Edge AI: Why Companies Are Moving Analytics Closer to Where Data Is Created
Analytics Insight, Friday, October 2nd, 2026
Edge AI moves ML inference onto devices near data sources, cutting latency and bandwidth for real-time decisions.
Analytics Insight explains why the centralize-everything cloud analytics model is under strain as cameras, sensors and machines generate more data than is practical to move, and how edge AI runs trained models on or near those devices to filter, score and act on data locally, sending only what matters upstream.
Three shifts make it practical now: low-power AI chips, smaller models via quantization, pruning and distillation, and better tooling.
The piece covers where edge AI pays off, what makes it hard, and how to judge whether a use case belongs at the edge.