Transformation of Risk Management through the Integration of an Artificial Intelligence Approach in Various Industrial Sectors

Tambunan, Martua Eliakim and Santosa, Perdana Wahyu (2026) Transformation of Risk Management through the Integration of an Artificial Intelligence Approach in Various Industrial Sectors. MIX: JURNAL ILMIAH MANAJEMEN, 16 (1). pp. 328-340. ISSN 2460-5328

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Abstract

Background: The development of artificial intelligence (AI) technology has brought fundamental changes to various industrial sectors, including risk management practices. Conventional risk management systems are often considered inefficient in dealing with the complexity, dynamics, and uncertainty of the digital era. This drives the need for a new, more adaptive, predictive, and data-driven paradigm to improve the effectiveness of risk mitigation. Objectives: This research aims to analyze the paradigm shift in risk management practices influenced by the development of AI technology, as well as to identify the contributions, challenges, and ethical implications of integrating AI in various industrial sectors. Methodology: The research employed a qualitative approach, utilizing systematic literature reviews, cross- sectoral case studies, and inter-industry comparisons. The analysis was conducted descriptively and analytically to gain a holistic understanding of the application of AI in the risk management cycle, encompassing the identification, analysis, mitigation, and monitoring stages. Finding: The study's findings demonstrate that AI integration significantly improves operational efficiency, risk prediction accuracy, and strategic decision-making acumen. These findings also highlight the importance of robust data governance, algorithmic transparency, and an ethical framework for the sustainable implementation of AI. The study's novelty lies in its comprehensive, cross-industry mapping of AI's role in the risk management cycle. The primary contribution of this research is to provide a theoretical foundation and practical implications for policymakers and practitioners in designing more responsive and responsible risk mitigation strategies in the era of digital transformation. Conclusion: This study confirms that AI plays a central role in transforming the risk management paradigm from a reactive to a proactive and adaptive system. The research's novelty lies in its comprehensive, cross-industry mapping of AI's role in the risk management cycle. Its primary contribution is providing a theoretical foundation and practical implications for policymakers and practitioners in designing more responsive, transparent, and responsible risk mitigation strategies in the era of digital transformation

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