Integrating Artificial Intelligence in Audit Workflow: Opportunities, Architecture, and Challenges: A Systematic Review

Background: This paper is a systematic review of 100 peer-reviewed articles (2015–2025) related to artificial intelligence (AI) applications in the auditing field, and includes machine learning, natural language processing, robotic process automation, and other AI methods. Purpose: The paper delves into the integration of these AI technologies into the audit workflow; empirical implications of these technologies on audit effectiveness; efficiency and quality; and technical, organizational, and regulatory obstacles that suggest more widespread adoption is still limited. Methods: Five large-scale databases and other sources were searched and selected using PRISMA; structured data were extracted, assessed in quality and narrative, and thematically analyzed. Results: The discussion indicates that machine learning-based anomaly detection and predictive analytics, document analysis through NLP, and automation through RPA are becoming part of planning, risk assessments, control tests, and substantive procedures/reporting, with improvements in detection capabilities, coverage and efficiency reported in various empirical and design science studies. The review also presents common architectural models of AI-enabled audit processes, including layered data and governance, model development and oversight, orchestration and automation, auditor-facing applications, and human-in-the-loop controls. Conclusions: The article proposes an AI-based audit workflow reference architecture and summarizes evidence on opportunities, threats, and implementation obstacles, highlighting gaps in longitudinal assessment, comparative evaluation of AI methods, and regulatory recommendations. The results have practical implications for auditors, standard-setters, and system designers seeking to revise the audit approach and regulations to enable AI-driven assurance.