This study aims to identify the factors that influence human workers' acceptance of AI robots and to analyze the moderating effect of job characteristics and differences across industrial sectors. To this end, a measurement model and corresponding indicators were developed by comprehensively reviewing interdisciplinary theoretical backgrounds, including the Technology Acceptance Model, Algorithm Aversion Theory, Human–Robot Interaction Theory, Media Equation Theory, and Task–Technology Fit Theory. The acceptance factors for AI colleagues were operationalized using measurement indicators reflecting trustworthiness, perception of fairness, external similarity, and emotional distance. Based on a survey conducted among workers in Korean industries, the perception of fairness and emotional distance were found to have a significant effect on acceptance. Job characteristics were found to exert a moderating effect on the relationship between trustworthiness and acceptance, and between the perception of fairness and acceptance. Furthermore, the financial industry exhibited higher acceptance than both the manufacturing and public sectors, and the service industry showed higher acceptance than the manufacturing sector. This study is expected to provide practical implications for organizational design and AI adoption strategies by identifying the determinants of AI robot acceptance, validating the moderating role of job characteristics, and quantifying industry-specific differences in acceptance levels.