Digital technologies are critical in advancing a human-centered approach to warehouses that account for productivity and staff well-being. These technologies generate data addressing the negative conditions affecting the well-being of staff during order picking (OP), a labour intensive activity. This study analyzes artificial intelligence (AI)-enabled vision systems to enhance human-centricity and improve the generation and analysis of information about tasks executed by staff in OP. The study presents results from a pilot study in automotive manufacturing applying a design science research approach. The results show that AI-enabled vision systems enhance task identification, analysis, and efficiency in OP. The study suggests five actions including staff information, data acquisition, access restriction, data storage, and protection addressing the privacy concerns of these systems. The study discusses how these systems can integrate staff well-being by identifying human factors and outcomes. It offers three contributions: (1) an overview of activities for collecting task information through AI-enabled vision systems in human-centered OP; (2) evidence that existing architectures for human-centered manufacturing are essential for managing privacy implications; and (3) a discussion of the systems’ impact on human factors and performance, and guidelines for developing and implementing these systems in future studies and operational environments.