Large Language Models in the Business World: Usage Areas, Benefits, Impacts, and Future Perspectives
Abstract
Large Language Models (LLMs) have become a central component of digital transformation in business organizations. Rather than considering LLMs holistically, existing research often tends to focus on their applications, organizational benefits, employment effects, or implementation challenges as separate dimensions. To address this gap, this study provides an integrated qualitative examination of the use of LLMs in the business world, focusing on their application areas, organizational benefits, impacts on employment and job transformation, limitations, and future potential. The study adopts a qualitative document analysis design. A systematic review of publications from 2018 to 2025 was conducted using internationally recognized academic databases and reputable industry sources. Qualitative content analysis was undertaken on the collected documents using a deductive-inductive thematic framework aligned with the study’s research questions. LLMs are extensively applied across multiple business functions, including human resources, customer service, data analysis and reporting, content creation and marketing, financial analysis, market intelligence, legal document processing, corporate training, and automation-integrated systems. LLMs adoption enhances organizational efficiency, supports cost optimization, and improves managerial decision-making by enabling rapid, data-driven, and scenario-based insights. In terms of employment, LLMs primarily drive task-level automation and job role transformation rather than direct job displacement, increasing the importance of higher-order skills such as critical thinking, supervision, and digital literacy. Despite these benefits, the study identifies persistent challenges related to ethical risks, data security and privacy, model bias, hallucinations, and governance mechanisms. This study contributes to the literature by offering an integrated qualitative framework that simultaneously links business value creation, workforce transformation, and implementation challenges, positioning LLMs adoption as a holistic phenomenon encompassing socio-technical and organizational phenomenon rather than a purely technological advancement.
Keywords
References
- Aghaei, R., Kiaei, A. A., Boush, M., Vahidi, J., Zavvar, M., Barzegar, Z., & Rofoosheh, M. (2025). Harnessing the potential of large language models in modern marketing management: Applications, future directions, and strategic recommendations. ArXiv. http://dx.doi.org/10.48550/ARXIV.2501.10685 google scholar
- Aldous, K., Salminen, J., Farooq, A., Jung, S.-G., & Jansen, B. (2024). Using ChatGPT in content marketing: Enhancing users’ social media engagement in cross-platform content creation through generative AI. Proceedings of the 35th ACM Conference on Hypertext and Social Media, 376-383. http://dx.doi.org/10.1145/3648188.3675142 google scholar
- Anthropic. (2023). Introducing Claude. https://www.anthropic.com/index/introducing-claude google scholar Arora, N., Chakraborty, I., & Nishimura, Y. (2024). Revolutionizing marketing research with a large language model: A hybrid AI-human approach. SSRN. http://dx.doi.org/10.2139/ssrn.4683054 google scholar
- Aziz, S., & Dowling, M. M. (2019). AI and machine learning for risk management. In T. Lynn, G. Mooney, P. Rosati, & M. Cummins (Eds.), Disrupting finance: FinTech and strategy in the 21st century, Palgrave, 33-50, SSRN. http://dx.doi.org/10.2139/ssrn.3201337 google scholar
- Bajjuru, R., Kacheru, G., & Arthan, N. (2022). AI for intelligent customer service: How salesforce einstein is automating customer support. Bullet: Jurnal Multidisiplin Ilmu, 1(5), 976-987. Retrieved from https://www.neliti.com/publications/592430/ai-for-intelligent- customer-service-how-salesforce-einstein-is-automating-custom google scholar
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, 610-623. http://dx.doi.org/10.1145/3442188.3445922 google scholar
- Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., Arx, S. V., … Liang, P. (2021). On the opportunities and risks of foundation models. ArXiv. http://dx.doi.org/10.48550/arXiv.2108.07258 google scholar
- Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., … Amodei, C. (2020). Language models are few-shot learners. Proceedings of the 34th International Conference on Neural Information Processing Systems, 1877-1901. Retrieved from https://dl.acm.org/doi/abs/10.5555/3495724.3495883 google scholar
Details
Primary Language
English
Subjects
Human-Computer Interaction, Machine Learning (Other), Natural Language Processing, Artificial Intelligence (Other)
Journal Section
Research Article
Publication Date
June 30, 2026
Submission Date
March 23, 2025
Acceptance Date
February 12, 2026
Published in Issue
Year 2026 Volume: 10 Number: 1