Large language models for business and management applications: A review

Large language models (LLMs) are rapidly reshaping business and management, yet existing reviews remain fragmented, with limited integration of bibliometric structure and operational insights. This study addresses that gap by combining bibliometric analysis with a systematic thematic synthesis, linking publication trends, intellectual structure, and real-world deployment patterns across 84 application-focused studies selected from an initial pool of 210 records identified from Scopus, Web of Science, and Google Scholar (up to August 2025). The analysis shows a sharp post-2023 expansion of research alongside a shift from general-purpose conversational use toward domain-specific and architecture-aware implementations. In contrast to prior reviews, this study identifies how application patterns differ across domains: knowledge-intensive contexts increasingly adopt retrieval-augmented generation (RAG) for grounding, while finance applications more frequently rely on fine-tuned and domain-specific models. Keyword co-occurrence analysis highlights a technology-centric core, dominated by ChatGPT, foundation models, and RAG, linked to key business domains including operations, finance, marketing, and supply chain management. While organisations are increasingly adopting customised LLM pipelines and governance-aware workflows, challenges persist in accuracy, integration, ethical risk, and data quality. By consolidating dispersed evidence, this study offers a structured understanding of emerging application patterns and provides guidance for future research and responsible enterprise adoption of LLMs.