For decades, futurists have anticipated the day when computers would relieve managers and professionals of the need to make certain types of decisions.1 Computer programs would analyze data and make sound judgments on such matters as how to configure a complex computer, how to diagnose and treat a patient’s illness or how to know when to stir a big vat of soup with little or no human help. But automated decision making has been slow to materialize. Many early artificial intelligence applications were just solutions looking for problems, contributing little to improved organizational performance.2 In medicine, for example, doctors showed little interest in having machines diagnose their patients’ diseases. In the business sector, even when expert systems were directed at real issues, extracting the right kind of specialized knowledge from seasoned decision makers and maintaining it over time proved to be more difficult than anticipated.
Even though the need for automated decision systems was recognized, full-blown decision-making systems were seen as impractical for use in business. So, during the 1970s, managers began to address this need by employing intelligence augmentation tools that provided managers and analysts with “decision support.”3 The idea was for the support system to help managers report, analyze and interpret data as opposed to actually making the business decisions. Although some decision support... To read the complete article, login or sign-up using the form below.
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