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Chapter 11 Managing Knowledge 463

11.4 INTELLIGENT TECHNIQUES

Artificial intelligence and database technology provide a number of intelli-
gent techniques that organizations can use to capture individual and collective
knowledge and to extend their knowledge base. Expert systems, case-based
reasoning, and fuzzy logic are used for capturing tacit knowledge. Neural
networks and data mining are used for knowledge discovery. They can
discover underlying patterns, categories, and behaviors in large data sets that
could not be discovered by managers alone or simply through experience.
Genetic algorithms are used for generating solutions to problems that are too
large and complex for human beings to analyze on their own. Intelligent agents
can automate routine tasks to help firms search for and filter information for
use in electronic commerce, supply chain management, and other activities.
Data mining, which we introduced in Chapter 6, helps organizations capture
undiscovered knowledge residing in large databases, providing managers with
new insight for improving business performance. It has become an important
tool for management decision making, and we provide a detailed discussion of
data mining for management decision support in Chapter 12.
The other intelligent techniques discussed in this section are based on
artificial intelligence (AI) technology, which consists of computer-based
systems (both hardware and software) that attempt to emulate human behavior.
Such systems would be able to learn languages, accomplish physical tasks, use
a perceptual apparatus, and emulate human expertise and decision making.
Although AI applications do not exhibit the breadth, complexity, originality,
and generality of human intelligence, they play an important role in contempo-
rary knowledge management.

CAPTURING KNOWLEDGE: EXPERT SYSTEMS

Expert systems are an intelligent technique for capturing tacit knowledge in
a very specific and limited domain of human expertise. These systems capture
the knowledge of skilled employees in the form of a set of rules in a software
system that can be used by others in the organization. The set of rules in the
expert system adds to the memory, or stored learning, of the firm.
Expert systems lack the breadth of knowledge and the understanding of fun-
damental principles of a human expert. They typically perform very limited
tasks that can be performed by professionals in a few minutes or hours, such as
diagnosing a malfunctioning machine or determining whether to grant credit
for a loan. Problems that cannot be solved by human experts in the same short
period of time are far too difficult for an expert system. However, by capturing
human expertise in limited areas, expert systems can provide benefits, helping
organizations make high-quality decisions with fewer people. Today, expert sys-
tems are widely used in business in discrete, highly structured decision-making
situations.

How Expert Systems Work
Human knowledge must be modeled or represented in a way that a computer
can process. Expert systems model human knowledge as a set of rules that
collectively are called the knowledge base. Expert systems have from 200 to
many thousands of these rules, depending on the complexity of the problem.
These rules are much more interconnected and nested than in a traditional
software program (see Figure 11.5).

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