AI Adoption in Accounting and Finance: Impact on Organizational Performance
Abstract
The rapid integration of artificial intelligence (AI) into accounting and finance functions is reshaping how organizations manage information, processes, and strategic decisions; however, empirical evidence on its performance implications remains limited, particularly in emerging economies. This study investigates the effect of AI adoption on organizational performance by examining financial, operational, and strategic outcomes. A quantitative research approach was employed using survey data collected from 370 accounting and finance professionals working in banking, manufacturing, information technology, and service organizations. The data were analyzed using descriptive statistics, correlation analysis, and regression models to assess the relationship between AI adoption and performance dimensions. The results indicate a high level of AI adoption and organizational preparedness, with AI adoption exerting a significant and positive influence on all three performance dimensions. Financial performance demonstrates the strongest association, followed by operational efficiency and strategic effectiveness, suggesting that AI enhances cost control, process optimization, and data-driven decision-making. While organizations continue to face challenges related to skill development, system integration, and change management, these constraints do not outweigh the overall performance benefits. The study concludes that AI adoption in accounting and finance functions serves as a strategic enabler that strengthens organizational performance and supports the transition of professionals toward higher-value analytical and advisory roles.
Fields of Study & Subjects
Big Data and Business Intelligence
Impact of AI and Big Data on Business and Society
Financial Reporting and XBRL
Publication details
- Journal / source
- Asian Journal of Economics Business and Accounting
- Publisher
- Sciencedomain International
- Volume
- 26
- Issue
- 1
- Pages
- 421–428
- Publication date
- 23 January 2026
- Language
- EN