Information Management for the Intelligent Organization

Chapter 1: The Intelligent Organization

"What magical trick makes us intelligent? The trick is that there is no trick. The power of intelligence stems from our vast diversity, not from any single, perfect principle. Our species has evolved many effective although imperfect methods, and each of us individually develops more on our own. Eventually, very few of our actions and decisions come to depend on any single mechanism. Instead, they emerge from conflicts and negotiations among societies of processes that constantly challenge one another."

(Marvin Minsky 1986, The Society of Mind, Section 30.8)


Selections from Chapter 1

Organizations are societies of minds. Actions and decisions are not the simple outcome of any single, orderly activity, but emerge from an ecology of information processes. A diversity of participants and points of view collaborate together as well as challenge each other. We now recognize this dynamic, open character of organizations. Yet for a long time, we cherished a static view of organizations. Organizations were places where we went to work everyday. They could be counted on to produce the goods and services we want, and some of them were responsible for preserving and protecting structures and values that underpin our society. Their very stasis and stability was a source of comfort. Organizations saw themselves almost as fortresses, with walls and boundaries that etched their domains of activity and influence. From time to time, they would open the gates to send out produce or to receive material, but this was not its primary concern. Early students of organizations made the simplifying assumption that organizations were closed systems, and the effective organization was one that could buffer its operations from the vicissitudes of the outside world and so concentrate on improving its internal form and function. For most purposes, the external environment was a given in the short run: markets changed sluggishly, and could sometimes be manipulated; technologies moved in small steps and could be assimilated incrementally; relationships with other organizations were clear-cut and cautious; economic conditions turned in periodic cycles that could occasionally be predicted. On the whole, many organizations, especially the larger ones, felt that they were in control of their own destinies.

In recent years, this static representation of organizations has become a relic. Today's organizations are no longer circumscribed by walls and boundaries. Their borders are porous, through which material, energy and information continuously flow. Instead of trying to do everything, they now parcel out their work to other organizations so that each can maximize on its strengths and advantages. A significant proportion of organizations do not live long. Some fail and disappear altogether, while others pursue alliances and linkages to increase their leverage and survivability. They spin networks that include competitors, customers and suppliers. Rather than fortresses, they are more like species of organisms seeking sustenance and growth in a dynamic environment. Their credo is to evolve or else perish. Their eyes are perpetually fixed on the external environment, watching markets shift from day to day, industries jostle to reconfigure themselves, technological innovations announce themselves at an unremiting pace, and government policies constrain or create options. Today's organizations realize that aiming to insulate themselves from their environments is a lost cause. Instead, they now behave as complex, open systems that share many features with living biological systems. Above all, they recognize that their survival and growth is ultimately conditioned by their capacity to learn and adapt to a changing environment.

In this chapter, we take a closer look at the relationship between organizations and environments - why are some organizations able to survive and grow decade after decade, while something like a third of the companies in the Fortune 500 list have disappeared over the last five years? We will suggest that survivability is dependent on the organization's ability to process information about the environment, and to turn this information into knowledge that enables it to adapt effectively to external change. We will suggest that such adaptability through learning is the hallmark of the intelligent organization. Organizational learning is the key to intelligent organizational behavior in a fast-changing environment.

Organizations and Environments

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Environment as a source of information

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Environment as source of resources

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Environment as source of variation

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Organizational Information Processing



Organizations as information processing systems

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Organizations as decision making systems

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Organizations as interpretation systems

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The Intelligent Organization

The picture we have so far is of an organization that behaves as an open system which takes in information, material and energy from the environment, transforms these resources into knowledge, processes, and structures that produce goods or services which are in turn consumed by the environment. The relationship between organizations and environment is thus both circular and critical: organizations depend on the environment for resources and for the justification of their continued existence. Because the environment is growing in complexity and volatility, continuing to be viable requires organizations to learn enough about the current and likely future conditions of the environment, and to to apply this knowledge to change their own behavior and positioning in a timely way.

