AI inference is moving to the network edge as sensor data multiplies, latency tolerance falls and specialized chips improve. Gartner analyst Thomas Bittman says the rapid growth of edge data and the need for AI business value are driving significant edge AI expansion. By 2028 more than two-thirds of enterprise-managed data will be created and processed outside data centers or the cloud. More than two-thirds of enterprises are expected to deploy edge AI by 2029, up from 10 percent in 2025. IDC predicts half of all enterprise AI inference workloads will run on endpoints or edge nodes by 2030. Key drivers include data gravity from billions of IoT devices, data control and sovereignty rules, the need for low latency in multimodal applications, and the high cost of shipping data to the cloud. Enabling technologies include neural processing units, neuromorphic chips and smaller language models that can be trained in the cloud and run at the edge.
AI inference is moving to the network edge as sensor data multiplies, latency tolerance falls and specialized chips improve. Gartner analyst Thomas Bittman says the rapid growth of edge data and the need for AI business value are driving significant edge AI expansion. By 2028 more than two-thirds of enterprise-managed data will be created and processed outside data centers or the cloud. More than two-thirds of enterprises are expected to deploy edge AI by 2029, up from 10 percent in 2025. IDC predicts half of all enterprise AI inference workloads will run on endpoints or edge nodes by 2030. Key drivers include data gravity from billions of IoT devices, data control and sovereignty rules, the need for low latency in multimodal applications, and the high cost of shipping data to the cloud. Enabling technologies include neural processing units, neuromorphic chips and smaller language models that can be trained in the cloud and run at the edge.