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Chapter 10 Notes – these notes are only some major highlights.
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Knowledge Management: the set of processes developed in an organization enabling the
firm to learn from its environment and adapt appropriate business processes in response.
 Explicit knowledge: product manuals, research reports, etc.
 External knowledge of competitors, products, and markets, including competitive
intelligence.
 Tacit knowledge: informal internal knowledge; the experience and expertise of
employees that have not been formally documented.
Chief Knowledge Officer (CKO): executive in charge of an organization’s knowledge
management program.
Data Workers: people, such as secretaries or bookkeepers, who process an organization’s
information and paperwork.
Knowledge Workers: people, such as engineers, who design products or services, or
create knowledge for the organization.
Different KM Systems:
1. Office Systems
 Managing documents – word processing, desktop publishing,
document imaging, web publishing, etc.
 Scheduling – E-calendars, groupware, intranet
 Group collaboration: voice, digital, and document-based
communication – email, voice mail, digital answering systems,
groupware, intranets
 Managing data – databases, spreadsheets, front-end interfaces
2. Knowledge Work Systems (KWS)
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Keeping the organization up-to-date with changes in the external world –
technology, science, social thought, etc.
Serving as internal consultants regarding the areas of their expertise, the
changes taking place, and their opportunities.
Acting as change agents – evaluating, initiating, and promoting projects.
3. Group Collaboration Systems
 Primarily groupware and intranets/portals – example: MS-Project
4. Artificial Intelligence Applications: use of computer systems to accomplish
physical tasks and emulate human expertise and decision-making.
 Store information in an active form; preserve expertise that might be
lost when an expert leaves the firm.
 Create a mechanism (physical or advisory) that is not subject to human
emotions.
 Eliminate routine and unsatisfying jobs held by people.
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Generate solutions to massive and complex problems that would take
humans far longer to solve.
Expert Systems: knowledge-intensive computer programs that capture the expertise of a human
in limited domains of knowledge.
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Expert systems are essentially AI programs with a very large number of
interconnected and nested rules, or If-Then conditional statements, that are the
basis of knowledge in a system.
AI Shells: the programming environments of expert systems; usually comprised of specialized
AI programming languages, such as LISP or Prolog, that can process lists of rules efficiently.
Forward chaining vs. Backward chaining
Case-Based Reasoning (CBR): AI technology that represents knowledge as a database of cases
and solutions.
Neural Networks: hardware or software that attempts to emulate the processing patterns of the
biological brain.
 A bottom-up approach to AI
Fuzzy logic: the ability of AI programs to tolerate imprecision.
The difference between expert systems and neural networks is that expert systems seek to emulate
a human expert’s way of solving highly specific problems, whereas neural networks do not model
human intelligence nor seek solutions, but rather seek to put intelligence into the “hardware” in
the form of a generalized capability to learn.
Intelligent Agents: software programs that use a built-in or learned knowledge base to carry out
predictable tasks.