Students of organizations have wrestled with the concept of organizational intelligence for several decades. Wilensky (1967) discussed organizational intelligence in terms of the gathering, processing, interpreting, and communicating of the information needed in decision making processes. Information is not only a source of power, but a source of confusion - information oversupply has exacerbated the problem of intelligence. The roots of organizational intelligence failures can often be traced to doctrines, structures, and problems and processes that increase distortion and blockage. In Wilensky's analysis, much of an organization's defence against information pathologies lies in the managers' attitude towards knowledge, and in the information specialists' capacity to influence strategic discourse. March and Olsen (1979) believed that organizational intelligence is built on two fundamental processes: `rational calculation,' and `learning from experience.' Rational calculation is the choice of alternatives based on an evaluation of their expected consequences according to preferences. It looks ahead into the future to anticipate outcomes. Learning from experience is the choice of alternatives based on rules developed from an accumulation of past experience. It looks backwards at history to find guidance for future action. March and Olsen observed that as we have come to recognize the limitations on rational calculation, interest in the potential for organizational learning as a basis for organizational intelligence has increased. Organizations and the people in them learn through their interactions with the environment - "They act, observe the consequences of their action, make inferences about those consequences, and draw implications for future action. The process is adaptively rational." (March and Olsen 1979, p. 67)

Recently, Quinn described an intelligent enterprise as "a firm that primarily manages and coordinates information and intellect to meet customer needs." (Quinn 1992, p. 373) The intelligent enterprise depends more on the development and deployment of intellectual resources than on the management of physical and fiscal assets. Its functions are disaggregated into manageable intellectual clusters that Quinn calls service activities. Information technology has made it possible to delegate and outsource many of these service activities to other organizations. Instead of focusing on products, the intelligent enterprise excels in a few core knowledge-based service activities critical to its customers and surrounds these with other activities necessary to defend the core. Then it uses advanced information, management, and intelligent systems to coordinate the many other diverse and often dispersed activity centers needed to fulfill customer needs. Apple Computer Inc. is an example of creating value by leveraging on a few critical knowledge-based service activities. The Apple II computer was primarily a software and marketing breakthrough that helped to launch the PC revolution. The machine retailed for about $2000 but cost less than $500 to build, with 70 percent of the components purchased from outside. Apple did not try to manufacture the microprocessors, circuit boards, housings, keyboards, monitors or power supplies that went into the computer. All of these components were outsourced while Apple concentrated on concept design, software, logistics, systems integration, and product assembly (Quinn 1992, p. 42). Until today, Apple's human-computer interface design guidelines still set the standards for elegance and user friendliness. Outsourcing was not limited to hardware. Apple worked with Dan Breklin to develop Visicalc (the first spreadsheet) and for over a year, Apple II was the only computer to support Visicalc. The Apple brand name and logo was designed by the public relations agency Regis McKenna to help project Apple's image at a time when the company had almost no sales. For product distribution, Apple joined with Bell & Howell, a reputed supplier to the education market, to help place Apple products in schools. As a result of its concentrating on a few knowledge-adding services while developing partnerships in complementary areas, Apple was able for many years, to attain sales per employee figures that were two to four times higher than its competitors.

Haeckel and Nolan(1993) define an organization's intelligence as its "ability to deal with complexity, that is, its ability to capture, share, and extract meaning from marketplace signals." (Haeckel and Nolan 1993, p. 126) An organization's complexity is in turn a function of how many information sources it needs, how many business elements it must coordinate, and the number and type of relationships binding these elements. According to their analysis, an organization's `intelligence quotient' is determined by three critical attributes: the ability to access knowledge and information (connecting); the ability to integrate and share information (sharing); and the ability to extract meaning from data (structuring). Connecting means that information sources, media, locations, and users are linked in such a way that accurate information can be captured and made available to the right users at the right time and place. Sharing means that people in the organization can share data, interpretations of the data, as well as their understanding of the core processes of the organization. Structuring means that insight or meaning is obtained by matching and relating information from multiple sources so that some form of pattern or trend emerges. Structuring is achieved by creating information about information, for instance, how data are organized, related and used. Classification categories, indexes, tables of contents, and data models are some examples of filtering and structuring data. Haeckel and Nolan believe that structuring holds the most potential for the strategic exploitation of information. To illustrate a firm applying the connecting-sharing-structuring loop, they describe the system used by Wal-Mart and its suppliers to replenish stocks. For example, Wal-Mart transmits detailed data about the day's sales to its jeans supplier Wrangler every evening. Sharing is not limited to data: the two firms also share a model that interprets the meaning of the data, and software applications that act on the interpretation to specify quantities of jeans of the right sizes and colors to be sent to particular stores from specific warehouses. As fashion seasons or pricing patterns change, the shared data model is adjusted accordingly, thus allowing both organizations to learn and adapt.

