Organizational Decision-Making Structures in the Age of Artificial Intelligence

By Y. Shrestha, Shiko M. Ben-Menahem, G. von Krogh. California Management Review, 2019

732 citations recorded by the source corpus.

How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid—human-to-AI and AI-to-human—sequential decision making; and aggregated human–AI decision making.

Subfields: Management Information Systems, Management Science and Operations Research, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/9e58e541b8549cd438c9d3e8cfb91dd12760589f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Management Information Systems

In Management Science and Operations Research

In Strategy and Management

Citations in the corpus

Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution (2020, background): “Shrestha et al. (2019) pro-pose a typology of configurations that can be implemented, ranging from full human-AI delega - tion (typically used for automated fraud detection or advertising recommendations) to hybrid AI-human or human-AI sequential decision making (used for hiring or health…”
Algorithmic management in a work context (2021, background): “Such complexities often make it difficult for those who are not AI experts to understand, even at a high level, how these techniques operate (Shrestha et al., 2019).”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, background): “…level, there are concerns raised about the implications and impact of the “ dark side ” of AI (e.g. Dwivedi et al., 2021; Weld and Bansal, 2019), such as bad decision-making, various types of discrimination, and other hidden biases and challenges (European-Commission, 2020; Shrestha et al., 2019).”