LLM-Driven ERP Transformation: From Data Silos to Intelligent Enterprise Insights
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Enterprise Resource Planning (ERP) systems have long served as the backbone of organizational operations by integrating diverse business processes into a unified platform. However, traditional ERP implementations often face challenges such as data silos, fragmented analytics, and limited decision-making capabilities. Recent advancements in Large Language Models (LLMs) present a transformative opportunity to reimagine ERP architectures by enabling contextual understanding, semantic data integration, and conversational analytics. This paper explores the design and implementation of LLM-driven ERP transformation frameworks that bridge disparate data sources, enhance real-time business intelligence, and support predictive decision-making. By leveraging natural language querying, automated report generation, and cross-domain reasoning, the proposed framework transforms ERP systems from static transactional repositories into dynamic, intelligent enterprise platforms. The study further evaluates system performance, implementation challenges, governance requirements, and security considerations associated with integrating LLMs into ERP environments. Finally, it presents a practical roadmap for organizations seeking to leverage LLM capabilities to modernize enterprise resource planning systems and accelerate the evolution toward intelligent, data-driven enterprises.
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