The impact of artificial intelligence on internal audit performance: optimization of methods and reliability

Abstract

Despite the growing interest in artificial intelligence in audit functions, few studies have empirically analyzed the differentiated impact of its applications on internal audit performance, particularly in the context of Moroccan organizations. This study aims to fill this gap by examining the influence of AI integration, operational task automation, big data utilization, and audit report automation on internal audit performance.

A quantitative approach was adopted through a questionnaire administered to 103 internal auditors and professionals using artificial intelligence tools. The data were analyzed using the PLS-SEM method with SmartPLS.

The results show that operational task automation (β = 0.341; p = 0.009) and big data utilization (β = 0.406; p = 0.001) have a positive and significant effect on internal audit performance, thus validating hypotheses H2 and H3. In contrast, overall, AI integration (β = 0.002; p = 0.989) and audit report automation (β = 0.155; p = 0.098) do not show a significant effect, leading to the rejection of hypotheses H1 and H4.

These findings indicate that internal audit performance depends more on specific uses of artificial intelligence than on its overall adoption. The study thus provides an empirical contribution to the literature on the digital transformation of internal audit in emerging economies.

JEL classification : M42, M15, O33, C38.
Article type : Empirical research.