aiqbench analysis
This study redefines workplace AI by categorizing systems into three distinct agentic roles—assistant, arbiter, and manager—to analyze how decision authority reshapes worker accountability. The research highlights a critical "accountability-authority asymmetry," where workers bear responsibility for outcomes driven by AI systems that have shifted the actual power to decide or direct work. For practitioners, this underscores the necessity of designing AI architectures that explicitly align human oversight with the specific level of autonomy granted to the agent.
From Tool to Agent: How Worker Needs Reorganize Across the Agentic Roles of Workplace AI
arXiv:2605.03078v2 Announce Type: replace 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.
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.
10 pages. To appear at the 14th International Conference on Human-Agent Interaction (HAI '26). Revised from the previous version
Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC)
arXiv:2605.03078 [cs.AI]
(or arXiv:2605.03078v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.03078 Focus to learn more arXiv-issued DOI via DataCite
https://doi.org/10.1145/3841580.3841599 Focus to learn more DOI(s) linking to related resources
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 296 words
Related coverage

Presentation: Designing AI Platforms for Reliability: Tools for Certainty, Agents for Discovery
InfoQ · May 27, 2026

Digital States 2026: AI Moves From Experiment to Infrastructure
govtech.com · Sep 24, 2026

Lessons from scaling teams of coding agents: reach the frontier at 30% of the cost
Cohere · Oct 14, 2026

Podcast: The Future of AI: from Enterprise Adoption to Open Source Sovereignty
InfoQ · Sep 25, 2026