AIREA: An AI-Driven Optimization Framework for Intelligent Automation in Large-Scale Enterprise Systems
Year: 2025
Type: article
Abstract: Cloud computing has become the foundation of modern enterprise infrastructure, enabling unparalleled scalability, flexibility, and cost efficiency. However, as cloud adoption accelerates, enterprises face critical challenges in sustainability, governance, and performance optimization. Existing cloud architectures fail to balance energy efficiency, compliance with global ESG mandates, and workload optimization in multi-cloud environments. This paper introduces a novel strategic framework for ESGcompliant cloud computing, integrating AI-driven energy optimization, decentralized governance models, and predictive cloud workload distribution to achieve unprecedented efficiency and regulatory alignment. Unlike traditional studies that focus exclusively on sustainability goals or performance trade-offs, this research pioneers a multi-dimensional approach, demonstrating how enterprises can achieve carbon footprint reduction, workload efficiency, and governance compliance simultaneously. Through an extensive comparative analysis of cloud providers (AWS, Azure, GCP), this paper identifies critical inefficiencies in current ESG cloud implementations and proposes a scalable, AI-driven framework for intelligent cloud resource allocation. By presenting real-world case studies and introducing a novel architectural model, this research sets a new benchmark for sustainable and performance-driven cloud computing. Additionally, we outline future directions in AI-powered cloud governance, blockchain-based ESG compliance, and next-generation cloud sustainability metrics, paving the way for a responsible, highperformance digital ecosystem.
Authors: Ashutosh Ahuja, Anant Wairagade, Nikhil Gupta
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4407994760