There are two distinct meanings to the concept of intelligence: the possession of knowledge and the creation of knowledge (Gregory 1981, 1994). The possession of knowledge provides a pool of knowledge that can be called upon to solve problems and give understanding. The creation of knowledge takes place when novelty is generated to solve new problems for which adequate solutions cannot be found in the knowledge base. Indeed, the creation of successful novelty is a convincing mark of intelligent behavior. The context of intelligent behavior is thus problem solving, and problem solving implies the pursuance of goals and objectives. The requirement for novelty is linked to the appearance of new problems, situations, and experiences. Intelligence may be conceived as a quality of behavior, behavior that is adaptive in that it represents effective ways of meeting the demands of environments as they change (Anastasi 1986). We see therefore that intelligent behavior is both goal-directed and adaptive (Sternberg 1982, Sternberg and Detterman 1986), and it will be the capacity of organizations to possess, create, and apply knowledge that will make the crucial difference.

An organization works with three classes of knowledge: tacit knowledge, rule-based knowledge, and background knowledge (Table 1.1). Tacit knowledge consists of the hands-on skills, special know-how, heuristics, intuitions, and the like that people develop as they immerse in the flow of their work activities. Tacit knowledge is deeply rooted in action and comes from the simultaneous engagement of mind and body in task performance. Tacit knowledge is personal knowledge that is hard to formalize or articulate (Polanyi 1966, 1973). The transfer of tacit knowledge is by tradition and shared experience, through for example, apprenticeship or on-the-job training. Tacit knowledge in an organization ensures task effectiveness - that the right things are being done so that the work unit could attain its objectives. It also provides for a kind of creative robustness - intuition and heuristics can often tackle tough problems that would otherwise be difficult to solve. Whereas tacit knowledge is implicit, rule-based knowledge is explicit knowledge that is used to match actions to situations by invoking appropriate rules. Rule-based knowledge guides action by answering three questions: What kind of situation is this? What kind of person am I or What kind of organization is this? and finally, What does a person such as I, or an organization such as this, do in a situation such as this? (March 1994) Rule-based knowledge is used in the design of routines, standard operating procedures, and the structure of data records. Rule-based knowledge enables the organization to enjoy a certain level of operational efficiency and control. It also promotes equable, consistent organizational responses. The third kind of organizational knowledge is background knowledge. This is knowledge that is part of the organizational culture and is communicated through oral and verbal texts such as stories, metaphors, analogies, visions, and mission statements. Background knowledge supplies the mindset or worldview by which people in the organization understand particular events, actions, objects, utterances, or situations in distinctive ways (Morgan 1986). Background knowledge draws the cognitive context for the construction of reality and endows meaning on the organization's actions and activities. It promotes commitment through the creation of shared meanings and values. All three forms of knowledge can be found in any organization. The intelligent organization however, is skilled at continuously expanding, renewing, and refreshing its knowledge in all three categories. The intelligent organization promotes the learning of tacit knowledge to increase the skill and creative capacity of its employees, takes advantage of rule-based knowledge to maximize efficiency and equability, and develops background knowledge to unify purpose and meaning in its community. In effect, the intelligent organization has mastered a fourth class of knowledge - a higher order or meta-knowledge - that it uses to create, integrate, and invigorate all its intellectual resources in order to achieve superior levels of performance.

Is the kind of intelligent organization we have described an unattainable goal or do such firms exist in reality? We believe that examples of intelligent knowledge creation may be found in Japanese companies like Canon, Honda, Matsushita, NEC, and Sharp. These companies are widely admired for their ability to innovate continuously, recognize and respond swiftly to customer needs, dominate technologies while they are still emerging, and bring new high-quality products to market with impressive speed. For example, Canon reinvented the 35mm camera, pioneered the personal photocopier and color copier, invented the laser printer and inkjet printer, and is now working on using ferroelectric liquid crystals for large flat panel displays. Judged by the number of US patents granted, Canon can claim to be the world's most consistently creative company - for a fifth of the R&D budget, Canon has obtained about as many patents as IBM (Johnstone, 1994a). Or consider Honda's history of agile adaptiveness: it gained a late but successful entry into the highly competitive automobile market, won victory in the motorcycle war against an established leader (Yamaha), and developed its own automotive engine that set new standards in fuel-efficiency and pollution control. Many regard Honda as one of the best managed companies in the world (Pascale 1990). A Japanese scholar explains the success of companies like Canon, Honda and Matsushita:

