Algorithmic Governance and Audit Efficiency: The Role of AI Adoption and IT Governance in Jordanian Commercial Banks

Abstract AI is rapidly developing in auditing practices worldwide, but there is little empirical evidence on the link between AI and audit efficiency among emerging market banks. In addition, the organizational variables through which AI improves audit efficiency— for example, IT governance maturity— are not theoretically and empirically theoretically sound and empirically untested. This study explores the effect of AI adoption on audit delay in Jordanian banks and the altering role of IT governance maturity. The data for 2015–2024 are summarized in a balanced panel of 15 commercial and Islamic banks listed on the ASE, indicating 150 bank years. The normalized score of AI adoption is obtained through bilingual content analysis of annual reports, and a CoBIT-aligned composite index for IT governance maturity is measured by a standardized score of the bilingual content analysis of the annual reports. Four model specifications employ pooled OLS regression with Driscoll–Kraay standard errors robust to heteroskedasticity, autocorrelation, and cross-sectional dependence. This suggests that AI adoption significantly reduces audit delay (= 26.07, p = 0.01); an increased AI score = 4.06 fewer audit days, which translates into a 6.5% efficiency improvement; and an increase in one standard deviation in the AI score yields 4.06 fewer audit days, or a 6.5% improvement in accuracy. I conclude that the interaction between AI adoption and IT governance maturity is negative and significant: 3 = 6.22, p = 0.011, and banks with larger IT governance maturity significantly increase the efficiency of audit performance. The results are reliable when the year fixed effects are replaced with a COVID-19 control variable. The present study contributes the first archival evidence from emerging-market banks in the MENA region on the AI–audit efficiency nexus, incorporates Agency Theory, TOE and Institutional Theory, and emphasizes IT governance as a critical barrier to achieving an efficient use of AI in auditing. JEL Classification: M41, M42, G21, G34, O33