Human-AI Integration in Industry 5.0: Mapping Relational Patterns among Antecedents, Mechanisms, and Outcomes

The diffusion of Artificial Intelligence (AI) within the Industrial Internet of Things is reshaping industrial systems and accelerating the transition toward human-centered innovation. Yet research on AI adoption in industrial environments remains fragmented, predominantly technology-centered, and largely based on context-bound empirical settings. Existing studies often examine isolated variables, limiting generalizability and overlooking how employee-related antecedents, alternative human-AI integration mechanisms, and their positive and negative consequences jointly influence process innovation and information integration. This study addresses these gaps by developing an integrated Antecedent-Process-Outcome framework of employee-AI integration. A taxonomy of 70 constructs is consolidated from the literature and empirically examined through the coding of 162 AI-oriented industrial practices documented in peer-reviewed case studies and application-based research. Using multi-layer association analyses, the study reconstructs a highly interconnected network linking employee capabilities, organizational conditions, distinct integration mechanisms, and multidimensional effects, including performance, knowledge development, engagement, and adverse reactions. The findings highlight a systematic distinction between augmentation-oriented and automation-oriented configurations. Collaborative and augmentative mechanisms of human-AI collaboration show stronger alignment with enabling conditions and positive knowledge and performance dynamics, whereas automation-oriented arrangements display weaker connections with human-centric outcomes and learning trajectories. Network analysis further identifies structurally central constructs that differ from those most frequently emphasized in the emerging worker-AI integration literature, offering a more evidence-based prioritization of integration dimensions. Human-machine collaboration emerges as a socio-technical configuration in which preparatory conditions, agency distribution, and both beneficial and adverse effects jointly shape sustainable process innovation and long-term adaptive capacity.