Artificial Intelligence Adoption as a Mediator of AI Implementation and Financial Accounting Performance

Abstract

Purpose: This study examines how the implementation of Artificial Intelligence (AI) in financial accounting systems affects operational efficiency, data security, and workforce skills, with AI adoption as a mediating variable. Design/Methodology/Approach: This study uses a quantitative survey design involving 207 valid respondents from accounting and information technology professionals in Indonesian organizations that use AI-based financial accounting systems. Data were collected through a third-party survey center after a pilot test with 30 respondents. The data were analyzed using Partial Least Squares Structural Equation Modeling with bootstrapping. Findings: The results show that AI implementation has positive direct effects on operational efficiency, data security, and workforce skills. AI adoption significantly mediates the effects of AI implementation on operational efficiency and data security, but does not significantly mediate the effect on workforce skills. Research Implications: The findings show that AI adoption strengthens process and security outcomes, while workforce transformation requires structured training, practical experience, and job redesign. Future research should examine training quality, leadership support, learning culture, and technology readiness. The study contributes to accounting information systems literature by explaining AI adoption as a mediating mechanism, while providing practical insights for organizations implementing AI-based accounting systems.