AI-Driven Enterprise Systems: A Framework for Digital Transformation, Intelligent Integration, and Future Business Operations - Madhulika Chaudhuri; Manar Shatara (Breakthroughs Information Technology, 2026)
Objective: This study aims to develop a comprehensive conceptual framework for Artificial Intelligence-Driven Enterprise Systems (AI-ES) by synthesizing recent literature on the integration of Artificial Intelligence (AI) within Enterprise Systems and examining its role in enabling intelligent business operations and digital transformation. Research Design & Methods: A Systematic Literature Review (SLR) was conducted following a structured review protocol. The selected studies were analyzed using thematic synthesis and classified into four interconnected analytical layers: AI Technology Layer, Intelligent Enterprise System Layer, Business Intelligence Layer, and Business Outcome Layer. Findings: The review indicates that AI technologies including Machine Learning, Deep Learning, Natural Language Processing, Large Language Models, Predictive Analytics, IoT, and cloud computing serve as the technological foundation for intelligent enterprise systems. Their integration into ERP, CRM, PLM, and enterprise platforms enhances intelligent process automation, predictive decision support, supply chain optimization, and real-time data integration. These capabilities collectively strengthen business intelligence by improving analytics, forecasting, operational visibility, and adaptive decision-making, ultimately leading to operational excellence, organizational agility, innovation capability, competitive advantage, and sustainable business performance. Implications: The proposed framework provides practical guidance for organizations seeking to implement AI-enabled Enterprise Systems by emphasizing the importance of technological readiness, enterprise integration, data governance, interoperability, and organizational capabilities as prerequisites for successful digital transformation. Contribution & Value Added: This study contributes by proposing a holistic AI-Driven Enterprise Systems Framework that integrates AI technologies, intelligent enterprise platforms, business intelligence capabilities, and business outcomes into a unified conceptual model. The framework extends existing Enterprise Systems literature by offering a structured foundation for future empirical research and practical AI adoption strategies in digital enterprises.