Author List: Gill, T. Grandon;
MIS Quarterly, 1995, Volume 19, Issue 1, Page 51-81.
Expert systems (ES) were among the earliest branches of artificial intelligence (Al) to be commercialized. But how successful have they actually been? Many well-publicized applications have proven to be pure hype, numerous Al vendors have failed or been completely reorganized, major companies have reduced or eliminated their commitment to expert systems, and even Wall Street has become disillusioned-a predicted $4 billion market proving to be smaller by an order of magnitude. Yet, in spite of these set- backs, there are many companies who remain enthusiastic propodents of the technology and continue to develop important ES applications. This paper explores how the first wave of commercial expert systems, built during the early and mid-1980s, fared overtime. An important subset of these systems, identified in a catalog of commercial applications compiled in 1987, was located through a telephone survey, and detailed information on each system was gathered. The data collected show that most of these systems fell into disuse or were abandoned during a five- year period from 1987 to 1992, while about a third continued to thrive. Quantitative and qualitative analysis of the data further suggests that the short-lived nature of many systems was not attributable to failure to meet technical performance or economic objectives. Instead, managerial issues such as lack of system acceptance by users, inability to retain developers, problems in transitioning from development to mainte- nance, and shifts in organizational priorities appeared to be the most significant factors resulting in long-term expert system disuse.
Keywords: Artificial intelligence; expert sys- tems; implementation; systems development
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#279 0.167 field work changes new years time change major period year end use past early century half traditional areas established strong
#129 0.147 expert systems knowledge knowledge-based human intelligent experts paper problem acquisition base used expertise intelligence domain inductive rules machine artificial task
#159 0.144 systems information objectives organization organizational development variety needs need efforts technical organizations developing suggest given effective designing lack help recent
#246 0.115 strategic benefits economic benefit potential systems technology long-term applications competitive company suggest additional companies industry operating costs difficult substantial total
#22 0.061 software vendors vendor saas patch cloud release model vulnerabilities time patching overall quality delivery software-as-a-service high need security vulnerability actually
#6 0.055 data used develop multiple approaches collection based research classes aspect single literature profiles means crowd collected trend accuracy databases accurate