Oil and gas facilities operate thousands of critical assets such as compressors, turbines, pumps, and pipelines, where equipment failures can lead to significant production losses, safety risks, and environmental impacts. Traditional maintenance strategies based on reactive or time-based approaches are increasingly inadequate for modern operations. This presentation shows the application of Intelligent Asset Management (IAM) enabled by artificial intelligence and vendor agnostic digital platforms. By integrating data from industrial sensors, SCADA/DCS systems, and maintenance management databases, AI-driven analytics enable anomaly detection, predictive failure modelling, and remaining useful life estimation. These capabilities reduce unplanned downtime, optimize maintenance scheduling, improve asset reliability, and support data-driven operational decision making across oil and gas operations.