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

arXiv: https://arxiv.org/abs/2605.03078 · HTML: https://mlsanity.com/paper/2605.03078/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 57 references that are in the map.
- First posted on arXiv 4 May 2026.

## Builds on (most cited first, 22 of 22)

- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [Improving fairness in machine learning systems: What do industry practitioners need?](https://mlsanity.com/paper/1812.05239/) (2018)
- [Questioning the AI: Informing Design Practices for Explainable AI User Experiences](https://mlsanity.com/paper/2001.02478/) (2020)
- [Harms from Increasingly Agentic Algorithmic Systems](https://mlsanity.com/paper/2302.10329/) (2023)
- [Expanding Explainability: Towards Social Transparency in AI systems](https://mlsanity.com/paper/2101.04719/) (2021)
- [Levels of AGI for Operationalizing Progress on the Path to AGI](https://mlsanity.com/paper/2311.02462/) (2023)
- [Levels of Autonomy for AI Agents](https://mlsanity.com/paper/2506.12469/) (2025)
- [Fully Autonomous AI Agents Should Not be Developed](https://mlsanity.com/paper/2502.02649/) (2025)
- [Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support](https://mlsanity.com/paper/2204.02310/) (2022)
- [Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens](https://mlsanity.com/paper/2102.00593/) (2021)
- [How Knowledge Workers Think Generative AI Will (Not) Transform Their Industries](https://mlsanity.com/paper/2310.06778/) (2023)
- [Understanding Frontline Workers' and Unhoused Individuals' Perspectives on AI Used in Homeless Services](https://mlsanity.com/paper/2303.09743/) (2023)
- [VeriPlan: Integrating Formal Verification and LLMs into End-User Planning](https://mlsanity.com/paper/2502.17898/) (2025)
- [From Future of Work to Future of Workers: Addressing Asymptomatic AI Harms for Dignified Human-AI Interaction](https://mlsanity.com/paper/2601.21920/) (2026)
- [REX: Designing User-centered Repair and Explanations to Address Robot Failures](https://mlsanity.com/paper/2405.16710/) (2024)
- [Generative AI Uses and Risks for Knowledge Workers in a Science Organization](https://mlsanity.com/paper/2501.16577/) (2025)
- [When Your Boss Is an AI Bot: Exploring Opportunities and Risks of Manager Clone Agents in the Future Workplace](https://mlsanity.com/paper/2509.10993/) (2025)
- [Regulating Algorithmic Management: A Multi-Stakeholder Study of Challenges in Aligning Software and the Law for Workplace Scheduling](https://mlsanity.com/paper/2505.02329/) (2025)
- [The Values of Value in AI Adoption: Rethinking Efficiency in UX Designers' Workplaces](https://mlsanity.com/paper/2603.05848/) (2026)
- [Consent as a Foundation for Responsible Autonomy](https://mlsanity.com/paper/2203.11420/) (2022)
- [Investigating Why Clinicians Deviate from Standards of Care: Liberating Patients from Mechanical Ventilation in the ICU](https://mlsanity.com/paper/2402.13464/) (2024)
- [Fulfillment of the Work Games: Warehouse Workers' Experiences with Algorithmic Management](https://mlsanity.com/paper/2508.09438/) (2025)

## Abstract (arXiv)

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: assistant, supporting worker decision-making; arbiter, making consequential decisions that workers had to explain to downstream stakeholders; and 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.