Fowler, A. Journal of Strategic Information Systems, 2000.
Mim-Summer Group (Dick, Holger, Ashwini, Eleanor, Kai)
Type of Article: Empirical study using case based research and analysis.
Audience: Management students, CIOs, IT professionals, Database developers, KM professionals.
Focus/Theme: Explores the potentials and limitations of AI technology use in knowledge management through the exploration of a case study.
Summary
This article relates the relatively older field of Artificial Intelligence (AI) to the newer practice of knowledge management and explores the potentials and limitations. The author does this through linking it to Nonaka’s knowledge spiral and related concepts. He also considers three main areas of AI investigation; knowledge based expert systems (KBES), neural networks (NN) and case based reasoning (CBR) and links these through investigation of a real world case study in a recognised “knowledge company”. The structure he uses is a “Knowledge Value Chain” (KVC) loop to test the concepts under investigation. The medium is a case study featuring a company (Bay-Point) and he takes a phenomenological approach using Glaser and Strauss’s Grounded Theory developed for ethnographic studies. He conceptualises the ideas discovered using the KVC and grounded in the data obtained during the empirical component of the study to reach his conclusions.
The article explores the idea that tacit knowledge is hard to capture. The article is primarily concerned with exploring the extent to which AI technologies, so full of promise early on and with very little practical application to date, can be employed to support businesses manage tacit knowledge ongoing. Overall can AI assist to capture and organise knowledge? His conclusion is yes it can support those processes (capture, disseminate, embody), but reiterates Davenport and Prusak (1998) that only the human brain has the ability to create knowledge. He recommends a combination of formal explicit (machine-based) and tacit (human) knowledge processes to produce problem-solving capabilities to provide a competitive edge in today’s companies. He quotes Polanyi by saying that tacit knowledge cannot be codified and represented by a set of articulated rules or algorithms, and notes that this is still true even despite the advances in fuzzy logic and “intelligent agent” technologies. Tacit knowledge is embedded in perception, belief and values which are characteristics that contravene the principles of computer science. Artificial Intelligence (AI) technologies cannot substitute for the knowledge process, but it can facilitate it.
Review
The article is very well written and readable, even though very lengthy in its attempt to encompass and relate the different knowledge management and AI concepts. Using one case study to expand the framework was a good idea, although it could be argued that only one case study was insufficient to illustrate the complex ideas described. However the author does provide a very thorough literature review and simplifies a very broad field (AI development) to three main concepts to enable better understanding. The choice of an ethnographic approach suited the subject of enquiry; can AI technologies deal with the capture and dissemination of tacit knowledge? The conclusion that no it can’t, but it can take the dog work out of it.
References;
Davenport, T and Prusak, L (1998). Working Knowledge; How Organisations Manage What they Know. HBS Press, Boston.
Nonaka, L. Takeuchi, H. (1995). The Knowledge Creating Company; How Japanese Companies Create the Dynamics of Innovation. Oxford University Press. New York.
Polanyi, M. (1998). Personal Knowledge: Towards A Post-Critical Philosophy. Routledge, London.
