Energy Efficient Enterprise Integration in the Age of AI addresses a problem that most enterprise architecture literature has largely overlooked: the growing energy cost of the systems that connect and power modern organizations, and what it takes to design that cost down without compromising performance. For decades, enterprise integration was optimized around a familiar set of concerns reliability, throughput, latency, and scalability. Energy consumption rarely factored into architectural decision-making in any deliberate way.

That has changed. The widespread adoption of AI within enterprise systems model training, real-time inference, continuous retraining, and large-scale data pipelines feeding these workloads has dramatically increased the computational and energy footprint of integration architectures that were never designed with this level of demand in mind. At the same time, organizations face real pressure from regulators, investors, and their own sustainability commitments to account for the environmental impact of their technology infrastructure. This book argues that energy efficiency must be treated as a first-class architectural concern in enterprise integration not an afterthought bolted on once systems are already in production, but a design principle weighed alongside performance, reliability, and cost from the very beginning.