Intelligent Enterprise Decision Support with Large Language Models AI and Multi-Cloud Architectures for Business Intelligence
DOI:
https://doi.org/10.15680/IJMRSETM.2025.0106015Keywords:
Large Language Models, Artificial Intelligence, Business Intelligence, Multi-Cloud Computing, Enterprise Decision Support, Generative AI, Data Analytics, Cloud Architecture, Decision-Making, Intelligent EnterpriseAbstract
The rapid growth of enterprise data, artificial intelligence (AI), cloud computing, and large language models (LLMs) is transforming traditional business intelligence (BI) into intelligent enterprise decision-support environments. Conventional BI systems primarily depend on structured data, predefined dashboards, fixed queries, and retrospective reporting, whereas LLM-enabled decision support can combine structured and unstructured information, interpret natural-language questions, generate analytical explanations, and support scenario-based managerial decision-making. Multi-cloud architectures further enhance these capabilities by enabling organizations to distribute data, analytical workloads, AI models, and applications across multiple cloud providers, thereby improving scalability, resilience, flexibility, and access to specialized services. However, integrating LLMs and multi-cloud technologies introduces challenges involving data governance, interoperability, security, privacy, model hallucination, cost management, latency, regulatory compliance, and explainability. This essay examines the conceptual relationship between LLMs, AI, BI, and multi-cloud architecture in developing intelligent enterprise decision-support systems. It proposes a research methodology based on a mixed-method and design-oriented approach to investigate how these technologies can improve decision quality, analytical agility, operational efficiency, and organizational responsiveness. The study emphasizes human-AI collaboration, trustworthy AI, cross-cloud interoperability, and governance as essential foundations for successful enterprise adoption. The proposed framework provides a basis for evaluating intelligent BI architectures and identifying the organizational and technological conditions required for reliable, scalable, and strategically valuable AI-supported decision-making
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