Abstract

This paper addresses human error in network orchestration systems and the high cost and resource requirements of integrating artificial intelligence (AI) for network orchestration. It proposes a framework for implementing an AI decision-maker and automation. Data are fed into the AI decision-maker to trigger designated automation robots’ tasks or notify IT specialists to gradually implement automated robots, ensuring efficient resource use, reducing costs, and enhancing productivity. We evaluated the proposed method in a simulation with genuinely uncertain outcomes, across 20 independent runs: the AI decision-maker reached 78.3% accuracy against an estimated 79.1% achievable ceiling, and the proposed framework reduced operational cost by 61.4 ± 0.7% relative to fully manual operation—the best of six operating policies in the training environment—while an explicit sensitivity guard, rather than the learned model, accounts for the absence of security incidents; under distribution shift, the framework retains 43.2 ± 0.8% savings, second only to a hand-tuned rule-based router that requires environment-specific threshold calibration. However, the proposed method requires an IT specialist to implement it properly, and the AI model’s accuracy depends on the amount of input data. In the end, we recommend that future work conduct a study focused on AI decision-makers, test the proposed method on real-world companies, and implement AI decision-makers across various departments to cover a broader range of the company’s systems.

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