International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

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Innovative Cloud Architectures: Revolutionizing Enterprise Operations Through AI Integration

Author(s) Babita Kumari
Country United States
Abstract This article examines the transformative impact of artificial intelligence (AI) and machine learning (ML) integration in cloud computing architectures across enterprise operations. Through an analysis of 250+ enterprise deployments, we demonstrate significant improvements including a 42% reduction in
operational costs and 53% enhancement in process efficiency. The article presents a comprehensive framework for implementing AI-enhanced cloud architectures, combining multi-cloud and hybrid approaches across major platforms including Microsoft Azure, AWS, and Google Cloud. Our findings
highlight key advancements in predictive resource management (91% accuracy), automated scaling (67% reduction in overprovisioning), and intelligent security systems (99.7% threat detection accuracy). The article provides detailed implementation guidelines, security protocols, and best practices, supported by a case study of a Fortune 500 manufacturing corporation that achieved $4.2 million in annual maintenance cost savings and 52% improvement in resource allocation efficiency. The article also explores future developments in quantum computing integration and multi-cloud orchestration, establishing a roadmap
for organizations seeking to leverage AI-enhanced cloud architectures for competitive advantage.
Keywords Keywords: Artificial Intelligence; Cloud Computing; Enterprise Architecture; Machine Learning; Multi-Cloud Integration
Field Computer
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-11-10
Cite This Innovative Cloud Architectures: Revolutionizing Enterprise Operations Through AI Integration - Babita Kumari - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.30201
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.30201
Short DOI https://doi.org/g8qtkh
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