ADVANCING INTERNAL AUDITING IN NIGERIA THROUGH ARTIFICIAL INTELLIGENCE: CHALLENGES AND FRAMEWORK FOR ADOPTION

Abstract

Integration of Artificial Intelligence (AI) into internal auditing presents significant opportunities to improve efficiency, enhance accuracy, and strengthen risk management, particularly in tasks
involving large volumes of data. Despite its potential, the implementation of AI within internal audit functions remains complex, especially in developing economies. This article explores the specific barriers to AI adoption in internal auditing practices in Nigeria, highlighting challenges such as technical limitations, infrastructural deficits, organizational resistance, limited human resource
capacity, and regulatory constraints. By analyzing relevant theoretical frameworks, including the
Technology Acceptance Model (TAM) and the Theory of Technology Dominance (TTD), the study
identified core factors that influence the successful implementation of AI in internal audits. Further,
the article proposed a framework for adopting AI in auditing to support decision-making processes,
improve data management, and uphold ethical standards. Findings suggest that while AI can
transform audit processes, effective adoption requires addressing infrastructure, skill development, and regulatory compliance. This article contributes to the field by offering a structured approach to AI adoption in auditing, considering the unique socio-economic context of Nigeria.