The centerpiece of the Japanese approach is the recognition that creating new knowledge is not simply a matter of "processing" objective information. Rather, it depends on tapping the tacit and often highly subjective insights, intuitions, and hunches of individual employees and making those insights available for testing and use by the company as a whole. The key to this process is personal commitment, the employees' sense of identity with the enterprise and its mission. Mobilizing that commitment and embodying tacit knowledge in actual technologies and products require managers who are as comfortable with images and symbols ... A company is not a machine but a living organism. Much like an individual, it can have a collective sense of identity and fundamental purpose. This is the organizational equivalent of self-knowledge - a shared understanding of what the company stands for, where it is going, what kind of world it wants to live in, and, most important, how to make that world a reality. ... In the knowledge-creating company, inventing new knowledge is not a specialized activity - the province of the R&D department or marketing or strategic planning. It is a way of behaving, indeed a way of being, in which everyone is a knowledge worker - that is to say, an entrepreneur. (Nonaka 1991, p. 97)

The intelligent organization adopts a holistic approach to knowledge management that successfully combines tacit, rule-based, and background knowledge at all levels of the organization. Tacit knowledge is cultivated in an organizational culture that motivates through shared vision and common purpose. Personal knowledge is leveraged with explicit knowledge for the design and development of innovative products, services and processes. Strategic vision and operational expertise are fused in creative action.

Intelligence Through Organizational Learning

An intelligent organization pursues its goals in a changing external environment by adapting its behavior according to knowledge about its external and internal settings. In other words, an intelligent organization is a learning organization that is skilled at creating, acquiring and transferring knowledge, and at modifying its behavior to reflect the new knowledge and insights (Garvin 1993). Learning thus begins with new knowledge and ideas which may be created in-house, or may come from external sources, but must be applied to change the organization's goals and behaviors in order for learning to be complete. Failure to learn often means failure to survive: nearly 30 percent of the corporations in the Fortune 500 list of five years ago are missing today (Pascale 1990); and for every successful turnaround there are two declining firms that do not not recover (Howe 1986). When the Royal Dutch Shell Group surveyed 30 firms that had been in business for over 75 years, it attributed their longevity to "their ability to live in harmony with the business environment, to switch from a survival mode when times were turbulent to a self- development mode when the pace of change was slow. ... Outcomes like these don't happen automatically. On the contrary, they depend on the ability of a company's senior managers to absorb what is going on in the business environment and to act on that information with appropriate business moves. In other words, they depend on learning." (de Geus 1988, p. 70) Much of an organization's learning is from past experience. After the problem-plagued launch of their 737 and 747 planes, Boeing formed an employee group called Project Homework to compare the development of the 737 and 747 with that of the 707 and 727, hitherto two of the firm's most lucrative planes. After working for three years, Project Homework identified hundreds of lessons learned and recommendations. Some group members were moved to the 757 and 767 start-ups which eventually produced the most succesful, error-free launches in Boeing's history (Garvin 1993). In another example of learning from the past, British Petroleum established a five-person project appraisal unit that reported directly to the board of directors. Every year, the unit reviewed six major investment projects, wrote them up as case studies, and derived lessons to guide future planning. This form of review is now done regularly at the project level. (Gulliver 1987)

Single-loop and Double-loop Learning

Effective learning must stretch beyond detecting and correcting past errors. Sometimes, basic questions about the norms, policies, and goals of the organization need to be answered afresh. In a classic discussion, Argyris and Schon (1978) describe organizational behavior as being governed by the organization's theory of action which includes the norms for organizational performance, strategies for achieving norms, and assumptions which bind strategies and norms together. Organizational learning takes place when members of an organization respond to changes in the external and internal environments by detecting and correcting errors between outcomes and expectations. Error correction is through modifying organizational strategies, assumptions or norms in order to bring outcomes and expectations back into line. The altered strategies, assumptions or norms are then embedded into the organization's memory. Two modes of organizational learning are possible. Learning is single-loop when the modification of organizational action is sufficient to correct the error without challenging the validity of existing organizational norms. In other words, there is a single feedback loop between detected outcomes to action which is adjusted so as to keep performance within the range set by organizational norms. The goal of single-loop learning is therefore to increase organizational effectiveness within existing norms. Learning is double-loop when error correction requires the modification of the organizational norms themselves, which in turn necessitates a restructuring of strategies and assumptions associated with these norms. Learning in this case is double-loop because a double feedback loop connects error detection not only to organizational action but also to the norms. The goal of double-loop learning is therefore to ensure organizational growth and survivability by resolving incompatible norms, setting new priorities, or restructuringing norms and their related strategies and assumptions. While single- loop is adaptive and is concerned with coping, double-loop learning is generative learning and has to do with creating new mindsets.

