Next-Generation Enterprise Cloud-Oriented Intelligent Workflow Automation through AI-Driven Natural Language Processing Models
DOI:
https://doi.org/10.15662/IJARCST.2024.0706035Keywords:
Enterprise Automation, Cloud Computing, Natural Language Processing, Workflow Intelligence, Artificial Intelligence, Transformers, Microservices Architecture, Digital Transformation, Intelligent Systems, Business Process AutomationAbstract
The rapid digital transformation of enterprises has accelerated the demand for intelligent workflow automation systems capable of improving efficiency, reducing operational costs, and enhancing decision-making accuracy. Traditional workflow automation tools rely heavily on rule-based engines and structured inputs, which limit their adaptability in dynamic enterprise environments. With the emergence of cloud computing and artificial intelligence, particularly natural language processing (NLP), organizations now have the opportunity to build next-generation intelligent workflow systems that can interpret, process, and execute tasks based on unstructured human language inputs.
This research proposes a cloud-oriented intelligent workflow automation framework powered by AI-driven NLP models. The system leverages advanced deep learning architectures such as transformers to understand enterprise-level textual data including emails, documents, tickets, and chat-based communications. By integrating NLP with cloud-native microservices, the framework enables automated task classification, prioritization, routing, and execution across distributed enterprise systems.
The study further explores how AI-based workflow intelligence enhances scalability, reduces human intervention, and improves operational agility. It also incorporates security, governance, and compliance mechanisms to ensure enterprise-grade reliability. The proposed approach demonstrates how organizations can transition from traditional automation to adaptive, context-aware, and self-learning workflow systems, enabling smarter enterprise operations in highly dynamic digital ecosystems
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