Enterprise AI services increasingly support customer interactions, operations, planning, analytics, and autonomous processes. Disruption of models, providers, vector stores, data platforms, GPUs, APIs, identity services, or agent tools can therefore create significant business impact. Traditional disaster recovery plans may not address AI-specific dependencies such as model endpoints, prompt repositories, retrieval indexes, evaluation baselines, policy services, and external foundation-model providers. This paper proposes an Enterprise AI Disaster Recovery and Operational Resilience Architecture that defines recovery objectives, dependency mapping, multi-region design, provider fallback, model portability, data recovery, configuration backup, degraded operation, manual fallback, resilience testing, and recovery governance. The architecture supports predictive AI, Generative AI, RAG, and agentic systems. The proposed approach enables enterprises to restore AI-enabled services safely while maintaining data integrity, security, governance, and business continuity.

Sanjeeve Kumar Gajadi, "Enterprise AI Disaster Recovery and Operational Resilience Architecture" International Journal of Scientific Research and Engineering Development, V9(6): Page(150-151) November - December 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.