In factories, energy and logistics, AI is delivering — but it looks different from the chatbot economy. Most of the value comes from classical machine learning on sensor data, not generative models.
Where the gains are
- Predictive maintenance — anomaly detection on equipment sensors to fix things before they break.
- Quality inspection — computer vision catching defects faster and more consistently than the eye.
- Optimisation — scheduling, routing and energy use, where small percentage gains are large absolute savings.
Why generative AI is slower here
The bottleneck is physical and infrastructural. Software moves only as fast as the hardware it observes, and the cost of a wrong action on a production line is high — so autonomy stays tightly bounded.
In the field, AI earns trust the slow way: by being measurably right on a narrow task, then expanding. There's no demo shortcut.
The newest layer is generative on top — copilots that let an engineer query sensor history in plain language, or summarise an incident — but the value engine underneath remains classical and quietly effective.
Written by ivector
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