Researchers have introduced a framework called HAAS, designed to dynamically allocate tasks between humans and artificial intelligence systems based on policy awareness. This approach recognizes that the division of labor between humans and AI is not a simple binary decision, but rather a complex interplay that depends on various factors such as context, fatigue, and stakes. HAAS aims to balance efficiency, oversight, and human capabilities by adaptively adjusting task assignments. The framework takes into account the operational nuances of human-AI collaboration, where tasks are often shared or performed in complementary roles. By incorporating policy awareness, HAAS enables more effective governance of task distribution, allowing organizations to optimize their workflow and make better use of human and AI resources1. This matters to practitioners because it offers a more realistic and flexible approach to human-AI collaboration, which can lead to improved productivity and decision-making in complex environments.