The adoption of artificial intelligence in internal audit: determinants, uses and challenges – a systematic review of the literature based on Prisma

Abstract

The integration of artificial intelligence into internal audit is progressing rapidly, driven by developments in machine learning, predictive analytics, natural language processing and, more recently, generative artificial intelligence. This study aims to systematically analyse the determinants of AI adoption in internal audit; its main uses and the challenges associated with its integration. A systematic literature review was conducted in accordance with the PRISMA 2020 guidelines, using the Scopus and Web of Science databases, covering the period 2015–2026. Following identification, deduplication, screening and eligibility assessment, 35 studies were selected for analysis. The results show that the adoption of AI depends primarily on technological maturity, data quality, auditors’ skills, organisational support, governance and trust in the systems. Its main applications relate to risk assessment, anomaly detection, task automation, continuous auditing, document analysis and decision support. The main challenges relate to governance, explainability, reliability, skills, human oversight and data protection. The review highlights the multidimensional nature of AI adoption and emphasises the importance of aligning technologies, human capabilities and governance mechanisms to ensure controlled integration within the internal audit function. Keywords: Artificial intelligence; Internal audit; Technology adoption; Generative artificial intelligence; Systematic review.     Received Date: August 17, 2026 Accepted Date: September 09, 2026 Published Date: October 01, 2026 Available Online at: https://www.ijsrisjournal.com/index.php/ojsfiles/article/view/975