AI-Enabled ERP Systems: A Cross-Functional Systematic Literature Review

This study conducts a systematic literature review following the PRISMA framework to synthesize fragmented research on Artificial Intelligence (AI) enabled in Enterprise Resource Planning (ERP) systems, screening Scopus-indexed studies published between 2020 and 2026 to select 46 papers for thematic analysis. The results indicate that AI integration profoundly enhances decision-making, operational efficiency, and process automation, while achieving notable cost reductions and processing-time minimization. However, successful adoption is heavily constrained by critical challenges, including data quality and availability, complex legacy system integration, security, privacy, regulatory compliance, and a lack of user competence. Crucially, the review reveals a distinct imbalance in technological distribution across enterprise modules. Supply Chain Management and Finance and Accounting heavily dominate the existing literature, comprehensively utilizing and integrating advanced methodologies such as Machine Learning, Deep Learning, Natural Language Processing, Intelligent Automation, and Generative AI or Large Language Models. In stark contrast, operational functional areas like Production Planning remain significantly underrepresented and technologically limited. Ultimately, this structural synthesis provides a comprehensive understanding of AI-enabled ERP adoption, highlighting that its profound organizational implications and strategic value depend heavily on existing data readiness and maturity.