<![CDATA[Rapid enterprise adoption of artificial intelligence within SAP Business Technology Platform environments has introduced critical challenges associated with governance transparency, ethical automation, regulatory compliance, and operational accountability. Existing enterprise governance models often lack integrated mechanisms capable of addressing explainability, adaptive policy enforcement, and intelligent risk monitoring across complex AI driven ecosystems. This study proposes a comprehensive Responsible AI Governance framework for SAP BTP deployments designed to strengthen trusted enterprise transformation through explainable systems, adaptive compliance controls, and secure automation engineering. The research applies a mixed methodological approach that combines qualitative analysis of enterprise governance practices with quantitative evaluation of governance effectiveness across AI lifecycle management processes. The framework integrates explainable intelligence models, automated compliance orchestration, policy driven governance workflows, and continuous risk assessment mechanisms to improve transparency, operational resilience, and decision accountability within enterprise platforms. Findings demonstrate that the proposed architecture significantly enhances governance efficiency, reduces compliance complexity, improves trust in AI enabled business operations, and supports scalable enterprise innovation without compromising security or ethical standards. The study contributes strategically by establishing a governance blueprint that aligns enterprise AI adoption with organizational accountability and regulatory readiness, while academically extending existing research in responsible AI governance and intelligent enterprise systems. The proposed framework provides a scalable foundation for organizations seeking to operationalize trustworthy AI capabilities within modern SAP driven digital transformation environments.]]>
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