International Journal For Multidisciplinary Research

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Case Study of Skilful Use of LLM-Based Generative AI in Management of a Hypothetical Business

Author(s) Dr A.L.Ravimohan, Dr Vikas Sahasrabudhe
Country India
Abstract Now that generative AI technology products are already spreading worldwide and are improving their capabilities very fast, it is high time we examine what it can do to decision making, which is the essence of managing businesses. The present paper has used an innovative methodology where one of the currently available Large Language Model based product, namely ChatGPT, is given a series of questions/prompts to use its own capabilities to give its self-assessment of supporting the different dimensions of managing a business. Multiple conversations illustrated the need for well-structured prompts/questions to get the most out of ChatGPT, otherwise it can get confused and provide illogical response. The quality of mutual learning improves by constructive feedback between the human interlocutor and generative AI. Probing it further with multiple scenarios for a hypothetical mid-sized business has verified and confirmed those self-assessments. The insights and conclusions drawn from those conversations are that skillful use of the LLM-based generative AI products can assist human managers in making better quality decisions at all levels of management in a timely manner. Senior managers of businesses need to begin an effort to test-drive an available LLM-based generative AI product and plan to leverage it in the ongoing management to become a learning organization in the interest of the business.
Keywords Generative Artificial intelligence, Large language model, Case study, Management of a business, Strategy, Business analysis
Field Business Administration
Published In Volume 6, Issue 3, May-June 2024
Published On 2024-05-07
Cite This Case Study of Skilful Use of LLM-Based Generative AI in Management of a Hypothetical Business - Dr A.L.Ravimohan, Dr Vikas Sahasrabudhe - IJFMR Volume 6, Issue 3, May-June 2024. DOI 10.36948/ijfmr.2024.v06i03.19697
DOI https://doi.org/10.36948/ijfmr.2024.v06i03.19697
Short DOI https://doi.org/gttbdp
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