Artificial intelligence adoption and audit quality in an emerging country: Does auditor experience matter?

This study investigates the effect of Artificial Intelligence Adoption (AIA) on Audit Quality (AQ) in an emerging market, examining whether Auditor Experience (AE) moderates this relationship. Using data from survey completed by 214 external auditors in Jordan and this study apply partial least squares structural equation modeling (PLS-SEM). The findings show that AIA positively influences AQ, AE has a strong direct effect, and the interaction between AE and AIA is significant and negative, indicating that experienced auditors relied more heavily on professional judgment and established routines, which reduced incremental gains derived from AI tools. In contrast, less experienced auditors appeared to benefit more from AI support, as these tools compensated for limited experiential knowledge and enhanced decision-making confidence. The model explains 86.5 percent of the variance in AQ, demonstrating substantial explanatory power. The results suggest that audit firms should implement AI tools in ways that complement auditors’ expertise, and regulators should provide guidance that promotes technological adoption without undermining professional judgment. This study contributes to the auditing literature by highlighting the interplay between AI adoption and auditor experience in enhancing audit quality, providing insights particularly relevant for emerging markets.