Figure 1.1 Single and Double Loop Learning

Many organizations have become quite good at single-loop learning - they measure their performance according to objectives, and correct deviations by changing operational procedures. Far fewer organizations are adept at double-loop learning, and not many organizations challenge their own norms, goals or policies in relation to their changing environments. If budgetting is the archetypal mechanism for single-loop learning, then strategic planning is the tool for double-loop learning. Current uncertainty about the value of strategic planning may reflect as much on the inability of organizations and their managers to engage in generative learning as on the inherent difficulty of strategic planning. Royal Dutch/Shell benefited from double-loop learning through its preparedness for the 1973 oil crisis. From its scenario planning exercises, Shell was able to change the conceptual frames of reference that its managers used to perceive reality about the world (Wack 1985). Shell's managers have been assuming that oil demand will continue to grow at rates higher than GNP in a calm political environment where oil supply was unproblematic, a set of norms that the managers had taken for granted for some time. The planning scenarios forced them to challenge these norms and to think about a low-growth world where oil consumption was increasing more slowly than GNP, where oil producers were reaching the limits of their capacities and were reluctant to raise output further because they were unable to absorb the additional revenues. As a result, Shell management was better prepared for the 1973 oil shock, and was able to more quickly revise its assumptions and strategies to respond to the new realities of tight oil supply-demand.

Although many organizations realize that change and learning are needed, they have difficulty stepping out of their existing mental models to learn from the experience of change. Organizational learning should not be equated with organizational change. Incremental change based on existing assumptions and parameters does not constitute learning. Instead, the intelligent organization learns to change as well as learn from change (McGill and Slocum 1994). The key is to unlearn the past, discard processes and practices that are previously known or believed to be smart. Intelligent organizations learn by assimilating their experiences with customers, competitors, partners, and so on, and using this knowledge to rejuvenate their mental frames of reference. McGill and Slocum (1994) distinguish between four kinds of organizational intellects: the knowing organization, the understanding organization, the thinking organization, and the learning organization. The knowing organization is dedicated to finding the `one best way' to do business. The understanding organization believes in a `ruling myth' and uses strong cultural values to guide actions and strategies. The thinking organization sees business as a series of problems that need to be solved or fixed. Finally, the learning organization sees every business experience as an opportunity to improve - it models learning, encourages experimentation, and promotes dialogue.

Future Learning

It is not enough to learn from the past, the intelligent organization must also be able to learn about the future. Hamel and Pralahad (1994) call learning about the future developing `industry foresight' and assuming `intellectual leadership.' Developing foresight starts by gaining a deep understanding of the trends and discontinuties in technology, demographics, government regulation, and social lifestyles - forces that will draw the competitive space of the future. Developing foresight is creating a point of view of the future that answers three key questions: What new kinds of benefits for customers or clients should the organization provide in the future? What new competencies are needed to offer these benefits? and How will the interface with customers or clients need to be redesigned? Identifying future opportunities requires not only a profound understanding of the underlying drivers but also the courage and capability to imagine the future. Envisioning possibilities "grows out of a childlike innocence about what could be and should be, out of a deep and boundless curiosity on the part of senior executives, and out of a willingness to speculate about issues where one is, as of yet, not an expert." (Hamel and Pralahad 1994, 82-83) Having the knowledge and imagination to develop industry foresight will establish the organization as intellectual leaders who can influence the direction and form of the industry it is in, and so allow the organization to regain control of its own destiny.

