Strategic Decision-Making Support Using Large Language Models (LLMs)

This paper investigates the role of Large Language Models (LLMs) in enhancing strategic decision-making within complex business environments. As organizations grapple with increasing data complexity and volatility, traditional decision-making methods often fall short. LLMs, as advanced AI tools, offer the ability to analyze vast amounts of both structured and unstructured data, generate predictive insights, and support real-time scenario planning. Through a detailed case study of a multinational retail corporation, the paper illustrates how LLMs can improve business forecasting, facilitate dynamic scenario planning, and provide real-time decision support. The implementation of LLMs in the case study led to more accurate forecasts, better risk management, and a more agile strategic response, ultimately strengthening the organization’s competitive position. The findings underscore the potential of LLMs to serve as critical components of decision support systems, offering significant advantages in navigating today’s rapidly changing business landscapes.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-29
DOI
https://doi.org/10.5281/zenodo.13444480
Primary Topic
Forecasting Techniques and Applications
Type
article
Field-Weighted Citation Impact
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article

Strategic Decision-Making Support Using Large Language Models (LLMs)

Zenodo (CERN European Organization for Nuclear Research)
Forecasting Techniques and Applications
article

Strategic Decision-Making Support Using Large Language Models (LLMs)

article en

Abstract

This paper investigates the role of Large Language Models (LLMs) in enhancing strategic decision-making within complex business environments. As organizations grapple with increasing data complexity and volatility, traditional decision-making methods often fall short. LLMs, as advanced AI tools, offer the ability to analyze vast amounts of both structured and unstructured data, generate predictive insights, and support real-time scenario planning. Through a detailed case study of a multinational retail corporation, the paper illustrates how LLMs can improve business forecasting, facilitate dynamic scenario planning, and provide real-time decision support. The implementation of LLMs in the case study led to more accurate forecasts, better risk management, and a more agile strategic response, ultimately strengthening the organization’s competitive position. The findings underscore the potential of LLMs to serve as critical components of decision support systems, offering significant advantages in navigating today’s rapidly changing business landscapes.

Zenodo (CERN European Organization for Nuclear Research)
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Peace, Justice and strong institutions
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Forecasting Techniques and Applications
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