Shifting workloads from traditional batch processing to real-time data streaming can significantly lower AI’s energy footprint by flattening compute spikes. This transition allows organizations to process data continuously, reducing the need to provision infrastructure for peak loads and minimizing idle energy waste. By preprocessing data in transit, architectures like Kafka and Flink also reduce redundant storage and cooling demands.