From tech-driven to principle-driven supply chains: responsible AI governance as a dynamic capability

Artificial intelligence (AI) is increasingly embedded in supply-chain forecasting, sourcing, planning, and logistics, yet governance arrangements required to ensure responsible use remain fragmented. This article develops a Responsible AI Governance (RAIG) framework that explains how ethical principles can be translated into operational supply-chain capabilities. Drawing on an integrative synthesis of AI-enabled supply-chain management, responsible AI, dynamic capabilities, relational governance, and socio-technical systems research, the framework connects three principles – transparency and traceability, validity and robustness, and role separation – to four mechanisms: model cards and explainability reports, data and model audits, responsible-sourcing clauses, and human-in-the-loop sales and operations planning reviews. These mechanisms are embedded in procurement, planning, scheduling, and logistics routines and are theorised to support agility, resilience, decarbonisation, and ethically legitimate decision-making. Six propositions specify the framework’s causal logic, while a 90-day implementation playbook translates it into managerial action. The article also develops frugal adaptations for emerging and humanitarian supply chains. Because the field is nascent and access to governance artefacts and data is limited, the study proposes a progressive validation agenda using simulations, vignettes, comparative cases, and micro-pilots. RAIG is positioned as a dynamic capability that turns responsibility from a compliance obligation into an adaptive source of performance.