A Systematic Literature Review of Large Language Model Applications in Industry

Abstract

Large Language Models are rapidly transforming processes across industries by enabling advanced capabilities in natural language understanding, code generation, diagnostics, and decision support. Despite the growing adoption of this technology, a systematic understanding of their application across the industrial value creation processes remains lacking. This paper addresses this gap by conducting a systematic literature review of 96 peer-reviewed studies, following the PRISMA guidelines. Based on this foundation, large language model use cases across industries were identified, categorized, and structured using the primary and secondary activities of Porter’s value chain as a classification framework. The analysis reveals that LLM adoption is heavily concentrated in technology-focused and internal operational activities across industries, where they offer immediate benefits at lower risk. In contrast, areas such as logistics, procurement, and customer-facing functions remain largely unexplored, mainly due to challenges related to integration, data governance, and regulatory requirements. The analysis shows that current deployments are primarily limited to isolated, manageable use cases, leaving substantial innovation potential unrealized in underexplored value chain activities. These findings provide a foundation for further research and for the strategic adoption of large language models throughout the industrial value chain.