Data Governance and Auditor Readiness for Generative AI-Based Audits: A Survey Study of Indonesian External Auditors with AI Literacy as a Mediator

Abstract

The integration of generative artificial intelligence into financial statement auditing promises enhanced efficiency and analytical depth. This study addresses this gap by investigating the relationship between data governance and auditor readiness for generative AI-based audits, with AI literacy as a mediating variable. Using a quantitative survey design, data were collected from 248 external auditors across public accounting firms in Indonesia, including Big Four affiliates, large national firms, and small and medium practices. The research model, grounded in the Technology-Organization-Environment framework and knowledge-based theory, was tested through Partial Least Squares Structural Equation Modeling. Data governance exerted a significant positive effect on both auditor readiness (β = 0.47, p < 0.001) and AI literacy (β = 0.67, p < 0.001). AI literacy partially mediated this relationship (indirect effect = 0.26, p < 0.001; VAF = 35.6%), indicating that robust data governance enhances readiness both directly and indirectly by elevating auditors' AI comprehension. The model explained 61.7% of the variance in auditor readiness. Among data governance dimensions, data quality demonstrated the highest relative importance, while data architecture showed lower contribution. These findings extend the TOE framework by specifying data governance maturity as a critical organizational context factor for generative AI adoption in auditing, and they provide practical guidance for audit firm leaders, regulators, and professional bodies in emerging economies. Key recommendations include institutionalizing data quality standards, establishing clear stewardship roles, and integrating AI literacy development with data governance training.