From Tool to Agent: How Worker Needs Reorganize Across the Agentic Roles of Workplace AI

Abstract

As AI systems gain agency in workplaces, workers increasingly interact with systems that do more than support tasks: they make decisions, allocate work, and shape how workers communicate with others. We interviewed 16 workers in healthcare, finance, and management about their daily interactions with workplace AI systems. Across these domains, AI occupied three agentic roles: \emph{assistant}, supporting worker decision-making; \emph{arbiter}, making consequential decisions that workers had to explain to downstream stakeholders; and \emph{manager}, directing work and displacing social structures through which work had been negotiated and learned. We find that worker needs depended on how decision authority and accountability were configured around the AI system. Explanation, control, accountability, and communication each took different meanings depending on whether AI supported, decided, or directed work. We contribute a role-based account of workplace human-agent interaction and identify accountability-authority asymmetry as a mechanism through which AI deployments can place responsibility on workers for outcomes whose authority has shifted to AI. We discuss implications for designing workplace AI around both the agent and its surrounding agency configuration.