Beyond Retrospective Reporting: Artificial Intelligence and the Potential Emergence of Predictive Accounting for Sustainable Competitive Advantage

Digital transformation and recent advances in artificial intelligence are changing how organizations generate, process, and use accounting information. In increasingly data-driven business environments, traditional retrospective reporting is often insufficient for supporting fast and complex managerial decisions. As a result, accounting is gradually expanding beyond its conventional reporting role toward more anticipatory and decision-oriented functions. This study examines predictive accounting as a possible direction in the evolution of the accounting function, emerging from the interaction between digital infrastructure, AI-based analytics, and governance mechanisms. The research adopts a qualitative conceptual–applied approach that combines a focused literature review with an illustrative mini-case study on cash-flow forecasting. The purpose is not to empirically validate predictive models, but to illustrate how predictive analytics may support early identification of financial deviations and improve managerial responsiveness.

The findings suggest that the transition toward predictive accounting depends not only on technological capabilities, but also on data quality, organizational maturity, and the existence of clear governance and control mechanisms. Based on the analysis, the paper develops an integrative conceptual framework of accounting remodeling and proposes a predictive accounting maturity model that reflects the progression from basic digitalization to strategically integrated prediction. The study contributes to the literature by connecting discussions on digital transformation, artificial intelligence, and managerial control within the accounting context. At the same time, it provides a practical maturity-oriented perspective that organizations may use to assess their readiness for predictive accounting integration. However, the results also indicate that predictive outputs remain strongly dependent on the reliability of historical accounting information, since weaknesses in past data may directly affect the quality of future-oriented estimates.