From ERP to Cognitive ERP: The Role of Generative AI, LLMs, Machine Learning, and Cloud Computing in Enterprise Transformation

Abstract

Traditional enterprise resource planning (ERP) systems are characterized by static, hard-coded configuration, rule-based automation, periodic batch data processing, and decision-making that remains entirely human-driven. This paper reviews the transformation from traditional ERP to what this review terms cognitive ERP, an architecture distinguished by dynamic, metadata-centric configuration, intelligent and increasingly agentic process automation, continuous event-streaming data processing, SAP-specific platform realization through Business Technology Platform-based LLM integration and S/4HANA real-time intelligence, and decision-making augmented by generative AI, large language models (LLMs), and machine learning operating on an elastic cloud substrate. The review examines a metadata-centric platform architecture for dynamic configuration and intelligent process automation, a configurable enterprise workflow architecture for digitizing risk management, and event streaming architectures for high-volume transaction processing as the structural foundations of cognitive ERP; a strategic enterprise framework integrating large language models with SAP Business Technology Platform and an intelligent enterprise framework combining SAP S/4HANA with SAP Analytics Cloud as the SAP-specific realization of these structural foundations; and generative AI business process agents, agentic multi-agent LLM architectures for autonomous enterprise resource planning, machine learning-driven ERP optimization, large language model capability, and cloud computing as the enabling technology layer. The review assembles a comparative model contrasting traditional ERP and cognitive ERP across configuration, automation, data processing, and decision-making dimensions, and identifies open challenges in governance, explainability, and the organizational change required to adopt cognitive ERP coherently rather than as isolated point capabilities.