This paper introduces a novel framework for context-aware sliding work sharing (SWS) to enhance human-AI collaboration. It addresses the challenge of dynamically balancing human and machine contributions by leveraging real-time contextual information and machine confidence levels. The proposed system integrates advanced context awareness mechanisms—comprising context modelling, monitoring, and extraction—with adaptive SWS management to facilitate informed task delegation between humans and AI/robots. By adjusting autonomy levels based on situational complexity and user expertise, the approach aims to improve decision-making, efficiency, and trust in AI-supported environments. A logistics-based use case demonstrates the practical applicability and potential benefits of the system.