DOI: 10.24818/jamis.2025.03003
Vol. 24, No. 3, pp. 456-478, 2025
© 2025. This work is openly licensed via CC BY 4.0.
Author(s): Kishore Singh1,a, Mario Bojilova and Peter Besta
a CQ University, Australia
1 Corresponding author: Kishore Singh, School of Business and Law, Central Queensland University, 160 Ann Street Brisbane, Queensland, Australia, 4001, email: k.h.singh@cqu.edu.au
Keywords: artificial intelligence, auditing, audit quality, risk management, machine learning, integration
JEL codes: M40, M42, O30, O32, O33
Abstract
Research Question: What are the challenges to implementing AI in organizations and how can they be overcome?
Motivation: The rapid growth of artificial intelligence (AI) presents both opportunities and challenges for organizations. While AI can enhance efficiency, accuracy, and strategic decision-making, implementation is often constrained by workforce readiness, ethical concerns, and system integration issues. Despite increasing interest, limited research explores how organizations navigate these complexities in practice.
Idea: This paper investigates the integration of artificial intelligence (AI) into auditing, focusing on the challenges, strategies, and outcomes of deployment in the Australian context. Using a qualitative case study approach, it demonstrates how tools such as machine learning, natural language processing, and robotic process automation can enhance audit efficiency, accuracy, and risk management.
Data: A semi-structured interview format was adopted to collect responses from industry professionals working with AI. The open structure enabled additional exploration of individual circumstances, ensuring that unanticipated but important topics could be investigated.
Findings: The study highlights the need for robust data governance, ethical alignment, and the redesign of audit workflows. While AI enhances automation, auditors remain critical for nuanced judgment, interpretation, and stakeholder trust. Building internal expertise through structured upskilling, certification, and collaborative learning is essential, alongside the use of bias detection tools, fairness-aware models, and transparent governance structures. These measures are central to responsible AI adoption and the preservation of audit integrity.
Contribution: This study offers a practical roadmap for AI adoption in auditing, addressing system integration, workforce upskilling, and bias mitigation through transparent and ethical model design. Academically, it extends theories of technology adoption in professional services by highlighting the interaction of technical, cultural, and ethical dimensions. It also identifies directions for future research, particularly concerning transparency, explainability, and the convergence of AI with other emerging technologies.
Full paper at: http://online-cig.ase.ro/jcig/art/24_3_2.pdf
