The AI Implementation Gap: Policy–Audit Misalignment in the UAE and Egypt

Abstract

Following the International Auditing and Assurance Standards Board’s (IAASB) findings, artificial intelligence (AI) developments over human governance in the February 2026 Technology Quality Management roundtables outcome statement, this study aims to disclose the disconnect between the policy and practice of external audit functions in AI adoption. The research employs a qualitative multi-method design that compares the narratives of Big Four organizations’ transparency reports and audit practitioners in the United Arab Emirates (UAE) and Egypt. The study’s context was determined by the identified gaps in prior empirical research on the differences between the attitudes of corporate management and auditors towards AI usage in external audits. The lead research question focuses on the distinctions in Big Four strategies and individual auditors’ practices in AI applications. The findings are based on content analysis of the narrative of Big Four organizations’ 2021–2025 transparency reports and thematic analysis (TA) of the semi-structured interviews with the auditors in the UAE and Egypt in 2026. The study discovers that auditor practices are currently disconnected from the strategic level propositions. While corporate reports depict a vision in which AI is regarded as a normal component of audit processes, auditors’ experiences suggest it is limited and token adoption in practice. The authors conclude that governance and transparency issues, regional disparities in implementation, and algorithmic complexity contribute to the difficulties in auditor practices in adopting AI tools. These challenges drive the need to establish policy-practice configurations for the effective implementation of audit technologies based on AI.

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