The Impact of Artificial Intelligence on Accounting Practices and Business Innovation
Keywords:
Artificial Intelligence, Accounting Practices, Business Innovation, Automation, Data AnalyticsAbstract
The rapid advancement of Artificial Intelligence (AI) has significantly transformed accounting practices and driven business innovation across industries. AI technologies such as machine learning, natural language processing, and robotic process automation have enhanced the accuracy, efficiency, and reliability of financial reporting and auditing processes. In accounting, AI reduces human error, automates repetitive tasks, and provides predictive insights that support strategic decision-making. Beyond improving traditional accounting functions, AI fosters innovation in business models by enabling real-time data analytics, advanced risk management, fraud detection, and customer-oriented financial solutions. Organizations adopting AI-integrated accounting systems are better equipped to respond quickly to dynamic market changes, strengthen corporate governance, and enhance overall competitiveness. Moreover, the integration of AI contributes to more agile decision-making processes, enabling firms to identify opportunities, anticipate risks, and allocate resources more strategically. However, these opportunities are accompanied by notable challenges, including ethical concerns, data security risks, implementation costs, and the urgent need to upskill accounting professionals. Without adequate training and governance, organizations risk creating dependency on automated systems or facing biases embedded in algorithms. This study therefore explores the dual role of AI in reshaping accounting practices and promoting business innovation, highlighting both the transformative benefits and the potential limitations. By reviewing recent literature and practical applications, the article underscores how AI can be leveraged not only to optimize internal accounting operations but also to drive long-term value creation, sustainability, and innovation at the organizational level. The findings suggest that successful integration of AI requires a balanced approach that combines technological advancement with ethical responsibility, regulatory compliance, and human expertise. Ultimately, the adoption of AI in accounting and business innovation represents a paradigm shift, paving the way for more adaptive, data-driven, and sustainable organizational growth.
References
Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W. W. Norton & Company.
Cockburn, I. M., Henderson, R., & Stern, S. (2018). The impact of artificial intelligence on innovation (NBER Working Paper No. 24449). National Bureau of Economic Research. https://doi.org/10.3386/w24449
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE Publications.
Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108–116.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Guthrie, C., & Murthy, V. (2019). Artificial intelligence and accounting: Past, present and future. Accounting, Auditing & Accountability Journal, 32(4), 869–876. https://doi.org/10.1108/AAAJ-01-2019-3440
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.
Issa, H., Sun, T., & Vasarhelyi, M. A. (2016). Research ideas for artificial intelligence in auditing: The formalization of audit and workforce supplementation. Journal of Emerging Technologies in Accounting, 13(2), 1–20. https://doi.org/10.2308/jeta-10511
Jensen, M. C., & Meckling, W. H. (1976). Theory of the firm: Managerial behavior, agency costs and ownership structure. Journal of Financial Economics, 3(4), 305–360. https://doi.org/10.1016/0304-405X(76)90026-X
Kline, R. B. (2016). Principles and practice of structural equation modeling (4th ed.). Guilford Press.
Lombardi, R., Secundo, G., & Dumay, J. (2021). Exploring the role of artificial intelligence in intellectual capital research and management. Journal of Intellectual Capital, 22(1), 2–9. https://doi.org/10.1108/JIC-02-2020-0055
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
Richins, G., Stapleton, A., Stratopoulos, T., & Wong, C. (2017). Big data analytics: Opportunity or threat for the accounting profession? Journal of Information Systems, 31(3), 63–79. https://doi.org/10.2308/isys-51799
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.

