scenarios in which we play out likely outcomes. We can then test promising new ideas and potentially choose to override or replace the prescribed response. Depending on how well the new rule fares in practice, we either reinstate the old one or replace it with the new, more successful “habit.” Our knowledge (i.e., the rules that produce successful outcomes in practice) evolves accordingly.

The extent to which an organism engages in healthy rule-making and knowledge innovation will largely determine its success in life. An agent (e.g., person, animal, community, economy, etc.) that rarely tests its rules will tend to perform more poorly in practice than one that constantly challenges, upgrades, and refreshes its rules. The same is true for human organizations. A business that rarely revises its approach to the marketplace or its operating processes will tend to ossify and atrophy. On the other hand, companies that engage in healthy levels of rule-making and rule-revising are inherently more capable of adjusting to changes in their environment. Indeed, organizational agility depends, to a large extent, on just how well an organization’s learning system works.

That is the principal aim of second-generation KM – to enhance an organization’s ability to engage in constructive levels of double-loop learning. In a sense, what we’re talking about is double-loop KM, an OL practitioner’s framework for helping organizations, not just individuals, learn.

Double-Loop KM Understanding Argyris’s notion of single versus double-loop learning is an important early step in appreciating the fundamental differences between first and second-generation KM. Only first generation KM assumes that current knowledge is valid. The goal of such approaches is to optimize the delivery of existing organizational rules to workers so that they can function successfully in their operating environments.

Organizational agility depends, to a large extent, on just how well an organization’s learning system works.

This is why technology has played such a conspicuous role in knowledge management to date. After all, computers and telecommunications networks are unparalleled in their ability to deliver information to people, where and when it’s needed. Thus, conventional knowledge management practice boils down to little more than getting the right information to the right people at the right time, using tools such as document management, imaging, data warehousing, data mining, and information-retrieval systems. While useful, this is all single-loop learning.

Other conventional KM practices, including some attempts to build communities of practice and corporate intranets, also focus on knowledge sharing and transfer. Once again, the target of this kind of intervention is single-loop learning: The purpose of sharing knowledge is merely to distribute existing organizational rule sets as widely as possible so that workers can employ “best practices” on the job front. But although knowledge sharing has some value to an organization, it completely side-steps the question of where organizational knowledge comes from to begin with – not to mention where knowledge resides within an organization and how it is expressed.

Knowledge Structures and Rules

One of the fundamentals of second-generation KM is the concept of knowledge structures – codified expressions of collective knowledge. For millennia, human civilizations have been embedding knowledge in myths, rituals, dance, and other cultural artifacts. In turn, these structures, along with our societies’ institutions, reveal much about our cultural values, beliefs, and rules, and the ways in which they have evolved over time. The codification of collective knowledge facilitates knowledge transfer from one generation to the next without individuals’ having to rely on the frailties of human memory. Cultural artifacts can thus be seen as a record of organizational knowledge. From this perspective, although we might have thought that knowledge management was new, as defined by second-generation practitioners, it is as old as the hills.

How does the concept of knowledge structures apply to the corporate world? Well, business processes, such as how to handle a mortgage application, are really nothing more than codified expressions of procedural knowledge (know-how). Business strategies, such as whether to be in the mortgage business in the first place, are codified expressions of declarative knowledge (know-what). All organizational knowledge, then, is expressed in the form of procedural and declarative rules that are recorded in various organizational knowledge structures. Some knowledge is expressed in literal structures such as business plans and policies-and-procedures manuals, while other knowledge is acted out in the processes or chain-of-command structures that we follow.

Although many modern-day knowledge structures take the form of information systems, documentation, videos, and other recorded representations, they are just as commonly found in corporate stories, repeated patterns of behavior, and leadership styles. Regardless of where knowledge is held, the distinction between procedural and declarative knowledge is important for two reasons. First, in order to double-loop learn, an organization must know what it knows, as well as see and recognize its own knowledge as such. Understanding that knowledge is expressed in the form of rules that are contained in culture, business strategies, processes, and organizational schemes makes it easier for practitioners to discover and articulate what their organizations know.

Second, comprehending that declarative knowledge drives procedural knowledge can dramatically increase an organization’s rate of learning and innovation. For example, IBM’s declarative knowledge of what the market for e-commerce consists of will determine its approach for how to engage customers and competitors in the marketplace. Ultimately, every process that employees follow in practice can be traced to collectively held paradigms about the e-commerce market. The slightest error in any underlying declarative assumptions can render whole operating divisions obsolete or entire value chains irrelevant in the blink of an eye. Therefore, it is important to know that the leverage for making lasting, large-scale change is in altering declarative knowledge rather than in tinkering with procedural knowledge.

No discussion of knowledge management would be complete without addressing the persistent question of how knowledge differs from data, information, and wisdom. Based on the definitions of procedural and declarative knowledge given above, all instances of data, information, knowledge, and wisdom can be categorized as either knowledge of fact (declarative rules) or knowledge of practice (procedural rules). Even the most sterile summary of statistics taken from a laboratory experiment convey someone’s knowledge of what happened, which is declarative knowledge.

