The role of AI -based technology in support of knowledge management value activity model

ARTICLE SUMMARY & REVIEW (Week3-Class4)
[Vijay Rao]

(Group: Ian,Helen,Pennie,Torrance,Vijay)

This article summarises “AI-based technology in support of the knowledge management value activity cycle” Fowler (2000). It surfaces the use and limitations of AI technology in the context of Knowledge management based systems. Primarily it focuses on the use of AI, to improve the business process, performance, productivity in an Organization by capturing the raw data and information and explores how the AI can support business in their attempt to successfully manage the explicit and tacit knowledge.

The conceptual frame work of “Knowledge Value-Chain” (KVC) based on ‘knowledge spiral’ model proposed by Nonaka Takeuchi (1995) is developed to demonstrate the “knowledge activity cycle”.

The article introduces different knowledge base systems such as:

Knowledge based expert system(KBES):

The knowledge based expert system comprising of Knowledge base, KB expert system and subsystems inter linked with an inference search engine incorporating rule base selector and interpreter with working memory with in a framed network and user interface.







Neural Network (NN):

The technique uses neural nets which are strictly data manipulators and storage/presentation devices, respectively, and do not contain a knowledge base. It comprises of various nodes similar to human brain. The strength and weight of each node are calculated and adjustments are manipulated before presenting final decision or output. It can operate with incomplete set of data/information. These types of technique can be correlated to tacit knowledge held by individuals in making decisions operating with incomplete set of data.

Case Based Reasoning (CBR):

In this technology the optimizer uses algorithms which does not manipulate the inputs unlike the NN, it select the best possible case or scenario by scanning with in the knowledge base before presenting or reporting. The disadvantage of such systems requires larger memory and infrastructure, necessitating more human intervention.

Tacit Knowledge in AI:

Capture of tacit knowledge has always been a challenge due to human behaviour is no different by using newer technologies; it becomes even more complex as more objects are involved. The characteristics of tacit knowledge impede the implementation within the context of AI technology. However from the illustrative research model “Knowledge value activity” shows combination of formal, explicit knowledge in the machine, and the non-formal, tacit knowledge of the users, can result in enhanced problem-solving capabilities which surpass either one of these components acting alone.

Comparison of different AI techniques:

The comparison of KBES, NN and CBR shows each of the technologies has implications and restriction with varying advantages. The classical rule based expert systems rely on ideal assumptions of structures, certainty, rationality and linearity which is in sharp contrast to KM domain which assumes (subjective meaning, uncertainty, belief and faith), which forms the concept of tacit knowledge.

Review:

The implementation and use of AI in an organization is not a unique solution to meet the organization demand in context of KM. However the combination of the AI and human mental and knowledge can improve the quality, expedite KM process in an organization. Furthermore, the creation of hybrid systems comprising combinations of rule-based expert systems and NN’s may offer access to embedded knowledge coupled with an ability to function in the partial absence of certain data.

Reference:

Davenport, T.H., Beers, M.C., 1995. Managing information about processes. Journal of Management Information systems 12 (1), 57±80.

A.Fowler , (2000) , The role of AI-based technology in support of the knowledge management value activity cycle, Journal of Strategic Information Systems,pp 107 -128

Nonaka, I., 1991. The knowledge-creating company. Harvard Business Review 69 (6), pp 96 – 104

Nonaka, I., 1994. A dynamic theory of organisational knowledge creation. Organisation Science 5 (1), pp14 - 37.