A Context-Aware Framework for Modeling ERP Business Process Adaptation Under National Legal Requirements

Abstract

Traditional enterprise resource planning (ERP) systems struggle to adapt to rapidly evolving legal environments because of their static architectures and dependence on manual updates, particularly in developing economies. This study addresses this limitation by proposing a context-aware, artificial intelligence (AI)-driven framework that enables ERP systems to interpret and adapt to evolving legal requirements. The framework integrates ontology-based reasoning, natural language processing (NLP), and adaptive learning to transform legislative changes into machine-interpretable rules and executable process updates, supported by human validation to ensure accuracy and accountability. Designed using a design-oriented research approach, the framework establishes a structured architecture that supports continuous, traceable, and adaptive compliance. The findings demonstrate the framework’s potential to enhance regulatory alignment, improve transparency, and reduce dependence on manual system updates. The study contributes to the development of intelligent and explainable ERP systems capable of sustaining real-time compliance in dynamic regulatory environments.