Relay Protection and Automation (RPA) devices maintain power system stability by isolating faults using predefined or adaptive thresholds. However, even flexible configurations require manual pre-tuning, limiting their adaptability in dynamic grid environments, such as those with renewable energy integration. Artificial Intelligence (AI) augments RPA functionality by generalizing to new, unseen data, improving fault detection accuracy in transient or noisy scenarios. Our early studies demonstrate that embedded AI systems outperform static thresholds while operating in real time. These systems, implemented via neural networks on System-on-Chip (SoC) platforms with dedicated Neural Processing Units (NPUs), avoid cloud-dependent latency.