Why Intelligence At The Edge Is No Longer Optional
RTInsights, Tuesday, August 19th, 2025
By processing data locally, organizations can filter and act on the most important insights immediately, while sending only relevant or aggregated data upstream to the cloud for longer-term analysis and storage.
AI and intelligence at the edge have become critical because of the fundamental limitations of centralized processing in today's data-heavy world. The proliferation of sensors, IoT devices, visual systems, and other devices along production lines, in autos, embedded into industrial devices, and more, generates ever-larger data volumes. Much of that data needs to be analyzed or run through AI models to make fast and intelligent decisions.
Sending all this information back to centralized data centers for processing creates unavoidable bottlenecks. Latency is the most obvious constraint. Milliseconds matter when any system must act in real time.