When AI improves entire workflows in the workplace matters most
When Does AI Augment Work? A Workflow-Level Framework for Human-Agent Collaboration
Artificial IntelligenceComputers and SocietyHuman-Computer Interaction
Summary
AI is often judged by how well it automates small tasks or how widely it is used. This paper says that to truly understand AI’s value at work, we should look at how it helps people cooperate with machines across whole workflows, not just individual jobs. The authors define six important conditions that make AI helpful, such as clear benefits, human control, and supporting workers’ growth over time. They illustrate this with a study on AI helping with social surveys and suggest their ideas can guide companies and policymakers planning for the future of work.
What this means in practice
- •For workplace designers: Create AI-enhanced workflows that ensure meaningful human control and lasting value beyond task automation.
- •For policy makers: Develop guidelines for AI use in work that promote accountability, worker learning, and career growth.
A position paper. It proposes an approach and reports no results.
Authors
CIVIC-AI Collaboration, :, Jiaying Wu, Caleb Ziems, Raymond Chan, Nancy F. Chen, Corlyss Chua, Gerard Chung, Jungpil Hahn, Wee Sun Lee, Zhengyuan Liu, Jamie Ng, Desmond C. Ong, Jeryl Ong, Da Ren Soon, Tianqi Song, Zhi-Xuan Tan, Sixing Tao, Emily Yang, Yajing Yang, Stella Xin Yin, Min-Yen Kan, Diyi Yang
Abstract
We aim to characterise the value of artificial intelligence in the workplace. Current studies largely measure this value in terms of the current automation capabilities and public adoption of AI. However, such metrics ignore the greater impacts of human--agent collaboration in transforming the nature of work. To account for this, we must expand the scope of our analysis beyond atomised tasks of today, and instead focus on how AI can augment entire workflows of the future. To ground this analysis, we establish a precise definition of AI augmentation comprising six conditions, spanning durable net value, meaningful human control, accountability and recovery, and long-term human development through learning, career pathways, and job purpose. We elaborate on these conditions and apply the framework in a case study of AI-mediated social surveys. We conclude by outlining how organisations, researchers, and government leaders can use this framework to make sense of the future of work.