A Systems Perspective on AI‐Driven Labor Transitions: Insights From the Rise of Robotaxis

ABSTRACT Advances in artificial intelligence (AI) are transforming how automation technologies interact with and change work systems, impacting non‐routine manual tasks that have historically remained unaffected. Leveraging an in‐depth study of taxi service automation, and drawing on government occupation data, direct observations, semi‐structured interviews, and archival data, our analysis focuses on changes in roles and tasks as a “system.” In contrast to prior studies, which focused exclusively on part of the work system (e.g., driving), we empirically demonstrate that the introduction of AI to a non‐routine manual task‐oriented work system spurs a significant rebundling of tasks and labor role organization in a way that ripples across the full system. This rebundling occurs via three archetypal patterns of role change: distributing, consolidating, and scaffolding. These patterns highlight system architecting decisions regarding how new and remaining tasks are redistributed and rebundled into roles and how work systems are reorganized. The patterns also suggest refinements for how researchers perform labor impact analyses at the task, role, and sector levels.