Institute for Work & Employment Research
The AI Work Redesign Playbook
About the Project
The actual impact of an AI tool depends on how it is designed and implemented, but frontline workers often have little say in those decisions. AI can conceivably improve work by reducing low-value tasks and streamlining operations, so that employees can concentrate on the work they find most meaningful.
Those changes could benefit many workers who feel overloaded, burned out, and tempted to quit the jobs that they have trained for and on which their families rely. Of course, AI can also degrade job quality by amplifying productivity pressures and worries about job security. Those concerns often prompt AI resistance and avoidance—leading to failed AI pilots and limited worker investment in developing their AI skills.
To address these challenges, a research team based in the Institute for Work and Employment Research (IWER) at the MIT Sloan School of Management will test and iterate concrete strategies for integrating frontline workers’ voices and insights into the development and deployment of AI. In Phase 1 and 2 of this project, we will test these frameworks in health care jobs such as medical coders, respiratory technicians, billing staff, medical assistants, and more.
These pilots will be codesigned, implemented, and evaluated in partnership with the health system we are working with and the union representing many of those workers. We hypothesize that these engagement strategies will support more complete implementation of AI tools and greater development of AI skills, while reducing overload, burnout, and turnover, both in this health care setting and beyond. We hypothesize that frontline engagement will improve work for all employees; consistent with some of our past research, we expect benefits will be even stronger for women and those with family caregiving responsibilities.
In Phase 3, lessons from the health care pilots will be integrated with insights from research and cases in other industries and from our expert advisors from MIT and across sectors. We will prepare and disseminate a Playbook offering concrete, tactical guidance on how to effectively and efficiently learn from and engage with frontline workers when designing and implementing AI solutions.
Research Partners
We are pleased to be working with UMass Memorial Health and the SHARE AFSCME union on this project.
The Research Team
Erin Kelly
Sloan Distinguished Professor of Work and Organization Studies
Erin L. Kelly is the Sloan Distinguished Professor of Work and Organization Studies at the MIT Sloan School of Management and Co-Director of the MIT Institute for Work and Employment Research. Kelly’s research has been published in many top…
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Kate Kellogg
Hear name pronounced.David J. McGrath jr (1959) Professor of Management and Innovation
Kate Kellogg is the David J. McGrath Jr Professor of Management and Innovation, a Professor of Business Administration at the MIT Sloan School of Management. Kate's research focuses on helping knowledge workers and organizations develop and…
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Zana Buçinca
Assistant Professor, Starting in Fall 2026
Buçinca, whose research interests include human-AI interactions and responsible AI, will have a shared appointment at the MIT Sloan School of Management and the MIT Department of Electrical Engineering and Computer Science.
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Thomas Kochan
George Maverick Bunker Professor of Management, Emeritus
Thomas A. Kochan is the Post-Tenure George Maverick Bunker Professor at the MIT Sloan School of Management and a faculty member in the MIT Institute for Work and Employment Research. Kochan focuses on the need to update America's work and…
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Palak Shah
Senior Fellow, MIT Institute for Work & Employment Research
From 2013 to 2024, Shah served in executive roles at the National Domestic Workers Alliance, where she was instrumental in shaping the organization’s strategic vision and led innovation initiatives.
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Institute for Work and Employment Research Bringing Worker Voice Into Generative AI
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Ideas Made to Matter AI Expert Spotlight: Kate Kellogg
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IWER Overload: How Good Jobs Went Bad and What We Can Do about It
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Extended Abstracts of the CHI Conference on Human Factors in Computing Systems|2024 Optimizing Decision-Maker’s Intrinsic Motivation for Effective Human-AI Decision-Making
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IWER Warehouse Work and Well-Being Study
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SSRN | December 2025 A Field Guide to Deploying AI Agents in Clinical Practice
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Proceedings of the 30th International Conference on Intelligent User Interfaces| 2025 Personalising AI Assistance Based on Overreliance Rate
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Institute for Work and Employment Research Trends in Worker Voice and Worker Activism: An IWER Research Compendium
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