Impacts of artificial intelligence on organizational decision making

Abstract

Of all of the new technologies emerging in the late 20th century, the production of artificial intelligence may provide the most profound impacts on organizational decision making. Because the development of artificial intelligence technologies and models has largely been based on psychological models of human cognition, the effects of their implementation in complex social settings have not been thoroughly examined. This paper is an attempt to generate research which will develop a comprehensive understanding of the impacts of artificial intelligence and its role in complex organizations. A set of 11 hypotheses has been developed which examine the relationships between artificial intelligence technologies and the dimensions of organizational decision making. It is argued here that the implementation of expert systems will lead to less complex and political decision processes, while the implementation of natural language systems will lead to more complex and political decision processes.KEY WORDS Artificial Intelligence Organizational decision making Power and politics.

Organizational Risk Perception and Reduction: A Literature Review

“…Some evaluation systems take a fairly simple qualitative approach (Busch, 1963) while others are much more complex and quantitative in nature (Hinkle et al, 1969;Wind et al, 1968). Recently, Lawrence (1991) has argued that the implementation of expert systems will lead to less complex and political decision processes which may in turn lead to less risk. Concerning the extent of usage of formalized supplier evaluation systems, a research study reported by Parasuraman (1981) found that only 29 per cent of the sample used such systems than were small or large companies.…”

On the current state of combining human and artificial intelligence for strategic organizational decision making

“…Nevertheless, even with more people involved, the human capacity to process information is limited (Lawrence 1991;Fiori 2011). Human decision makers, therefore, consciously construct simplified models, called heuristics or rules of thumb (Simon 1987;Fiori 2011), which deal with complex problems sequentially to make them treatable for the human computation capacity.…”

“…For Simon, however, even intuition is based on stored information and experience, which the decision maker decides to rely on when determining alternatives and probabilities, although more unconsciously (Simon 1986;Fiori 2011). Rational behavior is thus assumed to be on the continuum between intended rationality and intuition, depending on the information-processing capabilities of the agent, the complexity of the problem, and various aspects of the environment (Lawrence 1991;Fiori 2011). However, rational behavior is guided by rules, which means that it is always bounded (Fiori 2011).…”

“…Including technology in business is not a new development, as machines have been part of manufacturing processes to support humans for centuries, but machines are rather a tool, completely governed by humans, and less defined in real social collaboration settings than organizations are (Lawrence 1991;Nguyen et al 2018;Boone et al 2019). With AI, machines are assumed to act and react to humans, implying a possible change in the human-machine relationship (Huang and Rust 2018).…”

“…With AI, machines are assumed to act and react to humans, implying a possible change in the human-machine relationship (Huang and Rust 2018). Opportunities and hazards, however, are neither agreed nor analyzed in more detail, making research necessary (Lawrence 1991;Silva and Kenney 2018;Vaccaro and Waldo 2019).…”