This study applies an intersectional approach to address concerns aboutdiversity of data acquisition when applying computer vision systems in humanorder picking. The study draws empirical data from a single case study conductedat an automotive manufacturer. It identifies critical factors of intersectionality forthe use of vision systems to enrich data collection in human order picking at fourlevels including form and function, experience and services, systems and infrastructure,and paradigm and purpose. These findings are helpful for mitigating biasand ensuring accurate representation of the target population in training datasets.The results of our study are indispensable for enhancing human-centricity whenapplying computer vision systems, and facilitating the acquisition of unstructureddata in human order picking. The study contributes to enhancing diversity in humanorder picking, a situation that is highly relevant because of the variations in age,gender, cultural background, and language of staff. The study discusses theoreticalandmanagerial implications of findings, alongside suggestions for future research.