Abstract: As manufacturing and service systems evolve towards high levels of digitalization, networking, and intelligence, the focus of Industrial Engineering (IE) is shifting from relatively predictable production systems to complex systems characterized by cross-hierarchy interactions, strong coupling, and continuous evolution. The expansion of system scale, increased demand uncertainty, the integration of multi-source heterogeneous data, the deepening of human-machine collaboration, and the multi-objective conflicts introduced by sustainable development goals pose significant challenges to traditional core methods centered on modeling, prediction, and optimization in terms of complexity representation and decision support. Generative Artificial Intelligence (GenAI), with its capabilities in cross-modal understanding, conditional generation, and strategic distribution learning, offers a new technological pathway for IE to address the planning and operational problems of complex systems. This paper systematically reviews the developmental trajectory of AI-empowered IE, analyzes the characteristics of complexity in future manufacturing and service systems, and focuses on discussing the enabling mechanisms and application value of GenAI in scenarios such as manufacturing system planning, production management, quality control, human-machine collaboration, logistics optimization, and major public health events, supported by typical industrial cases. The research indicates that GenAI contributes to enhancing the flexibility and resilience of complex system decision-making, providing a new direction for the evolution of the Industrial Engineering methodological framework.