Empowering Enterprise Decision Intelligence through Retrieval Augmented Generation and Adaptive Cloud Computing
DOI:
https://doi.org/10.15662/IJARCST.2024.0706047Keywords:
Retrieval Augmented Generation, Adaptive Cloud Computing, Enterprise Decision Intelligence, Artificial Intelligence, Knowledge Retrieval, Cloud Infrastructure, Large Language Models, Decision Support Systems, Business Intelligence, Data AnalyticsAbstract
Enterprise decision intelligence has emerged as a strategic approach for enabling organizations to make data-driven, timely, and accurate decisions in increasingly complex business environments. The integration of Retrieval Augmented Generation (RAG) with adaptive cloud computing presents a transformative framework that enhances knowledge retrieval, contextual reasoning, scalability, and operational efficiency. Traditional artificial intelligence models often struggle with outdated knowledge, hallucinations, and limited access to enterprise-specific information. RAG addresses these limitations by retrieving relevant information from trusted organizational repositories before generating responses, thereby improving accuracy, transparency, and reliability. Adaptive cloud computing complements this capability by providing scalable infrastructure, dynamic resource allocation, real-time analytics, and secure access to distributed enterprise data. Together, these technologies support intelligent decision-making across domains such as finance, healthcare, manufacturing, retail, and public administration. This study explores the role of RAG and adaptive cloud computing in strengthening enterprise decision intelligence by examining existing literature, technological advancements, implementation strategies, and research methodologies. It highlights the significance of integrating intelligent retrieval mechanisms with cloud-based computational resources to improve business agility, reduce operational risks, and enhance organizational competitiveness. The proposed research framework provides insights into developing scalable, secure, and context-aware decision support systems capable of responding effectively to evolving business challenges and rapidly changing digital ecosystems
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