Feasibility of Utilizing Advanced Artificial Intelligence Technologies to Improve Auditing Processes in the Country

Objective
Auditing, a cornerstone of ensuring the reliability, credibility, and transparency of corporate financial information, is vital to economic stability. The advent of information technology has revolutionized traditional auditing practices, rendering effective audits nearly impossible without technological integration. Artificial intelligence (AI) introduces both opportunities and challenges in accounting and auditing, particularly by enhancing data processing speed and quality. Although extensive research has explored AI’s role in auditing in developed countries, studies in developing nations, including Iran, remain limited. This study aims to evaluate AI’s potential to enhance auditing processes, focusing on its capacity to improve efficiency and quality across diverse economic contexts.
Methods
This applied study adopts a mixed-methods approach. The qualitative phase involved semi-structured interviews with 12 purposefully selected experts in auditing and AI, including members of the Iranian Association of Certified Public Accountants and academics. Interview data were analyzed using grounded theory through open, axial, and selective coding, with MaxQDA software employed for precise and efficient textual analysis. The quantitative phase utilized a survey of 200 auditors from various Iranian audit firms and organizations. The proposed model was validated using structural equation modeling (SEM) via SmartPLS to assess model fit indices.
Results
AI significantly improves audit quality by enabling efficient processing of large financial datasets, pattern recognition, and anomaly detection. Qualitative findings identified six key categories influencing AI adoption in auditing: causal conditions (environmental incentives, cultural, social, and political factors, and international pressures), contextual conditions (firm environment, accounting and financial systems, and financial incentives), core phenomenon (AI technology as the central element), strategies (establishing internal controls, AI training, designating responsible entities, setting standards, and promoting modern technologies), consequences (enhanced financial reporting quality, increased social trust, capital market development, and strengthened auditing profession), and intervening conditions (firm structure, corporate governance, industry competition, and managerial behaviors). These categories formed the foundation of the study’s model.
Conclusion
 The findings demonstrate that AI automates routine audit tasks, such as data entry and reconciliation, thereby enhancing accuracy and allowing auditors to focus on complex analyses. AI-driven tools enable real-time financial forecasting, anomaly detection, and error reduction, significantly improving audit processes. By leveraging historical data, AI supports financial forecasting, budgeting, and the identification of suspicious accounts. This study highlights AI’s benefits and challenges, offering practical solutions for auditors and managers to make informed strategic decisions about its adoption. Given AI’s role in risk reduction and accuracy enhancement, this research provides a practical framework for policymakers and practitioners to optimize this transformative technology, contributing to a deeper understanding of its operational potential and implementation challenges in auditing.