Toward a Human-AI Task Tensor: A Taxonomy for Organizing Work in the Age of Generative AI
This work addresses the need for a systematic approach to study human-AI interaction in tasks, offering a foundational taxonomy for organizing research and decision-making in the age of generative AI.
The authors tackled the problem of understanding the impact of generative AI on human work by introducing a framework called the human-AI task tensor, which organizes tasks along eight dimensions to provide analytical tractability and practical insight for organizational decision-making.
We introduce a framework for understanding the impact of generative AI on human work, which we call the human-AI task tensor. A tensor is a structured framework that organizes tasks along multiple interdependent dimensions. Our human-AI task tensor introduces a systematic approach to studying how humans and AI interact to perform tasks, and has eight dimensions: task definition, AI integration, interaction modality, audit requirement, output definition, decision-making authority, AI structure, and human persona. After describing the eight dimensions of the tensor, we provide illustrative frameworks (derived from projections of the tensor) and a human-AI task canvas that provide analytical tractability and practical insight for organizational decision-making. We demonstrate how the human-AI task tensor can be used to organize emerging and future research on generative AI. We propose that the human-AI task tensor offers a starting point for understanding how work will be performed with the emergence of generative AI.