Designing Secure and Scalable Customer and Order Automation Frameworks in AI-Enabled Cloud Ecosystems
DOI:
https://doi.org/10.15662/IJARCST.2024.0703010Keywords:
AI automation, cloud computing, enterprise systems, secure software engineering, order management, customer automation, workflow optimization, data privacyAbstract
The rapid growth of cloud computing and artificial intelligence (AI) technologies has transformed enterprise operations, enabling automated customer interactions and order processing. Optimizing customer and order automation is essential for enhancing operational efficiency, reducing human error, and delivering personalized services in modern enterprise systems. This study explores the integration of AI-driven automation with cloud-enabled enterprise architectures, focusing on secure software engineering practices to ensure system reliability, data integrity, and regulatory compliance. AI models, including machine learning algorithms for customer behavior prediction and natural language processing for automated customer support, are implemented in scalable cloud environments to streamline order management, inventory tracking, and customer engagement. The research evaluates architectural frameworks, workflow automation strategies, and security protocols to minimize latency, maximize throughput, and safeguard sensitive information. A mixed-methods approach, combining simulation, prototype implementation, and security analysis, is employed to validate system performance. Results indicate that integrating AI and cloud solutions with secure software engineering practices enhances operational efficiency, improves customer satisfaction, and reduces operational costs. The study also highlights challenges, including data privacy, model interpretability, and system scalability, providing actionable insights for enterprises aiming to modernize and secure automated business processes.References
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