Expert systems for knowledge management: crossing the chasm between
information processing and sense making
Y. Malhotra*
Abstract
Based on insights from research in information systems, information science, business strategy and organization science, this paper
develops the bases for advancing the paradigm of AI and expert systems technologies to account for two related issues: (a) dynamic radical
discontinuous change impacting organizational performance; and (b) human sense-making processes that can complement the machine
learning capabilities for designing and implementing more effective knowledge management systems. q 2001 Elsevier Science Ltd. All
rights reserved.
Keywords: Expert systems; Arti®cial intelligence; Knowledge management; Information systems; Information science; Business strategy; Discontinuous
change; Sense making; Information processing
ªThere has been an over-concentration on Shannon's
de®nition of information in terms of uncertainty (a
very good de®nition for the original purposes) with
little attempt to understand how MEANING directs a
message in a network. This, combined with a concen-
tration on end-points (equilibria) rather than proper-
ties of the trajectory (move sequence) in games has
lead to a very unsatisfactory treatment of
the
dynamics of organizations.º Ð John H. Holland
(personal communication, June 21, 1995)1
1. Introduction
The narrative cited above as an observation by the noted
psychologist and computer scientist John Holland was in
response to my query to him regarding the possibility of
using intelligent information technologies for devising
self-adaptive organizations. As meaning seems to be a
crucial construct in understanding how humans convert
information into action [and consequently performance], it
is evident that information-processing based ®elds of arti®-
cial intelligence and expert systems could bene®t from
understanding how humans translate information into mean-
ings that guide their actions. In essence, this issue is relevant
to the design of both human- an