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The LIS profession is beginning to place expert systems in perspective. Expert systems are no longer heralded as being the only necessary tool but rather one tool among an array of several. LIS educators are realistic, both about expert systems technology and about what can be achieved within the limitations of an LIS course. New technologies for refining and controlling information are constantly emerging; LIS schools have to keep up‐to‐date with them as they emerge, but they must also ensure that they do not overprioritize one particular development at the expense of others. They can, at best, only hope to give a taste of the possibilities and potential in different areas. Expert systems are still new enough to warrant special treatment but no doubt they will be ousted by newer technologies in the course of time. Meanwhile, LIS professionals should make the most of what is currently available. Hopefully it should pay dividends in the future.

Expert systems are frequently mentioned in business circles these days. They have the potential to assist greatly in the dissemination of scarce or complex expertise. But although they can be immensely valuable if properly understood, developed and used, they can also be a waste of resources. Aimed at managers who feel the need to know more about expert systems, but who are not themselves computing specialists, what an expert system is and is not is explained. The types of application for which it is suitable, and who is most likely to find the time, trouble and expense of creating one that is most worthwhile is discussed. Different types of expert system are explained, and the means and merits of prototyping are outlined. In order to have a successful expert system, certain essentials are required: a subject area which can be suitably defined; an expert who can provide the knowledge; users who know what they want and how they want to use it; a knowledge engineer who can translate the expertise into facts and rules for the system. A short but useful glossary of technical terms which may be encountered in the world of expert systems is included.

Argues that expert systems are a useful tool in implementing quality customer service. Examines seven steps of customer service and illustrates how expert systems can support each step. Draws on the literature in the field to cite commercial installations of expert systems to support quality customer service.

Neural networks and expert systems are two major branches of artificial intelligence (AI). Their emergence has created the potential for a new generation of computer‐based applications in the area of financial decision making. Both systems are used by financial institutions and corporations for a variety of new applications from credit scoring to bond rating to detection of credit card fraud. While both systems belong to the applied field of artificial intelligence, there are many differences between them which differentiate their potential capabilities in the field of business. Presents an analysis of both neural networks and expert systems applications in terms of their capabilities and weaknesses. Uses examples of financial applications of expert systems and neural networks to provide a unified context for the comparison.

Expert systems are computer programs which can emulate certain functions of human expertise. They are now used in many areas of business. In the last five years or so a number of software packages have become available, called “shells”, which offer the prospect of users building their own systems. They are relatively cheap and provide a framework into which knowledge or expertise can be built. Describes the development – using one of these shells ‐ of a prototype expert system for evaluating tenders for the supply of new freight containers. The prototype was tested using some data from a previous tender and was found to save more than 90 per cent of the time normally taken to carry out this function. Further benefits were obtained in respect of improved quality of the analysis which could result in additional substantial cost savings. This expert system was built by the manager responsible for the tender evaluation, with a limited amount of assistance. Concludes with some suggestions for managers considering building similar small expert systems.

Examines the relationships between the frequency of expert system use and the system′s hardware, access location, and features. Also examines the relationship between the expert system′s hardware and access location. The study is empirical, using a survey of marketing executives who work within marketing organizations employing expert systems. The findings include that the hardware type appears to influence the frequency of expert systems use. Daily use is dominated by mainframe computers, while weekly and monthly use is dominated by the microcomputers. Further, the frequency of expert system use increases with access availability and decreases as the expert system becomes less available. The dominant feature of these expert systems is the ability to perform what‐if‐analysis. When access location and hardware type are examined, the dominant hardware is the microcomputer. Further, particular hardware types tend to dominate specific access locations.

The author discusses the provision of training programmes on expert systems managers in the USA.

Examines the value of expert systems in marketing organizations through a national mail survey of 117 marketing executives. All the examined respondents reported the successful use of expert systems in their organizations. The results indicate that while expert systems provide operational benefits (e.g. they assist in making decisions more quickly), they also present new problems (e.g. increased security needs) that the adopting organization must consider. Based on these results, discusses implications for managers regarding the encouragement of the adoption and use of expert systems. Also presents questions concerning expert systems which require additional investigation.

The paper seeks to identify determinants of general practice staff's intention to further implement a smoking cessation expert system, a computer‐generated tailored intervention, following their participation in an effectiveness study.

Written questionnaires based on the I‐Change Model, a socio‐cognitive model, were left in general practices where the expert system had been trialled. Respondents intending to continue their use (intenders, n=62) were compared to those who did not (non‐intenders, n=27).

Eighty‐nine individuals from 55 practices responded (73 per cent). GPs were more often intenders than general practice assistants. Responses from the same practice were not significantly related to each other. Intention to continue using the expert system was determined by a more positive attitude towards the expert system, a social norm towards engaging in smoking cessation activities, and higher self‐efficacy. Practice staff who had actively offered the expert system to their patients were more likely to be an intender.

Cognitive factors and trial involvement determined intention to further implement the expert system. Discussing barriers with practice staff could increase motivation to implement and ownership. Intenders can aid the implementation process by sharing experiences with non‐intending peers.

There are many practical difficulties facing managers who decide to venture into the field of Expert Systems. They will have to cope with both technological and human problems. Care must be taken in choosing the application, software house, hardware and software. A company may decide to build an Expert System in‐house, in which case it will need the right sort of Knowledge Engineer(s). The system must be of obvious benefit to the user otherwise it will fail. Knowledge Acquisition for Expert Systems is difficult, but possible if handled properly. The users of the system must be involved in its design and evaluation and a methodology for doing so is suggested. Managers can no longer pay lip service to the concept of user involvement in the design and implementation of new technology.