Artificial intelligence (AI) is reshaping decision-making across sectors, yet a persistent and well-documented gap separates the normative principles that guide its development from the organizational and governance practices through which those principles must be enacted. This chapter examines how core responsible AI principles—fairness, accountability, transparency, autonomy, and privacy—can be translated into organizational and institutional practices that promote social equity and sustainable development. The chapter proceeds in four stages. It opens by mapping the foundational principles underpinning responsible AI, critically assessing major institutional frameworks from the EU, OECD, UNESCO, and IEEE, and identifying the structural tensions among principles that any operationalization must navigate. The second stage examines who bears accountability for AI systems, mapping responsibility across developers, deploying organizations, consumers, and regulators, and arguing that effective governance requires control-accountability alignment rather than diffuse normative commitment. The third stage addresses why existing governance instruments so consistently fail to produce behavioral change, identifying three structural failure modes: performative responsibility, accountability diffusion, and the translation gap between principles and practice, and examining the organizational archetypes through which firms approach implementation. The chapter concludes with a forward-looking analysis of the barriers to scaling responsible AI from isolated practice to systemic institutional change, connecting the governance agenda to Sustainable Development Goals and ESG frameworks. The chapter offers a structured reference for academics, practitioners, and policymakers seeking to embed responsible AI principles into organizational and governance architectures that are durable, enforceable, and capable of adaptation as AI capabilities continue to evolve.