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Expert systems 437
to focus on ‘the process of marketing planning itself rather than any
situation specific system’. The overall EXMAR system objective was ‘to
provide assistance for the marketing–planning process in such a way
as to spread knowledge and further understanding of how and why
the multi-various factors of the marketing interact and serve to define
the parameters of the business activity’. As such it shares a feature of
generalized advice-giving similar to that found in the NEGOTEX system.
The underlying model covered the ‘data manipulated by a marketing
planner when developing a strategic marketing plan, and structures the
marketing planner’s task’. McDonald noted that ‘the more complex and
amorphous the expertise to be captured, the longer it takes ... requires
both time and resources of massive proportions’.
Interestingly, from our context, McDonald felt that the decision to
investigate marketing planning was sensible because it fitted Rangaswamy
et al.’s four-point checklist. Focusing on the second and third
points, McDonald argued the case that ‘successful practitioners make
judgements using criteria and rules which are difficult to define and the
process to be computerized was not documented in any detail’. But, as
McDonald noted, ‘after almost two years of work and an expenditure of
over a quarter of a million pounds, all there is to show is a demonstration
model ...’.
In summary, all the expert system developments that we have
described can be seen as advisory systems. Several of these advisory
systems, like McDonald’s EXMAR system, have attempted to
model expertise where the knowledge domain was semi-structured
and incomplete. Such ventures have not produced commercially viable
expert systems that provide a quantifiable payback to the sponsoring
organizations. The majority of the advisory systems which we have
described seem to be destined for standalone PCs as assistants to
experts who may, or may not, utilize them in practice. Intuitively,
it seems to us that very few companies could prepare a business
case for the investment of hundreds of thousands of dollars in such
ventures. But what then are the characteristics which differentiate ‘commercially
viable’ systems? How can lucrative development areas be
identified? In the next section we draw on our own experience of
building expert systems in the financial services sector to answer these
questions. Our argument is that strong business cases can be made for
systems which automate a part of the business process. In our view, such
systems are best developed where the knowledge domain is structured
and complete.