How AI Is Reshaping Risk Management in Energy Industry: A Qualitative Study of Leading Companies

Abstract

Abstract This article examines the applications of artificial intelligence (AI) in risk management process in energy sector and the emergent adoption patterns across subsectors. The paper focuses on practical and observable deployments, building on recent academic work that frames AI as a driver for the energy transition while also highlighting governance, data and cybersecurity concerns. A dataset of 50 AI use-case instances was compiled and assessed, employing a structured content analysis, which were gathered from public data available for leading energy companies. Each case was coded according to four risk management stages (identification, analysis/assessment, prediction and mitigation/treatment), a primary AI capability class (e.g. predictive maintenance, forecasting/optimization, computer vision inspection, digital twins, robotics, neuro linguistic programming (NLP)/Generative AI, cybersecurity analytics) and a dominant risk-control archetype (e.g. reliability control, inspection at scale, resilience forecasting and response optimization, operational decision support).The results suggest that AI is most frequently integrated into mitigation pathways rather than utilized as a standalone analytics layer. There are two primary patterns that emerge: (1) reliability oriented solutions that focus on time-series anomaly detection and predictive maintenance, which connect sensor signals to maintenance actions and (2) resilience and inspection oriented solutions, particularly in utilities and nuclear contexts, that prioritize interventions and reduce exposure to hazards by utilizing forecasting/optimization and computer vision/robotics methods. Subsector comparisons demonstrate domain-specific risk fit: upstream / offshore prioritize reliability control and digital twins; utilities prioritize resilience forecasting and inspection at scale; and refining/downstream prioritize decision support and compliance-linked use cases. The results indicate that the value of AI in risk management is the reduction of the detection to action cycle. Equally, to progress on the automation maturity, it is necessary to improve cybersecurity governance, transparency and validation.