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Operationalizing AI Governance: A Practical Roadmap and Case Study for Organizations Adopting Third-Party AI Systems
Abstract
This chapter presents the second phase of a multi-stage research agenda on operationalizing AI governance for organizations adopting third-party AI systems. Building on our conceptual AI Governance Implementation Framework (AGIF), this chapter translates theory into practice by developing the foundational oversight mechanisms that anchor an AI governance program through a practice-inspired, action design research based, collaborative inquiry. The resulting Roadmap for AI Governance (RAIG) outlines an eight-step implementation model, establishing the structures necessary for later operationalization. The roadmap provides practical guidance for establishing oversight structures, scoping AI systems, defining risks and controls, and integrating evaluation, testing, and validation processes. Findings reveal that effective AI governance requires iterative development, cross-functional collaboration, and integration with existing risk and data governance programs. This work demonstrates how governance can be embedded in real-world organizational settings, bridging the gap between conceptual frameworks and applied practice for responsible AI adoption.
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