Work

Beyond tickets and texts: Addressing challenges in AI-driven customer service

Year: 2026

Type: article

Abstract: Adapting AI-driven solutions in customer services (CS) is becoming increasingly important to counter the combined impact of increased workload and management expectations. Addressing copious text volumes, text analytics (TA) assumes an important supporting role within CSs. However, the absence of TA and NLP application guidelines slows progress. Utilization of AI advancements in linguistically intricate languages and less digitally developed regions presents challenges. Using a systematic in-depth review of the literature and expert feedback, this study presents the TA/NLP landscape in the context of CS. The paper discusses challenges, open issues, and active research areas in TA/NLP, mainly in customer service, and validates its findings with a country example and industry experts. In addition to the theoretical and practical value of the AI-driven customer service landscape, a unique methodological approach, which includes AI-assisted content mining with BERTopic, systematic literature review, domain expert validation, and the example contributes to methodology development.

Source: International Journal of Information Management Data Insights

DOI: 10.1016/j.jjimei.2026.100395

Authors: Szabina Fodor, Csaba Csáki, Andrea Kő

Institution: Corvinus University of Budapest

Language: English

Topic: Sentiment Analysis and Opinion Mining

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,190

Funders: Nemzeti Kutatási és Technológiai Hivatal, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal