Oil and gas companies operate complex; capital-intensive assets where unplanned downtime and maintenance costs can significantly impact financial performance. This paper explores how integrating AI-driven predictive maintenance with digital twin technology can improve operational efficiency and, in turn, drive better financial outcomes in the oil and gas sector. Adopting a qualitative case study approach, we examine real-world implementations at leading oil & gas companies to assess reductions in downtime, optimized asset utilization, and improved safety. Transformation of operations gains into measurable financial metrics such as cost savings, return on investment (ROI), and increased production output was investigated. The analysis is grounded in the Resource-Based View (RBV) and Dynamic Capabilities theoretical frameworks, which help explain how these digital innovations become strategic assets that enhance firm performance. The findings indicate that AI-enabled predictive maintenance systems, coupled with accurate digital twins of critical equipment, can reduce unplanned outages by up to 20%, extend asset life, and lower maintenance costs, leading to substantial improvements in profit margins and asset productivity. This study contributes to the growing literature on digital transformation in heavy industries and provides practical insights for oil and gas executives seeking to leverage AI and digital twins for competitive advantage.