Rather than differentiate among data, information, knowledge, and wisdom, it is more constructive to focus on gradations in the value of knowledge. Measuring the value of a given set of procedural and/or declarative rules boils down to evaluating how well they are serving the organization in meeting its goals. Using this criterion, what is considered low-grade “data” one day could easily become high grade “wisdom” the next without any change in the actual content. If only we knew then what we know today about the space shuttle Challenger’s flawed “o” rings, that sad chapter in the history of the U. S. space program could have been averted – and yet, the wisdom that speaks volumes to us now is identical to the data that NASA had at the time.

Having established second-generation KM’s view of the form that knowledge takes and the containers in which it is stored, the next concept of fundamental importance is the process by which new rules come into existence.

The Knowledge Life Cycle

To address the shortcomings of the earlier phase of knowledge management, experts in the field have developed a three-phase model of the knowledge life cycle: Knowledge Production, Knowledge Validation, and Knowledge Integration (see “Organizational Knowledge Production”). It is here, in particular, that the influence of organizational learning theory has had its strongest effects on knowledge management. Until recently, KM’s basic assumption has been that “knowledge exists” -we need only capture, codify, and share it. Learning, or knowledge creation, never really entered into the picture. By embracing the OL community’s notion of collectively held knowledge and group learning, a more complete life-cycle view of the subject has emerged. From this perspective, knowledge exists only after it has been produced; at that point, we can capture, codify, and share it.

In this view of knowledge creation, during Knowledge Production, organizations generate new knowledge through mostly spontaneous interactions among individuals and groups. These interactions lead to the formation of new “knowledge claims,” or procedural and/or declarative rules in their seminal stage. In the Knowledge Validation phase, the group measures the new or changed rules against the effectiveness of current knowledge to the organization. The satisfaction of validation criteria often leads to the formal adoption of this new knowledge in the form of procedural and declarative rules expressed in one or more knowledge structures.

ORGANIZATIONAL KNOWLEDGE PRODUCTION

Organizational learning leads to the production of organizational knowledge. Collectively held knowledge is, in turn, expressed in the form of Knowledge Structures.

The third phase, Knowledge Integration, involves operationalizing the new knowledge. A new business process, for example, doesn’t instantly supplant yesterday’s standard operating procedures. Getting large numbers of workers to follow a newly conceived process calls for an act of willful transformation. Integrating this new procedural knowledge therefore entails the deliberate abandonment of one set of operating rules in favor of another.

To help illustrate how this cycle works, think of a well-defined business process in your own department or unit. It might be the mortgage application process in a bank, the order fulfillment process in a manufacturer, or some other workflow that you can clearly envision from start to finish. What you’ve conjured up in your mind is a chunk of procedural knowledge that is expressed in practice by the patterns of work that people conventionally follow. This knowledge may also be expressed in other knowledge structures, such as written procedures manuals and training programs.

Now, think back to how long this process has been practiced in its current form. Next, try to visualize the pattern of practice that preceded it. More important, see if you can reconstruct the circumstances by which the preceding knowledge was rejected and the new knowledge embraced. Where did the new workflow idea come from? How was it defined? What shape did it take as it moved toward the validation phase? Your answers to these questions will characterize your own organization’s Knowledge Production phase and the people, processes, and technologies that made it work. Next, ask yourself, how was the new process evaluated? What criteria were used to measure it against then-current operating procedures? Who performed the evaluation? Was it a special-case effort, or are new knowledge claims systematically evaluated? Here again, your answers will characterize your organization’s Knowledge Validation phase. And finally, trace the circumstances by which the new business process was formally operationalized. By doing so, you’ve just described the Knowledge Integration phase in your own company. You have now traced the genesis of current procedural knowledge across all three stages of its evolutionary cycle.

The value of this exercise is that it can not only assist you in understanding the three-phase life cycle, but can also help you make the crucial distinction between knowledge “content” management (first-generation KM) and knowledge “process” management (second-generation KM). By focusing on improving the fundamental knowledge processes at work behind all of an organization’s knowledge structures, second-generation KM helps make best practices in knowledge creation, not just codification and sharing, available to everyone in the organization.

With this life-cycle framework in mind, we can see the majority of first-generation KM as Knowledge Integration work, with little or no focus on Knowledge Production or Validation. But even the best Knowledge Integration work produces little meaningful organizational learning. Because the production of new knowledge lies at the heart of organizational learning, it’s easy to understand why KM and OL have evolved on such separate paths over the years.

The secret of successful doubleloop organizational learning can be found in the combination of Knowledge Production and Validation. Of particular importance are the processes by which new ideas are formed and subjected to group scrutiny for potential adoption. Ideas that survive the test and are then embraced by the organization can be seen as the progeny of organizational learning. Once born, these ideas then become systematically codified, expressed, and diffused throughout the organization in the form of new procedural and/or declarative rules. Training programs and new personnel policies are common examples of how new organizational knowledge is consciously embedded in one or more knowledge structures in the hope that it will spread effectively throughout the organization.

