Yes, embedded systems can be used to implement AI at the edge by running lightweight machine learning models directly on edge devices. These systems use specialized hardware like microcontrollers, FPGAs, or AI accelerators to process data locally without relying on cloud servers. This enables real-time decision-making, reduces latency, and enhances data privacy. Applications include smart cameras, voice assistants, and predictive maintenance systems. By optimizing models for low power and memory usage, developers can make AI accessible on constrained devices. To gain hands-on experience, consider exploring an embedded systems course.