Boundaries of Generative AI Capabilities in Multi-tasking Environments

Recent advancements in Generative AI (GenAI) have the potential to significantly enhance productivity and reshape workflows. A critical debate in exposed industries is whether GenAI should primarily be deployed as a labor-augmenting copilot, or as an autonomous agent that directly substitutes for human workers. In practice, jobs are typically designed as task bundles to balance specialization gains against coordination costs. Therefore, evaluating GenAI as an autonomous worker requires assessing not only its performance on isolated tasks, but also its ability to handle a multi-task bundle. To study this topic, we collaborate with a leading customer service company. Customer service is a typical multi-task job, comprising a bundle of tasks ranging from product consultation to personalized recommendation. These tasks differ fundamentally in their nature: some are standardized and require strict factual accuracy, while others are creative and require strategic persuasion to close the sale. To systemically evaluate GenAI’s performance in isolated tasks and task bundling relative to human workers, we will run three randomized controlled experiments (RCTs) that vary the degree of task bundling while holding other settings constant. Through these investigations, this study aims to inform the optimal design of human-AI workflows, providing micro-foundations for understanding the boundaries of autonomous agents versus augmenting copilots in complex work environments.