Artificial Intelligence Integration in Internal Auditing and its Influence on Error Detection and Reduction: Empirical Evidence from Zimbabwean Private Companies

Abstract

AI is revolutionising internal auditing world-wide in terms of accuracy, speed and error detection yet it still not widely used in private sector companies in Zimbabwe. Zimbabwe is also still using the manual and traditional approach to auditing which poses a higher risk for material misstatement, late reporting of transactions and operational ineffectiveness, hence the digitalisation gap. The aim of this research was to evaluate AI adoption in internal audit and to explore its effects on error detection and mitigation. The objectives of the study were to investigate the extent and impact of using AI in auditing on error detection level, as well as the characteristics of organisation and technology affecting AI usefulness. The paper is grounded in the Technology–Organization–Environment (TOE) framework, Resource-Based View (RBV), and Technology Acceptance Model (TAM), which collectively offer a broad theoretical insight into AI adoption and performance effects. It carried-out a systematic review to synthesize empirical evidence of AI adoption applications, audit error reduction, and organisational and technological enablers that lead to successful adoptions. Results reveal that the adoption of AI increases error detection, operational effectiveness and compliance while staff proficiency and ICT investment are important antecedents for audit accuracy. Despite these advantages, there are limitations in the form of lack of digital skill, budget constraints and weak governing system that dampen effectiveness in Zimba wean enterprises. The AI application significantly increases the internal audit performance with the dimensions of human resource capacity, organisational readiness and regulatory compliance. This has the broader implications of standardising AI audit frameworks, build capacity and introduce policy mechanisms in order to reinforce governance and operational performance. Suggestions are made in the areas of AI governance, professional education and training, ICT investment, regulatory oversight, and auditor-technology provider interaction to enhance audit quality in Zimbabwe.