Global labor policy and artificial intelligence: advocacy coalitions for the future of work

Purpose This article examines how transnational actors in global labor policy have responded to the social and labor risks posed by artificial intelligence (AI), including the recent disruptions of generative AI, from the mid-2010s to the 2020s. Design/methodology/approach Using process tracing and document analysis, the study applies the Advocacy Coalition Framework (ACF) to investigate how competing belief systems shape policy outcomes at the supranational level, operationalizing the “softness” or “hardness” of regulatory instruments as the key dependent variable. Findings The analysis identifies three advocacy coalitions - deregulation, hard regulation, and soft governance - that have shaped AI governance at work. The findings reveal that global policy outputs are dominated by soft governance mechanisms championed by multilateral agencies and international employer organizations, resulting in “soft” regulations like human-centric principles and voluntary ethical guidelines. Consequently, the hard regulation coalition of international unions and global civil society has had limited success in institutionalizing binding frameworks to mitigate specific worker risks, such as job displacement and deskilling. Originality/value The study contributes theoretically by extending the ACF to the supranational level and explaining coalition-building dynamics within global labor policy and governance. It expands the literature by analyzing non-traditional transnational actors, such as accounting firms and global think tanks, highlighting the structural social policy challenge of advancing enforceable labor protections when business-friendly and pro-innovation approaches dominate.