The rapid evolution of Artificial Intelligence has fundamentally altered the landscape of conventional Business Process Management Systems, transforming them into intelligent, adaptive frameworks capable of achieving enhanced autonomy and operational effectiveness. This proposal presents an AI-augmented Business Process Management System developed through the integration of machine learning, robotic process automation, and conversational AI, thereby establishing a framework for structured autonomy and optimising decision-support mechanisms. The envisioned framework emphasises advanced automation, transparent explainability, and robust adaptability in dynamic business environments. It specifically addresses pressing challenges such as context-aware explainability, expedited process agility, and enhanced human-AI collaborative trust. Experimental evaluations demonstrate that the proposed RPABPM methodology consistently achieves over 99.8% automation efficiency, outperforming existing models in environments with substantial task throughput. These findings substantiate the significant influence of AI-driven process management systems in optimizing operational performance, fostering greater transparency, and reinforcing confidence between human operators and intelligent agents.