An Exploratory Study on Types of Human-AI Collaboration and Situational Fluidity in Enterprises in the Generative AI Era

The spread of generative AI is bringing revolutionary changes across industries and introducing a new perspective on human-AI collaboration. It is also reshaping corporate operations, requiring a new interpretation of collaborative structures between humans and AI. This study conducted interviews with corporate employees who apply generative AI tools in their work, and through their experiences, categorized and analyzed the structures of human-AI collaboration in real-world environments. The study established three collaboration types as analytical frameworks: “Human Augmented by AI,” “AI Supervised by Human,” and “Human–AI Teaming,” and examined them through in-depth interviews. The analysis revealed that collaboration structures operate in a flexible, hybrid manner depending on the nature and stage of the task. In repetitive tasks, AI assists humans, while human supervision strengthens in judgment-based work. In cases requiring creativity or complementary skills, humans and AI collaborate in a mutually reinforcing manner. These findings demonstrate that human-AI collaboration is not a fixed division of roles but a dynamic structure shaped by the task’s type and situation, providing insights into the design of human-centered collaboration in the generative AI era.