Artificial intelligence and the end of bounded rationality: a new era in organizational decision making

Abstract

Purpose

The purpose of this viewpoint article is to serve as a discussion starting point regarding organizational leadership’s increasing reliance on AI – in particular, how the technology is used as a supplemental tool for supporting rational decision-making. Practical implications and directions for further research are presented.

Design/methodology/approach

With its inception in economics, the concept of rationality has a rich history across multiple research domains. Based on that literature, coupled with the recent advancements in AI, the paper asks: will AI afford organizational leadership the ability to move from making bounded rational decisions to making fully rational decisions? The paper only scratches the surface of such a large question; however, the goal is to start the discussion around the topic.

Findings

While bounded rationality supports efficient decision-making, a complete understanding of any given decision is typically limited, and as a result, neither accuracy nor effectiveness is guaranteed. As AI systems grow in speed and accuracy, they should provide positive support for organizational leaders to make fully rational decisions. AI’s ability to collect and organize data, analyze it, and offer decision alternatives may help close the gap between bounded and rational decision-making.

Originality/value

Although AI research is not new, the recent developments in natural language processing engines has rapidly brought about new possibilities for their use in rational decision-making in the business and organizational context. This is fertile ground for future research, particularly in the area of organizational decision-making.

Citation

Shick, M., Johnson, N. and Fan, Y. (2023), "Artificial intelligence and the end of bounded rationality: a new era in organizational decision making", Development and Learning in Organizations, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/DLO-02-2023-0048