AI-enhanced ERP systems: a systematic review and bibliometric analysis of intelligent enterprise analytics

Artificial intelligence (AI) is reshaping Enterprise Resource Planning (ERP) systems by extending them from transactional backbones into intelligent, data-centric platforms for automation, prediction, process monitoring, and decision support. Yet the AI-ERP literature remains fragmented across technical, organizational, and analytical streams, and existing studies rarely distinguish AI embedded within ERP workflows from external analytics layers built around ERP data. This study synthesizes 80 Scopus-indexed publications from 2014 to 2024 through a PRISMA-guided systematic literature review supported by bibliometric performance analysis and VOSviewer science mapping using predefined occurrence thresholds. Using a structured coding protocol, the review identifies five thematic domains: AI and machine learning in ERP, ERP-integrated decision support and business analytics, ERP-enabled business-process optimization, predictive and prescriptive analytics, and business intelligence within ERP platforms. Bibliometric evidence indicates that “enterprise resource planning” is the most central keyword in the network, with the highest occurrence (13) and total link strength (42), followed by “data analytics” and “information management,” each with six occurrences and a total link strength of 23. The document-type distribution shows a near balance between conference papers (47.5%) and journal articles (42.5%), suggesting a field that is expanding rapidly while still developing through both technical and journal-based outlets. Country-level mapping further indicates limited international collaboration and influence concentrated around a small set of contributors. The synthesis shows that AI-enhanced ERP research is strongest in forecasting, analytics, process automation, and decision support, but weaker in deployment-level validation, SME-specific architectures, explainability, and scalable integration with legacy ERP systems. The study contributes a consolidated evidence map and research agenda for advancing AI-enhanced ERP from isolated models and prototypes toward explainable, interoperable, and organizationally deployable enterprise analytics ecosystems.