Financial Performance Outcomes of AI -Adoption in Oil & Gas: The Mediating Role of Operational Efficiency

The oil and gas sector operates in a high-risk environment defined by capital intensity, regulatory uncertainty, and volatile commodity prices. While Artificial Intelligence (AI) technologies such as machine learning and predictive analytics promise to reduce risk and enhance profitability, the precise mechanisms converting AI adoption into tangible financial success remain under-researched. Grounded in the Resource-Based View and Technology Adoption Theory, this study employs a dual-methodological approach: a bibliometric analysis of 201 Scopus-indexed publications (2010–2025) and a focused financial analysis of industry supermajors (BP and Shell). The results demonstrate that AI adoption alone does not guarantee superior financial results. In-stead, the relationship is mediated by operational efficiency, which accounts for up to 45% of the variation in financial performance. Specifically, the application of AI in predictive maintenance and digital twins drives improvements in asset uptime and cost control, which directly correlate with stabilized Return on Average Capital Em-ployed (ROACE), even during periods of oil price volatility. By synthesizing five key research clusters, this study provides a strategic framework verifying that AI’s value is realized through a causal chain: AI enables operational efficiency, which in turn se-cures financial resilience and capital returns.