Artificial Intelligence (AI), Audit Quality, and the Future of Professional Judgment: Policy and Governance Challenges in Auditing - A Systematic Literature Review
Jul 2026· International journal of computer information systems and industrial management applications· 0 citations
TL;DR
It is concluded that the auditing profession is at a critical juncture, requiring a concerted effort from regulators, standard-setters, firms, and educators to navigate the transformative impact of AI.
Abstract
The integration of artificial intelligence (AI) into auditing has created a paradigm shift, presenting both unprecedented opportunities to enhance audit quality and significant policy challenges that threaten the foundations of professional judgment. This systematic literature review analyses peer-reviewed articles to synthesize the current landscape of AI in auditing and identify the primary policy challenges confronting the profession. Our analysis reveals a fundamental tension between the automation of audit tasks and the preservation of professional skepticism and judgment. Key themes emerging from the literature include the paradox of professional judgment in an automated environment, the double-edged sword of AI in enhancing audit quality while introducing new risks, the critical need for transparency and explainability in AI systems, the pervasive threat of algorithmic bias, and the significant gaps in regulatory frameworks and professional standards. The findings indicate that while AI offers powerful tools for data analysis, fraud detection, and risk assessment, its adoption is hampered by a complex web of ethical, technical, and organizational barriers. The primary policy challenges identified include regulatory lag, the erosion of professional identity, new quality assurance demands, evolving competency standards, the need for robust ethical frameworks, unresolved liability issues, and a lack of standardization. This review concludes that the auditing profession is at a critical juncture, requiring a concerted effort from regulators, standard-setters, firms, and educators to navigate the transformative impact of AI. I propose a research agenda focused on the long-term effects of AI on professional judgment, the effectiveness of governance models, and the development of new audit methodologies that effectively integrate human and machine intelligence.
The adoption of artificial intelligence (AI) in government audit processes, ranging from data analytics to algorithm-based anomaly detection, promises significant efficiency gains, yet raises global concerns regarding automation bias, the tendency of auditors to accept AI output without adequate critical evaluation. This study aims to analyze the effect of AI reliance on the professional judgment quality of government auditors in Aceh, and to test and explore the moderating role of professional skepticism in this relationship. This study employs an explanatory sequential mixed methods design, beginning with a quantitative phase through a survey of 110 government auditors from the Aceh Inspectorate, Regency/City Inspectorates, the Aceh Representative Office of BPKP, and the Aceh Representative Office of BPK RI, analyzed using Moderated Regression Analysis (MRA), followed by a qualitative phase through in-depth interviews with ten selected auditors. The (illustrative) quantitative results reveal a counter-intuitive finding: AI reliance has a significant negative direct effect on professional judgment quality, yet professional skepticism significantly moderates this relationship by buffering (weakening) the negative effect. The qualitative phase reveals the underlying mechanism: auditors with high trait skepticism treat AI as a complementary tool that is still cross-verified, whereas auditors with low skepticism tend to treat AI as a substitutive tool whose output is accepted uncritically, a pattern informants themselves described as 'thinking laziness resulting from excessive trust in machines.' This study offers novelty as one of the first studies to empirically examine the specific construct of AI reliance, rather than general audit technology use, within the context of public-sector auditors in a special-autonomy region such as Aceh, while extending automation bias theory by positioning professional skepticism as a cognitive buffering mechanism. The practical implications underscore the urgency of strengthening professional skepticism training integrated with AI literacy for government auditors, so that the digital transformation of auditing does not come at the expense of professional judgment quality, which remains the core of the audit profession itself.
Luthfiar Ramiady, Hendri Bin Muhammad Nur· Sumber Informasi Manajemen B...· 0 citations
Artificial intelligence (AI) is fundamentally reshaping the
auditing profession, challenging traditional competency
frameworks and redefining the scope of the auditor’s role.
This study is based on the premise that, beyond traditional
financial audit tasks, the contemporary auditor is
increasingly expected to contribute to audit committee
governance, sustainability (ESG) assurance, as well as
the direct application of AI-based tools in audit
engagements. Despite the growing academic and
professional interest in AI adoption, a comprehensive and
integrated framework capturing the full spectrum of AI-
related competencies required across all auditor roles
remains insufficiently developed in the literature. This
paper addresses this gap through a Structured Literature
Review (SLR) that examines 22 peer-reviewed articles
indexed in Web of Science and published between 2019
and 2025, identifying and synthesizing evidence on how
AI is reshaping auditor competencies across four
interconnected roles: financial auditor, audit committee
member, ESG assurance provider, and user of AI tools.
Based on the synthesized evidence, the authors propose
an integrated competency framework for the auditor
prepared for the AI era, structured around six competency
dimensions and four professional roles, with direct
implications for professional bodies, Continuing
Professional Development (CPD) programmes, and
certification requirements.
Elena Claudia Badea (florea), Andreea-Larisa Olteanu (Burca), M. Bunea et al.· Audit Financiar· 0 citations
It is suggested that AI enhances fraud detection, risk assessment, and audit quality, while raising concerns regarding algorithmic bias, transparency, and professional skepticism.
This critical review aims to analyze the article by Baldwin, Brown, and Trinkle (2006) titled Opportunities for Artificial Intelligence Development in the Accounting Domain: The Case for Auditing to show that auditing is a very potential field for AI application.
Muslimin, Nahwani Fadelan, Wahid Hasyim et al.· Journal of Creative Power a...· 0 citations
A systematic review of the literature on methodologies, frameworks, and techniques for auditing AI systems, focusing on legal and ethical considerations and compliance with regulations, reveals gaps in current auditing practices and highlights the importance of incorporating AI value chain stages and AI maturity levels into auditing frameworks.
Usman Shahbaz, A. Beheshti, B. Abedin et al.· ACM Computing Surveys· 0 citations
Artificial Intelligence (AI) has emerged as one of the most transformative technologies influencing organizational governance, financial oversight, and risk management practices worldwide. The integration of AI into internal audit functions has significantly altered the traditional audit landscape by enhancing operational efficiency, improving fraud detection capabilities, strengthening risk assessment procedures, and enabling real-time auditing practices. This research paper examines the transformative role of AI technologies such as machine learning, neural networks, natural language processing, robotic process automation, and predictive analytics in reshaping internal audit operations. The study explores how AI-driven systems automate repetitive audit tasks, analyze large volumes of structured and unstructured data, and improve audit accuracy while reducing operational costs. Furthermore, the paper evaluates the challenges associated with AI adoption, including ethical concerns, cybersecurity risks, data privacy issues, technological dependence, and skill gaps among auditors. A comparative analysis between traditional and AI-enabled audit practices is also presented to assess the effectiveness and efficiency of AI-based auditing systems. The study concludes that AI is not replacing internal auditors but transforming their roles into more strategic, analytical, and advisory-oriented functions. Organizations that successfully integrate AI into their audit frameworks can achieve greater transparency, stronger governance, and improved organizational resilience in an increasingly digital business environment.
F. Raidah, M. Jobair, Md. Halimuzzaman et al.· American Journal of Financia...· 0 citations
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