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AI-Driven Risk Management for Sustainable Enterprise Development: A Review of Key Risks

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  • Zhenglong Guo
  • Runshuai Li

Abstract

The proliferation and application scope of artificial intelligence (AI) have witnessed a gradual expansion in the last several years, marking a substantial leap in quality when compared to the landscape a decade ago. Its permeation into every industry is undeniable, yet it concomitantly poses a multitude of challenges that are inherently difficult to fully mitigate across various domains. By harnessing the power of AI, enterprises can significantly augment their risk identification and management capabilities, ultimately fostering sustainable development. The implementation of AI technologies facilitates automated analysis of complex data sets, rapid identification of potential risk points, and offers precise early warning systems along with invaluable decision support. However, it is imperative to acknowledge that AI itself is not without risks. The rapid pace of its development has been accompanied by a proliferation of incident reports, highlighting the wide range and intricate nature of the risks involved. The unfortunate instances of technology companies succumbing to bankruptcy further underscore the fact that the development and utilization of AI are not devoid of vulnerabilities, thereby posing latent threats to the sustainable development of enterprises. Consequently, this paper adopts a rigorous literature review method, carefully selecting 82 highly pertinent articles, to comprehensively synthesize and analyze the drawbacks and risks associated with AI applications in recent years. The ultimate aim is to provide insightful guidance for the sustainable growth of enterprises and the efficient harnessing of AI technologies.

Suggested Citation

  • Zhenglong Guo & Runshuai Li, 2024. "AI-Driven Risk Management for Sustainable Enterprise Development: A Review of Key Risks," International Journal of Business and Management, Canadian Center of Science and Education, vol. 19(6), pages 1-82, December.
    • Handle: RePEc:ibn:ijbmjn:v:19:y:2024:i:6:p:82
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    References listed on IDEAS

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    1. Xiaodong Teng & Kun-Shan Wu & Lopin Kuo & Bao-Guang Chang, 2023. "Investigating the double-edged sword effect of environmental, social and governance practices on corporate risk-taking in the high-tech industry," Oeconomia Copernicana, Institute of Economic Research, vol. 14(2), pages 511-549, June.
    2. Lundvall, Bengt-Åke & Rikap, Cecilia, 2022. "China's catching-up in artificial intelligence seen as a co-evolution of corporate and national innovation systems," Research Policy, Elsevier, vol. 51(1).
    3. Piotr Staszkiewicz & Jarosław Horobiowski & Anna Szelągowska & Agnieszka Maryla Strzelecka, 2024. "Artificial intelligence legal personality and accountability: auditors’ accounts of capabilities and challenges for instrument boundary," Meditari Accountancy Research, Emerald Group Publishing Limited, vol. 32(7), pages 120-146, June.
    4. Anna Viktorovna Shkalenko & Anton V. Nazarenko, 2024. "Integration of AI and IoT into Corporate Social Responsibility Strategies for Financial Risk Management and Sustainable Development," Risks, MDPI, vol. 12(6), pages 1-21, May.
    5. Thereza Raquel Sales de Aguiar, 2024. "ChatGPT: reflections from the UK higher education institutions, accountancy bodies and BIG4s," Accounting Research Journal, Emerald Group Publishing Limited, vol. 37(3), pages 308-329, June.
    6. A. Atabekov & O. Yastrebov, 2018. "Legal Status of Artificial Intelligence Across Countries: Legislation on the Move," European Research Studies Journal, European Research Studies Journal, vol. 0(4), pages 773-782.
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    JEL classification:

    • R00 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General - - - General
    • Z0 - Other Special Topics - - General

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