How to Succeed with Embedded AI in Automation

From Model to Microcontroller How to Succeed with Embedded AI in Automation

2026-10-01 A guest post by Christoph Stockhammer and Dr. Frank Graeber* | Translated by AI 4 min Reading Time

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AI is moving from the cloud directly to the machine: Embedded AI enables real-time decisions, saves energy, and secures data. This article shows how engineers use model-based design to efficiently deploy complex AI models on resource-constrained edge hardware.

Intelligence is increasingly moving from the cloud directly into the machine.(Source: © Mang – stock.adobe.com_AI-generated) — Intelligence is increasingly moving from the cloud directly into the machine.
(Source: © Mang – stock.adobe.com_AI-generated)

Artificial intelligence is increasingly shifting from the cloud to the network edge—directly into factory floors. This offers decisive advantages for autonomous systems and fast control loops: latency-free, real-time decisions, absolute data sovereignty, and energy-efficient offline operation. However, to run complex models and generative AI locally on the machine, developers today absolutely need advanced compression and optimization techniques.

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