Abstract

The rapid rise of artificial intelligence within the embedded systems domain has reshaped the landscape of the modern management. Prior work often treated intelligence as a cloud-centric asset, while the embedded systems role within local decision support received modest scholarly focus. The demand for responsive, context-aware, and resource-efficient platforms within the management environments was evident across industrial and organizational settings. Conventional management architectures relied on centralized computation and static rule sets, which were insufficient for dynamic operational contexts. Latency, data privacy risk, and poor adaptability constrained timely decisions at the operational edge. The absence of an integrated framework that aligned artificial intelligence with the embedded systems capabilities limited the effectiveness of real-time management support. This study did propose an architectural framework that integrated artificial intelligence models within embedded systems at the edge layer. The design did emphasize modular intelligence units, adaptive control logic, and local inference pipelines. The framework did rely on lightweight neural inference and rule-based reasoning for resource-aware execution. Experimental validation did occur on representative management scenarios that involved resource allocation, anomaly detection, and operational decision support under constrained hardware conditions. The proposed method demonstrates superior performance across all evaluation metrics. The system achieves a decision latency of 48 ms at 1000 cycles, which improves by over 64% when compared with centralized intelligence. Decision accuracy reaches 93.8%, while resource utilization efficiency attains an index of 0.90. The adaptability index increases to 0.82 under dynamic workloads, and the execution success rate remains at 99.3%. These results confirm that artificial intelligence within embedded systems enables scalable, low-latency, and reliable management decision support.

Authors

Veena Tewari
University of Technology and Applied Sciences-Ibri, Sultanate of Oman

Keywords

Artificial Intelligence, Embedded Systems, Decision Support, Management Control, Edge Intelligence