The integration of artificial intelligence (AI) in auditing offers significant potential for technological advancements, but its widespread adoption brings with it numerous challenges. My thesis investigates these challenges by examining the use of AI in a variety of auditing contexts across multiple branches of a Chinese accounting firm. This research facilitates the development of a framework for AI implementation in external audits, emphasizing the crucial roles of client attitudes and auditors’ risk tolerance. It examines how social factors influence auditors’ acceptance of augmented AI systems, specifically in joint audits, revealing that positive client attitudes and supportive regulatory environments significantly enhance AI acceptance. The study goes on to explore auditors’ AI algorithm aversion in general audit environments, driven by perceived risks and a preference for conventional methods, through the lenses of innovation resistance theory and the technology readiness index. The results indicate that psychological barriers significantly contribute to algorithm aversion, while functional barriers are less impactful, and clients’ technology readiness does not moderate these relationships. These findings provide practical implications for balancing AI innovation with auditors’ concerns, offering comprehensive insights into the challenges and factors influencing AI adoption in auditing.