The role of AI is changing and reshaping auditing processes. However, the adoption of AI in financial auditing remains limited due to auditors' lack of expertise, resistance to change, and unclear regulatory laws. The present study uses Innovation Resistance Theory (IRT) as a base to identify several functional (active) or psychological (passive) barriers on resistance to adopting AI in financial auditing. Open-ended survey questions were used to gather the responses from 41 auditors. The analysis identified five barriers: tradition, image, value, risk, and usage barriers associated with AI adoption. The study identified several concerns, including poor data quality, AI-biased outputs, regulatory and compliance issues, lack of qualitative and subjective judgment skills, lack of knowledge and technical expertise, high cost, and many more. The study extends the IRT framework and evaluates the relevance of both psychological and functional barriers when adopting AI. The study examines the five barriers' impact on users and the significance of each on their resistance to AI adoption.