Beyond Accuracy: Rethinking the Value of AI in Decision-Making Through Baseball’s Automated Ball-Strike (ABS) System

Abstract

This paper examines how organisations integrate AI systems to support decision making by studying Major League Baseball’s multi-year experimentation with the Automated Ball-Strike (ABS) system – commonly known as ”robot umpires”. While automating the strike zone may appear to be a straightforward technical task, our study reveals that it entails complex practices of negotiation and meaning making over time. Rather than the simple adoption of a decision aid, the implementation of ABS became a site of organisational sensemaking. We argue for a need to move beyond user-centric models toward stakeholder-centric, contextually embedded frameworks for understanding AI in organisational decision-making.