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This paper analyzes the application of NLP models in business processes, focusing on tasks like text generation, classification, customer support automation, summarization, translation, and marketing personalization. It examines the technological pipeline for NLP model creation, including tokenization, vector representation, and attention mechanisms. The study highlights the potential of NLP for optimizing business processes, particularly within the Russian context, and provides practical recommendations for implementation.
Unlock hidden pricing insights: NLP models can extract crucial information from unstructured dialogues, transforming business operations.
Goal. This article discusses the theoretical and practical aspects of applying natural language processing (NLP) models in business processes of organizations of various scales. The purpose of the research is to systematize and analyze the main directions of NLP models application in modern business, as well as to develop practical recommendations for their effective implementation. Model. The research is based on the work of foreign authors who conceptually studied the application of NLP models in relation to various business processes. The research methodology is based on a systematic analysis of scientific publications and industry reports, a comparative analysis of technological solutions and a structural and functional approach to the systematization of NLP applications. Conclusions. The analysis of the main tasks solved using NLP models in business is carried out, among which are the generation of text content, text classification, automation of customer support, information summarization, machine translation and personalization of marketing interaction. The technological pipeline for creating and training NLP models is studied with a detailed examination of tokenization processes, vector representation of data, and the application of the attention mechanism. The research results demonstrate the high potential of NLP technologies for optimizing business processes. It has been revealed that, despite the growing interest in these technologies, their full-fledged implementation into the business processes of domestic companies remains limited. Practical significance. Practical recommendations on a strategic approach to the implementation of NLP technologies are formulated, including step-by-step integration with measurable results, focus on solving specific business problems and the need for investments in staff training. An example of the application of the model to solve the problem of extracting information from text in Russian is shown. Social consequences. The widespread introduction of NLP technologies in business leads to significant changes in the employment structure, requiring the retraining of specialists and the creation of new competencies in the labor market. Originality/value. The study is valuable for business leaders, IT specialists and digital transformation specialists, and offers a comprehensive analysis of the possibilities of using NLP technologies in various sectors of the economy. The novelty of the work lies in the systematization of NLP models in business, taking into account Russian specifics and current market trends.
