<![CDATA[Digital technology has introduced major disruption to operational risk management, particularly for industrial companies listed on the Indonesia Stock Exchange (BEI). The growing complexity of operations increases the difficulty of identifying and mitigating risks such as process failures, human error, outdated systems, and external disruptions—factors that can threaten business continuity and cause significant losses. This study examines how the integration of Artificial Intelligence (AI) and Machine Learning (ML) can be applied strategically to improve the accuracy, speed, and efficiency of operational risk mitigation in BEI-listed industrial firms. The main focus includes the benefits of AI/ML implementation: predictive analytics for risk identification, real-time monitoring and anomaly detection, automation of routine tasks to reduce human error, efficiency gains and cost savings, as well as improved regulatory compliance and data security. It also addresses effective implementation strategies such as starting with low-risk pilot projects, fostering cross-department collaboration and workforce training, establishing strong governance and oversight (human-in-the-loop), improving data quality and mitigating algorithmic bias, and aligning the approach with regulatory requirements and security standards. The paper further presents practical examples including predictive maintenance, sentiment analysis for reputation risk mitigation, and AI/ML-based automation for compliance reporting and auditing. Overall, the study concludes that AI/ML is not an instant solution, but a transformative approach requiring careful planning, high-quality data, integration with existing systems, and organizational readiness through training and robust governance to minimize operational risk, enhance efficiency, and maintain competitiveness.]]>
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