Enhancing Enterprise Intelligence with Generative AI Supported by Cloud Native Computing and Public Health Leadership
DOI:
https://doi.org/10.15662/IJEETR.2023.0506029Keywords:
Generative AI, Cloud-Native Computing, Public Health Leadership, Enterprise Intelligence, Scalable Architecture, Healthcare InformaticsAbstract
This paper explores the convergence of Generative Artificial Intelligence (GenAI), cloud-native computing, and public health leadership to enhance enterprise intelligence within healthcare ecosystems. As modern healthcare demands rapid, data-driven decision-making, traditional monolithic data infrastructures fall short. By leveraging cloud-native architectures—characterized by microservices, containerization, and dynamic orchestration—enterprises can scalably deploy large language models (LLMs) and retrieval-augmented generation (RAG) frameworks. This integration empowers public health leaders with real-time, actionable insights, shifting institutional paradigms from reactive management to proactive, predictive governance. The paper outlines how this technological synergy optimizes epidemiological surveillance, resource allocation, and policy formulation while mitigating systemic biases. Furthermore, we examine the critical role of adaptive public health leadership in navigating ethical guardrails, data privacy compliance, and socio-technical challenges. Through a robust methodological framework detailing architectural pipelines and deployment strategies, this study demonstrates that cloud-native GenAI architectures significantly elevate enterprise intelligence. Ultimately, we provide a strategic roadmap for healthcare organizations to build resilient, scalable, and intelligent ecosystems capable of addressing complex global health challenges.
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