Digital transformation and the increase in the volume and complexity of financial data have presented auditing with new challenges and increased the likelihood of human error at different stages of the auditing process. In such circumstances, artificial intelligence can play a role as a complementary tool in identifying patterns, discovering anomalies, predicting high-risk events, and increasing the accuracy and speed of audit proceedings. The present study aims to explain the impact of artificial intelligence in auditing and reducing human error, with a review-analytical approach based on a library study and content analysis of reputable research. The results show that the use of artificial intelligence provides the possibility of moving from sample-based auditing to a more comprehensive and analytical auditing and can reduce some of the errors caused by fatigue, limited attention, cognitive biases, and manual calculations. However, this technology does not replace the auditor's professional judgment, and its role is more defined in strengthening decision-making, improving the quality of evidence, and increasing the efficiency of the auditing process. Also, the successful deployment of AI in auditing requires the existence of data infrastructure, information security, appropriate governance, and the development of professional skills of auditors. Accordingly, the future of auditing will be realized in the synergy between machine analytical power and human professional judgment.