Generative AI-Powered Service Operating Systems: A Comprehensive Study of Neural Network Applications for Intelligent Data Management and Service Optimization

Abstract

Generative AI-powered service operating systems are transforming data management and service optimization by leveraging 
neural networks to enhance automation, decision-making, and adaptability. This study provides a comprehensive analysis of 
how neural network architectures, including deep learning and transformer models, enable intelligent data processing, predictive 
analytics, and autonomous system optimization. By integrating generative AI, service operating systems can dynamically refine 
workflows, personalize user experiences, and enhance operational efficiency across industries such as healthcare, finance, and smart 
infrastructure. Additionally, we explore the role of AI-driven automation in improving scalability, reducing latency, and ensuring 
data integrity. This research highlights key advancements, challenges, and ethical considerations in deploying generative AI for 
intelligent service management, emphasizing the potential for more adaptive, efficient, and resilient digital ecosystems.