Applying AI Ethics to ERP Software: A Critical Case Study and Comparative Analysis of an Industry Framework

Enterprise Resource Planning (ERP) systems have become mission-critical infrastructures, integrating finance, human resources, supply chains, manufacturing, and customer management. The increasing integration of artificial intelligence (AI) into these platforms promises efficiency and strategic value, yet it also raises acute ethical and governance challenges: bias in recruitment, opaque decision logic in resource allocation, privacy and data protection risks, accountability gaps, and environmental costs from computational intensity. Although governments and scholars have proposed principles and regulatory frameworks - such as Floridi and Cowls' unified five principles, the U.S. NIST AI Risk Management Framework (RMF), and the European Union's AI Act - there remains limited scrutiny of industry-developed frameworks that directly guide enterprise practice. This paper addresses that gap through a critical case study of the SAP AI Ethics framework, which articulates nine principles, a risk assessment process, and a five phase lifecycle for AI within ERP systems. We evaluate its provisions against the EAGLE ERP governance framework, the NIST AI RMF, and Floridi and Cowls' principled framework, identifying areas of alignment, divergence, and under specification. We further situate the framework within ongoing regulatory debates, engaging critiques of the EU AI Act concerning risk standardization, categorization, reliance on private and challenges of enforcement. Our contributions are fourfold: (1) a transparent document analysis of SAP's ethics framework; (2) a comparative mapping to academic and policy frameworks; (3) a critical assessment of governance, accountability, and ethical trade-offs in ERP deployments; and (4) evidence-informed recommendations - including independent oversight, risk registers, transparency artifacts, participatory design, and sustainability metrics - illustrated through ERP-specific vignettes in human capital, procurement, finance, and planning. The paper demonstrates how enterprises can translate high-level AI ethics into operational governance, offering actionable guidance for both scholars and practitioners.