Implications for Practice

What are some of the tactical dimensions of this new approach to organizational learning? What specific steps can practitioners take – on Monday morning – to improve the learning performance of their collective constituents? Below are some examples of initiatives that practitioners can take to put double-loop knowledge management to work.

Taking Stock of Knowledge Structures. Creating an inventory of an organization’s knowledge structures by documenting where procedural and declarative knowledge lies is among the first steps in the practice of double-loop knowledge management. Unlike first-generation KM, which selectively focuses on the creation of artificial knowledge structures (computer-based systems, prescribed communities of practice, etc.), second-generation practice seeks, first and foremost, to understand and enhance existing knowledge structures in all of their forms, both natural and artificial. The result is an end-to-end view of organizational knowledge and where it resides.

Profiling Knowledge Processes. Using the three-phase life cycle as a guide, practitioners can then survey existing knowledge processes as a baseline indicator of how well the organization is currently learning. For example, businesses that relegate most of their knowledge production and validation functions to senior management can be characterized as dysfunctional learners. This categorization might lead the company to recognize the need for bottom-up innovation programs, thereby increasing the rate of organizational learning and knowledge production. The organization could in turn take remedial steps to establish critical knowledge processes in places where they might be missing or incomplete. In manufacturing, for example, the implementation of continuous improvement programs such as Kaizen has led to widespread advances in productivity at companies throughout the world. Unlike conventional top-down management programs, Kaizen initiatives tap directly into the workforce, are bottom-up in their orientation, and continuously produce innovations at a rate that exceeds that of even the most talented management teams.

Expressing Knowledge in Standard Form. Another fundamental tool in every practitioner’s toolkit is a technique for converting organizational knowledge expressed in different ways into a standard form. For example, a business strategy is reducible to all of the underlying declarative knowledge that an organization regards as true and valid about itself and the marketplace. This might include how the market is structured, what trends are in play, and knowledge of how competitors are approaching the same opportunities. Why not make these rules explicit? More important, why not subject them to constant scrutiny by making them plainly visible and, therefore, candidates for improvement? This is precisely the kind of process organizations need to implement to receive the benefit of bottom-up innovation.

Tools and techniques for expressing both tacit and explicit organizational knowledge are now starting to appear in commercial form. One such tool, Knowledge Harvester (LearnerFirst, Inc. in Birmingham, Alabama), provides a technique and a “language” that practitioners can use to express organizational knowledge in a standardized way. LearnerFirst takes commonly expressed organizational knowledge and converts it into procedural and declarative statements. Over time, tools of this sort will be seen as fundamental to the practice of second generation KM. As such, they will be used not only to catalogue existing organizational knowledge, but also to determine the extent to which an organization is actually learning. Dysfunctional learning organizations, for example would exhibit relatively stagnant rule sets; highly adaptive firms, by contrast, would display regular turnover in rules and, hence, higher rates of innovation.

Measuring Return on KM Investment. As organizational knowledge changes or evolves, evidence of this learning can be seen in the form of new rules, retired rules, more rules, fewer rules, or different combinations of new and old rules. By tracking the evolution of rules held by an organization at different points in time, practitioners can quite literally measure rates of learning and innovation. Indeed, returns on investments made in KM will increasingly be measured by their effects on rule-making and rule-set refresh rates, in addition to their tangential effects on business performance.

Measuring return on investment from KM and OL initiatives, then, should occur in two ways: 1) by tracking the evolution of rules held in knowledge structures, and 2) by measuring related changes in the performance of the organization. Knowledge management investments that lead to improvements in business performance, such as increased productivity, lowered costs, or higher revenue, can be declared successful; those that do not should be judged accordingly.

Linking Learning and Knowledge

Second-generation knowledge management explicitly links organizational learning with the concept of organizational knowledge (See “Some Principles of Double-Loop Knowledge Management”). In particular, it offers fresh perspectives on how knowledge is created and diffused in organizations that are germane to both disciplines. Indeed, the new field of second-generation, double-loop knowledge management not only embraces organizational learning as a concept, but also offers practical tools and techniques for what to do about it on Monday morning.

The greatest challenge we face as practitioners of double-loop KM and organizational learning is to create the conditions in which new ideas can be freely expressed and thoughtfully considered at an organizational level. Doing so would seem to hinge on making knowledge processes explicit in our organizations, nurturing well-running knowledge processes behind all of our knowledge structures, and supporting bottom-up participation in all stages of the knowledge life cycle. Thus, from a 21st-century perspective, our historical practice of relegating knowledge creation to the hands of a few will be seen in retrospect as one of the profound follies of our time – a grand succession of missed opportunities of enormous proportions. Fortunately, we now have the tools and knowledge to rectify this error as we build the organizations of the future.

Mark W. McElroy is a principal in IBM’s Knowledge Management Consulting Practice. In addition, he serves as chairman of the Knowledge Management Consortium’s KM Modeling Standards Committee. Mr. McElroy is also a board member of the Sustainability Institute, a think-tank in Vermont that applies system dynamics tools to the study of economic and social issues in business and industry. He can be reached at mcelroy2@us.ibm.com.

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