Artificial Intelligence-Augmented ERP Systems: A Systematic Review of Predictive Analytics Integration for Real-Time Financial Decision-Making

Abstract

Artificial Intelligence (AI), together with emerging digital technologies, is increasingly transforming Enterprise Resource Planning (ERP) systems by reshaping organizational operations, decision-making processes, and strategic planning. The rapid adoption of AI within enterprise information systems has generated substantial scholarly interest in AI-enabled ERP environments. Nevertheless, a more comprehensive and systematic understanding of this evolving research domain remains necessary. This study conducts a systematic literature review of academic and grey literature, following the PRISMA 2020 framework, to examine the integration of three major categories of AI technologies — generative AI, analytics AI, and automation AI — within business organizations, with particular emphasis on predictive analytics for real-time financial decision-making. The review traces the development of AI–ERP research, identifies emerging trends, and highlights important research gaps and future directions. Existing bibliometric evidence provides important context for this synthesis: analyzing 183 academic publications and 35 industry documents, Hurbean et al. (2026) report an annual growth rate of approximately 11.3% in AI–ERP research, with automation-related studies accounting for roughly 41% of the literature, positive impacts reported in approximately 93% of reviewed studies, and ethical or governance-related issues addressed in fewer than 2%. The present review extends this evidence base through a thematic rather than bibliometric synthesis; record counts for the database search underpinning this review are reported in the Methodology section rather than in this abstract. Overall, the findings identify automation, predictive analytics, and generative AI as the principal research themes, while trust, governance, explainability, and long-term strategic implications remain insufficiently explored. The evidence suggests that AI is fundamentally extending conventional ERP systems by introducing greater autonomy, adaptability, predictive intelligence, and intelligent process capabilities into integrated enterprise environments. These developments have important implications for enterprise information-systems research and practice, particularly by demonstrating how AI-enabled ERP systems can strengthen organizational processes, facilitate timely and data-driven decision-making, and improve operational and strategic performance.