Artificial Intelligence Utilization, Auditors’ Technological Competence, and Audit Quality: PLS‑SEM Evidence from Indonesia

Abstract

Audit firms and internal audit units in Indonesia have begun to use artificial intelligence (AI), but it is not yet clear whether audit quality improves because of the tool or because of the auditor who uses it. We examined the effects of AI utilization and auditors’ technological competence on audit quality. Questionnaire data were collected from 80 internal and external auditors selected by purposive sampling and analyzed with partial least squares structural equation modeling (PLS-SEM) in SmartPLS 4. The indicators met the convergent validity and reliability criteria, but the Fornell–Larcker test showed weak discriminant validity between the two predictors. Auditors’ technological competence had a positive and significant effect on audit quality (β = 0.593, t = 3.371, p = 0.001, f2 = 0.261), whereas AI utilization had a positive but non-significant effect (β = 0.349, t = 1.828, p = 0.068, f2 = 0.090). The two constructs explained 86.5% of the variance in audit quality. These results suggest that the benefit of AI for audit quality depends largely on the competence of the auditors who run it and read its output. We recommend that audit organizations plan AI investment together with competence development, and that future studies improve the measurement of both constructs and test competence as a mediator or moderator.