ARTIFICIAL INTELLIGENCE IN AUDITING: CHALLENGES OF DEVELOPING COUNTRIES

Abstract

The rapid development of artificial intelligence (AI) is radically transforming auditing activities, ensuring not only the automation of routine processes and the transition to continuous monitoring, but also changing the role of the auditor. Technologies such as big data analysis, machine learning, and Explainable AI (XAI) are becoming more and more commonplace in the audit community. Meanwhile, for developing countries, the process of integrating AI auditing poses a double challenge. On the one hand, technological advances, increasing data volume, and rapid globalization make the integration of AI into human activities not just a desirable improvement, but also a prerequisite for competitiveness. Moreover, AI allows not only to improve productivity, but also to compensate for human resources to some extent. On the other hand, there are a number of systemic limitations to the implementation of AI, which not only slow down the integration process but also make it impossible in some places. All this requires an integrated approach to the application of AI technologies in these countries. The available publications in this area are mostly focused on developed economies, leaving a research gap on a comprehensive model for implementing AI audits in resource-constrained settings. The purpose of this article is to identify key system barriers and develop a practical integrated framework for AI audit readiness in these countries. The study conducted in this article identifies and systematizes the systemic gap between global AI audit models, based mainly on developed infrastructure, and the realities of developing countries. Five interdependent key factors were identified, such as data infrastructure (fragmentation, poor quality, unstable power supply), technological readiness (lack of computing resources, need for new software solutions, weak cybersecurity), human resources (critical shortage of specialists, insufficient adaptation of educational programs), organizational capacity (lack of holistic strategies, informal procedures) and regulatory support (lack of comprehensive laws,  non-compliance of internal norms with international standards). To overcome this fragmentation, a practical integrated readiness framework is proposed, which unites all these components and can serve as a tool for diagnosing and planning AI integration. Thus, the study comprehensively analyzed and systematized the key barriers to the implementation of AI in auditing in developing countries, confirming the incompatibility of Western models with their resources. It showed that the successful digital transformation of audit requires not only technological investments but also the parallel strengthening of data infrastructure, staff training, and the creation of a strong regulatory framework. The proposed readiness framework is the basic guide that facilitates this process.