Trends and Limitations in the Application of NLP and GenAI in Customer Experience: An Integrative Literature Review

Abstract

The advancement of Natural Language Processing (NLP) technologies has transformed how organizations interact with their customers, particularly in the field of Customer Experience (CX). Despite these innovations, there are still significant challenges related to the effectiveness and value delivery of NLP-based systems, especially those leveraging Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs). This article presents an integrative literature review aiming to answer two main questions: 1) What are the most recent technological approaches to enhancing Customer Experience (CX) through NLP and Generative AI? 2) How have recent applications of NLP and Generative AI technologies improved Customer Experience (CX)? Based on the analysis of relevant publications from indexed databases, the review is structured around five key dimensions: (i) architectures (methods, artifacts), (ii) evaluation criterias, (iii) methods and techniques used, (iv) observed results, and (v) challenges faced. The study synthesizes the findings in a comparative matrix that maps the most commonly adopted approaches along with their strengths and limitations. The review concludes that although there have been significant advances in the use of NLP, GenAI, and LLMs to optimize customer experience, many solutions still face technical, ethical, and operational barriers that hinder their broad and effective adoption. This work contributes an updated view of the field, identifies deficiencies in previous approaches, and proposes future research directions aimed at improving the practical delivery of value through NLP technologies in the context of customer experience.