When Machines Create Value: Rethinking Transfer Pricing for AI-Driven Economies

Artificial Intelligence (AI) is transforming how multinational enterprises (MNEs) generate, manage, and capture value—posing unprecedented challenges for international transfer pricing frameworks rooted in traditional human-performed functions and tangible asset ownership. This paper explores the disruptive implications of AI-driven business models for the arm’s length principle, focusing on decentralized decision-making, data-based intangibles, and algorithmic value creation. By analyzing use cases across industries such as financial services, pharmaceuticals, and e-commerce, the study highlights core challenges including fragmented DEMPE functions, ambiguous data ownership, and comparability gaps in AI-to-AI intercompany transactions. The paper integrates extensive OECD Transfer Pricing Guidelines (2022) references, offering practical pathways for aligning AI transactions with the arm’s length standard through revised DEMPE analyses, hybrid profitsplit methods, and enhanced documentation of control over risk. In doing so, it proposes a conceptual framework for recognizing economic ownership of AI-generated intangibles and advocates for OECD policy evolution to address emerging forms of digital value creation. The findings underscore the need for both tax authorities and MNEs to reimagine transfer pricing compliance in an era defined by autonomous systems, synthetic outputs, and algorithmic control.