Artificial Intelligence in ERP Quality Management Modules: A Comparative Analysis of SAP and Infor - Heric, Staš; Bobek, Samo; Sternad Zabukovšek, Simona (Naše gospodarstvo/Our economy, 2025)
Enterprise Resource Planning (ERP) systems are widely recognised as strategic enablers of digital transformation, integrating core business functions and supporting operational efficiency. Quality Management (QM) is a pivotal component of their curriculum, critical in ensuring compliance, sustaining competitiveness, and driving continuous improvement. Recent advancements in artificial intelligence (AI) have further augmented the potential of ERP systems by facilitating predictive analytics, anomaly detection, and intelligent process automation. However, the integration of AI into ERP-based QM has not yet been systematically examined in academic research. The present article addresses this gap by comparing two leading ERP vendors, SAP and Infor, focusing on their AI-enabled QM modules. The study utilises academic literature, industry reports, and vendor documentation to demonstrate that SAP emphasises horizontal breadth and positions its QM functionalities as part of a global digital core. Concurrently, Infor prioritises vertical specialisation, customising its QM functionalities to meet the distinct needs of specific industries and augmenting them with advancements in AI and generative AI. The findings contribute to theory and practice by demonstrating how ERP vendors differentiate their approaches to AI integration in QM. The present study contributes to the extant literature on ERP and Total Quality Management (TQM) by extending the current body of knowledge in this area. It highlights the strategic role of AI in shaping ERP value creation. It provides managerial guidance for organisations evaluating ERP systems in the context of quality assurance, sustainability, and digital transformation.