International Journal of Science, Engineering and TechnologyAuthors: Hasina Chowdhury
Abstract: The integration of natural language artificial intelligence (AI) models into enterprise process automation signifies a profound evolution in how organizations manage, optimize, and execute business operations. Natural language models such as GPT, BERT, and LLaMA extend beyond traditional automation systems by incorporating deep contextual understanding and human-like communication capabilities. These models process unstructured data, interpret intent, and respond intelligently, enabling enterprises to bridge the gap between human reasoning and machine efficiency. Their deployment allows automation of communication-centric tasks, including customer service interactions, internal queries, and operational coordination, thereby reducing dependency on manual intervention and minimizing human error. In modern enterprises, natural language AI models are increasingly embedded within platforms for intelligent document processing, report generation, and decision support. Through capabilities like text summarization, sentiment analysis, and information extraction, these systems transform vast amounts of unstructured data into actionable insights. This not only accelerates workflow execution but also enhances strategic decision-making. For example, AI-driven chatbots and digital assistants can autonomously resolve customer issues or facilitate employee support, freeing human resources for higher-value tasks. Furthermore, when integrated with robotic process automation (RPA) and business intelligence (BI) systems, natural language AI models enable adaptive workflows that continuously learn from interactions and adjust processes in real time.
