Authors : Anish Naidu Basa

Volume/Issue : Volume 10 - 2025, Issue 4 - April

Google Scholar : https://tinyurl.com/jwdevsre

Scribd : https://tinyurl.com/54ez2cfe

DOI : https://doi.org/10.38124/ijisrt/25apr922

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Abstract : Artificial intelligence (AI) is transforming cloud analytics and real-time business intelligence (BI), but its rapid evolution has introduced new challenges in scalability, operational efficiency, environmental sustainability, and domain- specific adaptability. As AI models become larger and workloads more complex, businesses must grapple with rising infrastructure costs, latency bottlenecks, and performance degradation when deploying models in fast-paced, data-intensive environments. This paper introduces a unified theoretical framework designed to tackle these challenges through a strategic integration of four core components: Scalability-Efficiency Optimization Framework (SEOF), Edge-Cloud Hybrid Model (ECHM), Green AI Optimization (GAO), and Domain-Specific Tuning (DST). At the heart of the framework is SEOF, which combines model compression, distributed processing, and serverless deployment to optimize AI systems for responsiveness, cost-effectiveness, and resource efficiency. A new conceptual metric—the Scalability-Efficiency Trade-off Index (SETI)—is proposed to evaluate the interplay between data volume, processing speed, latency, and cloud infrastructure costs. SETI aims to help researchers and practitioners quantify trade-offs and identify optimal system configurations. ECHM addresses the growing need for low-latency AI services by moving part of the computation to edge devices, enabling faster, localized responses for applications such as real-time retail checkout, healthcare monitoring, and IoT analytics. GAO focuses on reducing the environmental impact of AI by promoting energy-efficient architectures, carbon-aware workload scheduling, and lightweight model deployment strategies—key for organizations aiming to align with sustainability goals. DST ensures that generalized AI models are tailored to industry-specific needs through fine-tuning, transfer learning, and retrieval- augmented methods that enhance accuracy and relevance in domains like finance, healthcare, and logistics. Backed by insights from over 20 academic and industry sources, this framework offers a comprehensive and adaptable roadmap for building AI systems that are scalable, sustainable, and tuned for real-world business use. By combining theoretical rigor with practical strategies, the paper contributes to the ongoing discourse on how to make AI not only smarter, but also faster, greener, and more context-aware in cloud-based business intelligence systems.

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