Intelligent agents (as discussed earlier in this chapter) have greatly improved the usability and efficiency of knowledge management systems. Many organizations have found electronic blackboards embedded in groupware to be somewhat inefficient because experts in their organizations end up spending unproductive time reviewing questions on blackboards placed by other employees. A more efficient approach has long been thought to be the centralized storage of documents such as memoranda and letters that explain problem resolutions and may be reusable with other customers and clients. The problem is how to find the right document for your problem.
Intelligent agents come to the rescue by providing software agents that learn about an individual’s work tasks and search behavior to better understand the information the user is likely to be seeking. The intelligent agent is then able to better refine the search and filter out much extraneous information that may be retrieved. The intelligence in these agents generally decreases search time, and so knowledge management systems end up getting used more frequently, as information is identified easily and quickly and with reduced frustration over mounds of unrelated information.
While intelligent agents tend to be the dominant form of AI used for knowledge management, you should certainly recognize that other AI components are used in knowledge management systems. Neural networks can be very helpful in recognizing patterns within the information stored in the vast knowledge warehouses. Further, such technologies can help pull together associated documents to recognize the common threads of information between different documents and pieces of information that have been stored. Additionally, expert systems and business intelligence systems are increasingly finding a home as integrated components of the knowledge management system.