Machine Learning Based Decision-Making: A Sensemaking Perspective

Abstract

The integration of machine learning (ML), functioning as the core of various artificial intelligence (AI)-enabled systems in organizations, comes with the assertion that ML models offer automated decisions or assist domain experts in refining their decision-making. The current research presents substantial evidence of ML’s positive impact on business and organizational performance. Nonetheless, there is a limited understanding of how decision-makers participate in the process of generating ML-driven insights and enhancing their comprehension of business environments through ML outcomes. To enhance this engagement and understanding, this study examines the interactive process between decision-makers and ML experts as they strive to comprehend an environment and gather business insights for decision-making. It builds upon Weick’s sensemaking model by integrating ML’s pivotal role. By conducting interviews with 31 ML experts and ML end-users, we explore the dimensions of sensemaking in the context of ML utilization for decision-making. Consequently, this study proposes a process model which advances the organizational ML research by operationalizing Weick’s work into a structured ML-driven sensemaking model. This model charts a pragmatic pathway, outlining the interaction sequence between decision-makers and ML tools as they navigate through recognizing and utilizing ML, exploring opportunities, assessing ML model outcomes, and translating ML models into action, thereby advancing both the theoretical framework and its practical deployment in organizational contexts.