Tax administrations are undergoing rapid digitalisation, while sustainability requirements are increasingly embedded in corporate governance frameworks. These parallel transformations are raising new expectations for transfer pricing (TP) documentation, which must be accurate, transparent, and audit-ready. This paper investigates the extent to which artificial intelligence (AI)—specifically natural language processing (NLP), robotic process automation (RPA), and machine-learning techniques—can support a sustainability-oriented governance framework for TP documentation in multinational enterprises. Using a longitudinal case study of the OMEGA Group, operating across 21 jurisdictions, we analyse an AI-enabled documentation architecture that streamlines data extraction, enhances comparability analysis, and strengthens audit preparedness, in line with the OECD Transfer Pricing Guidelines and relevant European Union regulatory requirements. The empirical evidence indicates substantial improvements in documentation efficiency (−68.3%), a significant reduction in processing errors (−81.5%), and higher audit acceptance rates (+27%). Beyond compliance, AI-driven digital workflows contribute to sustainability objectives by reducing resource consumption, improving data traceability, and facilitating alignment with CSRD-related reporting requirements. Overall, the findings demonstrate that AI-enabled TP documentation can evolve into a strategic pillar of sustainable tax governance, provided that its outputs remain explainable, auditable, and grounded in professional judgment. The study proposes an integrated governance framework that connects digital transformation, regulatory compliance, and sustainability within contemporary TP management practices.
Boiţă et al. (Thu,) studied this question.