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https://doi.org/10.62836/emi.v3i4.243
Tian, T., Sihan Jia, Jindi Lin, Zichen Huang, Kei O Wang, & Yubing Tang. (2024). Enhancing Industrial Management through AI Integration: A Comprehensive Review of Risk Assessment, Machine Learning Applications, and Data-Driven Strategies. Economics & Management Information, 1–18. https://doi.org/10.62836/emi.v3i4.243
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Copyright (c) 2024 Tian Tian, Sihan Jia, Jindi Lin, Zichen Huang, Kei O Wang, Yubing Tang
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Enhancing Industrial Management through AI Integration: A Comprehensive Review of Risk Assessment, Machine Learning Applications, and Data-Driven Strategies

This research investigates the transformative potential of integrating artificial intelligence (AI) with comprehensive risk management frameworks in industrial management. While AI applications have advanced in industrial settings, there is a lack of studies that fully integrate AI with macro risk factors such as PESTLE (political, economic, social, technological, legal, and environmental) and ESG (environmental, social, and governance) factors. These factors, often rooted in human activities and decisions, are critical to understanding and mitigating risks in complex industrial environments. By incorporating AI methods, such as machine learning and deep neural networks, organizations can enhance their ability to identify, analyze, and mitigate these risks efficiently. Recent developments, including OpenAI’s language models, further strengthen this approach by enabling large-scale data analysis and supporting real-time risk assessment and decision-making. OpenAI’s tools can interpret vast volumes of regulatory, economic, and social data, providing valuable insights to decision-makers. This research underscores the innovative potential of AI-driven risk management to enhance the stability and resilience of industrial management. By reducing human error and adapting to dynamic risk factors, this integration offers a forward-looking strategy for optimizing performance, ensuring operational excellence, and supporting sustainable practices across sectors.

risk factors; PESTLE; ESG; AI; OpenAI; industrial management

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Supporting Agencies

  1. Funding: This research received no external funding.