Transformative Potential of NLP-Enabled AI in Advancing Applied Science and Technology

The integration of Natural Language Processing (NLP) with Artificial Intelligence (AI) is rapidly transforming the landscape of applied science, information systems, and technologydriven decision-making. This interdisciplinary synergy empowers machines to understand, analyze, and generate human language with high semantic precision, enabling organizations to extract actionable insights from unstructured data. Core NLP techniques-such as tokenization, named entity recognition, sentiment analysis, and topic modelling-are now widely deployed across industries to enhance operational intelligence and automation. This paper presents a comprehensive examination of the transformative role of NLP-enabled AI in managerial and technical environments. A structured implementation methodology is introduced, underpinned by a hybrid computational architecture that combines Python-based NLP frameworks (spaCy, NLTK, Scikit-learn) for semantic processing with MATLAB's statistical engine for simulation, modelling, and interactive dashboard visualizations. This integration is achieved using the MATLAB Engine API for Python, enabling a seamless workflow between linguistic analysis and numerical computation. Simulations conducted on real-world datasets demonstrate improvements in information retrieval, sentiment precision, and adaptive decision-making. These findings highlight the critical role of NLP-AI systems in shaping agile, data-informed organizational infrastructures.