Financial Performance Outcomes of AI-Adoption in Oil and 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. Although Artificial Intelligence (AI) is widely promoted as a lever for profitability, the mechanisms through which AI adoption translate into financial outcomes remain insufficiently specified in the oil and gas literature. Grounded in the Resource-Based View and Technology Adoption Theory, this study combines bibliometric mapping of 201 Scopus-indexed publications (2010–2025) with a focused comparative case analysis of important players (BP and Shell), based on publicly reported operational and financial indicators (e.g., operating cost, uptime-related evidence, and return on average capital employed—ROACE). Keyword co-occurrence analysis identifies five thematic clusters showing that efficiency-oriented AI use cases (optimization, automation, predictive maintenance, and digital twins) dominate the research landscape. A thematic synthesis of five highly cited studies further indicates that AI-enabled operational improvements are most consistently linked to measurable cost, productivity, or revenue effects. Case evidence suggests that large-scale predictive maintenance and digital twin programs can support capital efficiency by reducing unplanned downtime and structural costs, contributing to more resilient ROACE trajectories amid price swings. Overall, the findings support a conceptual pathway in which operational efficiency is a primary channel through which AI can create financial value, while underscoring the need for future firm-level empirical mediation tests using standardized KPIs.