Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27–29, 2023, Tianjin, China

Research Article

Research on the Influence of AI Application on Business Decision Making Based on Machine Learning Algorithm

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  • @INPROCEEDINGS{10.4108/eai.27-10-2023.2341918,
        author={Ling  Peng and Xuming  Zhang},
        title={Research on the Influence of AI Application on Business Decision Making Based on Machine Learning Algorithm},
        proceedings={Proceedings of the 4th International Conference on Economic Management and Big Data Applications, ICEMBDA 2023, October 27--29, 2023, Tianjin, China},
        publisher={EAI},
        proceedings_a={ICEMBDA},
        year={2024},
        month={1},
        keywords={machine learning algorithms; ai; business decisions; clustering algorithms; regression analysis},
        doi={10.4108/eai.27-10-2023.2341918}
    }
    
  • Ling Peng
    Xuming Zhang
    Year: 2024
    Research on the Influence of AI Application on Business Decision Making Based on Machine Learning Algorithm
    ICEMBDA
    EAI
    DOI: 10.4108/eai.27-10-2023.2341918
Ling Peng1, Xuming Zhang1,*
  • 1: Guangdong University of Science and Technology
*Contact email: zhangxuming@pukyong.ac.kr

Abstract

With the rapid development of artificial intelligence (AI) and machine learning (ML) in various fields, business decision making is also undergoing unprecedented changes. This article aims to explore the application of machine learning algorithms in the business decision making process and how it is shaping the modern business ecosystem. By building ML models to demonstrate the practical applications of AI in market analysis, risk management, customer relationship management and supply chain optimization, we assess how these technologies can enhance the quality of decision making and promote business growth. The results show that by utilizing ML technology, enterprises are able to more accurately predict market trends, identify potential risks, optimize resource allocation, and achieve personalized customer experiences. However, these technologies also bring new challenges and ethical considerations, such as issues such as data privacy and algorithmic bias.