Revolutionizing audit practice in emerging economies: the role of Artificial Intelligence in enhancing the audit of consolidated financial statements

Artificial Intelligence (AI) adoption is reshaping audit practices globally, yet limited empirical evidence exists on its influence within fragile and turbulent markets such as Lebanon. This study investigates how AI affects the efficiency and effectiveness of auditing consolidated financial statements and extends prior models by examining the direct relationship between audit efficiency and effectiveness, as well as the moderating role of AI in this relationship. Using a cross-sectional survey of 356 auditors and structural equation modeling, the findings reveal that AI usage significantly enhances both efficiency and effectiveness in consolidated audits, and that efficiency itself is a strong predictor of effectiveness. However, the results show that AI does not moderate the efficiency–effectiveness relationship, suggesting that while AI improves both dimensions independently, it does not strengthen the synergy between them. Grounded in Agency Theory, the Theory of Inspired Confidence, and the Technology Acceptance Model, this study demonstrates AI’s potential as a transformative driver of audit innovation in emerging and turbulent markets. The study provides actionable insights for audit firms and regulators seeking to modernize audit processes under economic and institutional instability and offers contributions to theory by extending technology-adoption frameworks to the complex domain of consolidated financial statements in an emerging economy.