AIoT describes systems where Internet of Things devices do more than collect readings: machine learning models, running on the device, on a gateway or in the cloud, turn those readings into classifications, predictions and actions. The pairing matters because raw sensor streams are too large and too noisy for people to read, and because many decisions, such as stopping a machine or unlocking a door, have to happen faster than a round trip to a data center allows.
A typical example is a vibration sensor on a pump that runs an anomaly detection model locally and only sends an alert when a pattern changes. The trade-off is complexity: models need training data, updates and monitoring, which adds to the device lifecycle. Read What is AIoT? for the broader picture, including where edge AI fits.
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