Organizations are established to create value that cannot be produced by individuals (Huber & McDaniel, 1986). To make the value creation process effective and sustainable, organizations must continuously learn and adapt to the changing environment (Huber, 1991). The success of enterprises in the 21st century will likely depend as much on their ability to tap into organizational intellectual and systems capabilities as the tangible assets such as materials and capital. Quinn, Anderson, and Finkelstein (1996) called for designing organizations around intellect. They described organizational intellect as having (1) cognitive knowledge (know what); (2) advanced skills (know how); (3) system understanding and trained intuition (know why); and (4) self-motivated creativity (care why).
Organizational intelligence starts with the intelligence of units and members of the organization. Liang (2001, 2002) studied intelligence characteristics of individuals and how the individual mindsets come to form "orgmind" or collective intelligence. McMaster (1996) defined organizational intelligence as a function of the number of connections, the intricacy of those connections, and system design. Computer networking and communications technologies have greatly enhanced the connectivity of organizations and changed the way they operate.
The evolution of information technology is intertwined with the evolution of organizations. Huber (1990) pointed out the need to re-examine theories of organizational design when advanced information and communication technologies have dramatically changed the operations and management of organizations. Although substantial research has been conducted on the impact of information technology on organizations and the integration of various information systems (e.g., Markus, Majchrzak, & Gasser, 2002; Mentzas, 1994), most studies have not considered a comprehensive, totally integrated infrastructure for intelligent organizations. Information systems design theories have traditionally focused on the design of specialized systems such as DSS and EIS (Churchman, 1979; Walls et al., 1992). As more organizations realize the importance of building, nurturing, and growing organizational intelligence, research on organizational intelligence infrastructure will likely increase.
The phrase "intelligence infrastructure" is new, but the concept is not. Many successful businesses have already built an intelligence infrastructure, enabling them to become intelligent enterprises. Broadly speaking, intelligence infrastructure includes all basic facilities, services, and installations needed for the functioning of an intelligent enterprise. We define intelligence infrastructure as information technology-based facilities, systems, and services that support effective and efficient decision making at all levels of an organization. In the 1970s and 1980s, the information infrastructure of a business consisted of database and database management systems that supported various systems such as transaction processing, management information, decision support, and executive support systems. During the 1990s, business firms began the move from information age organizations to learning organizations, characterized by an information infrastructure based on data mining and knowledge management systems. Intelligent agents have emerged in recent years as key components in organizational information infrastructure.
An intelligence infrastructure is created by integrating existing information/knowledge systems through the introduction of an encompassing, efficient, flexible, and intelligent communication system that greatly enhance the integration, coordination, and collaboration of people and resources in an organization. This built-in intelligence at the infrastructure level makes the organization agile and robust. Although traditional information systems infrastructure offers a certain degree of intelligence, it is in general limited, fractural, and static in nature. We envision an intelligence infrastructure that is integrated, comprehensive, dynamic, and adoptive.
The evolution of modern organizations and their information infrastructure is depicted in Figure 1. TPS, MIS, DSS, and EIS are traditional business information systems at different managerial levels. DB and KM are short for database and knowledge management, respectively. Supply chain management (SCM), enterprise resource planning (ERP), business process redesign (BPR), and total quality management (TQM) are commonly used in learning organizations. Multi-agent systems (MAS) can be designed to create dynamic connections to various information systems. Intelligence infrastructure (II), global resource management (GRM) and customer relationship management (CRM) are indispensable components of an intelligent enterprise.
Figure 1: Evolution of Organizations and their Information Infrastructure.
A key feature of the intelligence infrastructure is the integration of all components and subsystems within the enterprise. Those components and subsystems include not only various information and knowledge systems, but also management control, human resource management, and environment management. Intelligent agents automate key operational processes, monitor operations, schedule activities, coordinate tasks, process data, anticipate needs, deliver proactive information and services, negotiate and collaborate with other agents, and intimate with their masters—the knowledge users. What distinguishes intelligence infrastructure from other types of systems is its ability to continuously capture and integrate business process knowledge, hence improving the organization's ability to learn and adapt while the changes occur, not after.
The transition from an information age organization to a learning organization occurs when the organization moves from information based managerial support to knowledge based managerial support. There are also increased needs for systems integration, business process redesign, and system-wide quality control. Intelligent organizations require seamless integration of all systems in the organization through the intelligence infrastructure. Global resource management systems allow the intelligent organization to scan, analyze, and integrate global resources. The existence of advanced information and communication systems such as MAS, II, and GRM does not automatically guarantee the success of the organization. It is a necessary condition for intelligent enterprises. Success of those enterprises depends on the assimilation of the information technologies into their organization design.
Gregory Mentzas (1994) suggested analyzing computer based information systems through the study of characteristics of the elements of those systems. The elements are divided into "basic" class and "optional" class. The composition of basic elements defines functions and behaviors of the information systems under examination. Optional elements are those modules that expand the functionality of the information systems beyond typical requirements. As integration is the key for building intelligence infrastructure, inter-systems communication and coordination, optional elements in traditional information systems, become basic elements for all subsystems in an intelligence infrastructure.
As intelligent organizations vary in size and structure—from project teams, to business entities, industries, and nations—so will the intelligence infrastructure. Although the capability of intelligence infrastructure covers a wide spectrum, to allow intelligent enterprises to reap the desired benefits of an organic structure and spontaneity in responding to environmental changes, the intelligence infrastructure must meet the following fundamental requirements:
A distributed information/knowledge system with central coordination
A distributed multi-agent system integrated with traditional systems
Open standards-based technologies to ensure a high degree of interoperability
A common ontology that enables agents to understand one another
A high level of self-regulation in system operation, monitoring, and adoption
A high degree of reliability and accessibility
Secure and trusted agent-user and agent-agent relationships
User-friendly interface and agent-supported learning
Improved value to the end users
Easy to develop, deploy, and maintain
Integrating and updating existing assets
Leveraging external resources
Following George Huber's theory of the effects of advanced information technologies on organizational design, intelligence, and decision making, we set forth the following propositions that stipulate the effects of intelligence infrastructure on organizations and elements of organizations.
II increases the number and variety of people participating in knowledge sharing and decision making.
II leads to more distributed and shared responsibilities.
II facilitates more coordination and collaboration.
II decreases the layers involved in organizational decision making.
II reduces the burden of human information processing.
II makes internal organization expertise more accessible to users.
II improves environmental scanning.
II leads to more accurate, comprehensive, timely, and accessible organizational intelligence.
II improves the quality and reduces the time of organizational decision making.