An example of envisioning and enacting the future on a national scale is the National Computer Board (NCB) of Singapore, the agency responsible for designing and implementing the country's national plans to use information technology to move Singapore into the front ranks of the information age (Choo 1995). Singapore is a small island state with a population of 2.8 million that enjoys one of the highest living standards in the world. Devoid of natural resources, Singapore recognized early that information technology must lever the skills and diligence of its citizens. The NCB was established in 1981, and one of its first responsibilities was to manage an ambitious program to computerize the civil service. The future vision was to provide the public with significantly better and wider range of services while improving productivity. At that time, the missing competency was indigenous expertise for information systems development. The NCB actively promoted a number of joint projects with foreign partners, training centers, overseas education and training schemes, incentive measures, and so on, to quickly build up a critical mass of computer professionals. The civil service computerization is an ongoing success - a recent audit showed that the government had obtained a return of over 2.7 dollars for every dollar spent on information technology in the program, and had avoided the need for some 5000 posts (NCB 1992). In the ensuing National IT Plan (1986-1990), the focus moved to the private sector, where the new vision was to create a strong, export-oriented, local IT industry, and to exploit IT to strategically enhance business performance. The required competencies were to be an awareness and understanding of how IT could be used strategically, and the local technical capabilities to develop world-class IT applications. Through partnership programs, joint ventures, showcase projects, the innovations of local R&D institutes, promotional activities, incentives and subsidies, and so on, a vibrant local IT industry emerged, growing at a compound annual rate of 30 percent between 1982 and 1990. A number of leading edge IT applications made their debuts, including a national electronic data interchange network (TradeNet) linking traders and government departments, and an expert system for ship planners in the port of Singapore. Both applications are used as case studies of exemplary strategic IT applications in the leading business schools of North America, and have spawned even more ambitious sister projects in Singapore. In 1991, the republic set its sights higher and launched its current IT2000 masterplan, to use advanced information technologies to transform the city-state into a networked, intelligent island. According to the IT2000 vision, IT will enable Singapore to turn into a global business hub, boost its economic engine, enhance the potential of individual citizens, link its communities both locally and globally, and improve the quality of life (NCB 1992). The new competencies now include expertise in working with broadband networks, multimedia, and telecomputing; and building an information infrastrucutre based on technical and legal standards. The role of the NCB as masterplanner and architect of Singapore's IT destiny has been in defining future visions of Singapore as an IT-enabled society, in acquiring and developing the required competencies, and in reaching out to industry, government and the public to promote the use and acceptance of IT.

The Intelligence/Learning Cycle

For the intelligent organization, learning and adaptation are behaviors that must paradoxically embrace their own opposites. Organizational learning necessarily includes unlearning about the past - the organization should not restrict learning and exploration to its existing markets, products or practices, but should rediscover new goals and responses by stepping out of habitual frames of reference and reexamining norms and assumptions (Hedberg 1981). Similarly, adapting to an environment necessarily includes creating an environment that is advantageous to the organization. After all, the external environment consists of other organizations, and every organization is in fact part of larger ecological systems whose members are bound together by common interests and interlocking activities (Moore 1993). In creating the environment, an organization, either by itself or with its partners, develops foresight about future benefits that it can deliver, grows capabilities to provide these benefits, and so ensure a future for itself (Hamel and Pralahad 1994). Creating the environment is more than reactively enacting or interpreting the environment, and more than finding a matching fit with the environment. In effect, the intelligent organization can engineer such a fit through its deep understanding of the forces and dynamics that give shape to the future.

The organizational intelligence/learning process is a continuous cycle of activities that include sensing the environment, developing perceptions and generating meaning through interpretation, using memory about past experience to help perception, and taking action based on the interpretations developed (Figure 1.1). Sensing is collecting information about the external and internal environment. Because the organization cannot attend to every event or development, it must select areas of priority, filter incoming data according to its interests, and sample events for learning. Memory is derived from the experiences of the organization in interacting with the environment, and is expressed formally (documents, procedures) and informally (beliefs, stories). Experience develops rules that are used to match situations with appropriate responses, and frames that are used to define problems and their salient dimensions. Perception is the recognition and development of descriptions of external events and entities using the knowledge that is available in memory. Perceptual strategies include developing a representation of an external scene, classifying objects and events according to categories that are known or have been encountered before, and recognizing the identity and main attributes of interested objects. Organizational perception depends heavily on the norms, frames and rules that members use as lenses to view trends and developments. Interpretation is at the centre of the intelligence cycle as it attempts to explain `What is really going on here?' in terms that are meaningful to the organization. Interpretation is hard because it must balance conservatism (to interpret data according to existing beliefs) with entrepreneurism (to interpret data for the exploration of new alternatives). Interpretation leads to understanding and creative insight by which future consequences and opportunities are anticipated and evaluated according to preferences. Ultimately, interpretation is the making of meaning - meaning about where the organization was in the past, what it is today, and where it wants to be in the future. In organizations, finding meaning is a social process, requiring people to socialize and exchange information. Finally, adaptive behavior initiates a new cycle of learning as the organization makes decisions and takes actions that result in effects and outcomes. These are fed back into the loop by modifying sensing strategies (adjusting selection and sampling criteria) and by modifying frames and rules in memory (changing existing beliefs, adding new rules).

Figure 1.2 Organizational Intelligence/Learning Cycle

Building the Intelligent Learning Organization

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