When Technology Manages: Workers’ Demands and Union Responses to AI and Emerging Digital Tools

AI and digital technologies are transforming work, yet their consequences for unions remain poorly understood. We develop a framework that distinguishes technologies by their role in the labor process: displacement, augmentation, and monitoring. These types shape unionization through co-worker interaction, perceived threat, and employment stability. Displacement can reduce unionization by shifting jobs into less organized sectors, although stable insiders may still mobilize when interaction and perceived threat are high. Monitoring can prompt demands for collective protection but can also fragment work by individualizing tasks. Augmentation may preserve bargaining power when new tools raise skill requirements and generate training claims. We test these expectations using a two-level research design. First, drawing on European Social Survey data from 15 countries (2012–2024), we link occupations to distinct types of digital technologies—such as remote monitoring and intelligent logistics—and examine their associations with union membership, workplace autonomy, job satisfaction, and political attitudes. Second, we provide the first systematic analysis of unions’ contractual responses by examining over 40,000 Canadian collective bargaining agreements (1993–2025) using NLP and an LLM-exposure-based index. Results show substantial heterogeneity. Exposure to machine learning, embedded systems, remote monitoring, and smart mobility predicts higher union membership, even as it is associated with lower autonomy, job satisfaction, and greater political alienation. By contrast, exposure to food-ordering platforms predicts lower unionization and greater insecurity. In bargaining, we document negotiations over training, health, safety, notice of displacement, and joint governance. Overall, technological change is not monolithic; its labor effects depend on workplace technologies.