AI-Driven Energy Optimisation for Enterprise Integration Platforms: A Framework for Sustainable and Intelligent Digital Enterprises

Abstract

Enterprise integration platforms increasingly rely on artificial intelligence to coordinate energy-intensive digital infrastructure across cloud, edge, and on-premises environments. This article presents a layered framework in which predictive analytics, reinforcement learning, and digital twin technologies operate within enterprise resource planning, middleware, and Internet of Things layers to reduce electricity consumption while preserving operational performance. Evidence gathered across recent scholarship shows that intelligent energy platforms lower total energy use, improve forecasting accuracy, and strengthen resilience against demand fluctuations without materially degrading service quality. Edge intelligence brings computation closer to data sources, cutting latency and easing the load placed on centralized infrastructure, while thematic analyses of the wider literature confirm that energy optimization, ethical governance, and computational efficiency now define the core research agenda for sustainable artificial intelligence. Enterprise-scale validation demonstrates that carefully tuned optimization algorithms achieve substantial reductions in energy consumption with only marginal effects on accuracy or throughput, and coordinated software engineering practices extend these gains through standardized benchmarking and sustainability-aware architecture. Circular economy applications further show that multi-layered optimization of resources, materials, and computing assets improves recovery efficiency alongside energy savings, and digital twin integration across smart and green buildings extends these benefits to the built environment that houses enterprise systems. Together, these findings indicate that intelligent, integrated energy platforms deliver measurable environmental and financial value for digital enterprises. Organizations that adopt layered artificial-intelligence architectures gain a practical pathway toward lower operating costs, stronger regulatory compliance, and more resilient digital operations, positioning energy-aware design as a core element of enterprise technology strategy rather than a peripheral concern.