Intelligence can be measured individually and organizationally. Just as we now measure the intelligence of people by using I.Q., the study of organizational intelligence measures the intellectual capacity of entire organizations. Since I.Q. has been found to account for roughly 50 percent of the differences in human success, it wouldn’t be a stretch to believe that something similar is true of organizational intelligence. Organizational intelligence is the capacity of an organization to create knowledge and use it to strategically adapt to its environment or marketplace.
Roughly half of corporate performance may be attributed to a company’s capability for responding to change and complexity intelligently, with the rest being determined by dynamic factors: leadership, strategy and environmental conditions.
The concept of organizational intelligence originates from an analogy with the human individual intelligence. Each human individual is supposed to possess a unique intelligence, and so may each organization as a whole be assumed to have a characteristic collectivity of its members’ intelligence. Now that each organization is highly information-sophisticated, the role of machine or artifact intelligence collectively employed in each organization is quite remarkable.
Human intelligence is still the most powerful engine driving the development and maintenance of this lexicographic profile. Technology tools help with the content mining, frequency analyses, and other measures valuable to a taxonomist, but the organization, concept expressions, and relationship building is still best done by humans.
In a similar way, the application of the thesaurus is best done by humans. Because of the volume of content items being created every day, it may not be possible to have human indexers review each of them. Automated systems can achieve perhaps 90% “accuracy” (i.e. matching what a human indexer would choose), so high-valued content is still indexed manually, much more efficiently than in the past, but still by humans. And the balance requires the contribution of humans to inform the algorithm in actual natural (human) language.
At a time when exciting concepts such as organizational learning, knowledge management and intellectual assets are being put into use, there is little solid understanding of how to evaluate these practices, how they relate to performance and how they can be improved. Nor do we understand how knowledge is enhanced by information technology.
Mathematical analyses work to identify statistical characteristics of a large number of items and is quite useful in making business decisions. For many decades now, researchers have been working to find a way to analyze natural language that would result in somewhere near the precision provided by human indexers and abstractors. Look at IBM’s super-computer “Watson” and the years and resources invested to produce it.
When you think of intelligent applications, you might think of the semantic web. These could be as simple as smarter web browsers and e-mail clients that can understand natural language instructions and complete more complex tasks like automatically booking flights for us, emailing friends and marking our calendars.
It could also be systems that can process data from multiple linked sources and arrive at something new. Like a corporate system that evaluates the areas of expertise of its employees and recommends optimal project teams. Or a knowledge-management system that can tell you whether a particular idea you just thought of is already being worked on by someone else.
Melody K. Smith
Sponsored by Access Innovations, the world leader in thesaurus, ontology, and taxonomy creation and metadata application.
