Artificial Intelligence in Organizational Decision-Making

Abstract

Artificial Intelligence (AI) has been around since the 1950s, but its potential is yet to be fully realized. Our findings suggests that AI can augment human intelligence and allow for more efficient and rational decision-making processes, given that the contingencies are effectively addressed. Inspired by the literature on organizational decision-making and AI, our study is grounded in the belief that understanding AI's impact on decision-making requires not just a technical understanding of AI, but also a deep exploration of its integration and implications within an organizational context. As such, we conducted interviews with various experts and users, utilizing a cross-case analysis to answer the following research question:

How do machine learning and natural language processing augment operational decision-making processes in organizations?

This gave us valuable insights into the perceived definition of AI, attitudes and expectations toward AI, and the benefits and challenges of AI in organizational decision-making. Juxtaposing this with the theoretical foundation, we discuss our most important theoretical findings: 1. Rationality and Accuracy, 2. Trust in AI, 3. Organizational Structure and Strategic Goals, and 4. Problem Comprehension. Following this, we provide a list of managerial recommendations, in addition to addressing possible limitations with the study and suggestions for further research. As such, we provide a holistic understanding of the dynamic factors and intricacies central to successfully augmenting organizational decision-making processes with AI.