This technical article examines how machine learning and generative AI technologies can transform flight operations and maintenance in the airline industry. It explores the implementation of predictive analytics for flight delay forecasting and component failure detection, demonstrating how these technologies enable airlines to shift from reactive to proactive operational models. The article analyzes specific algorithms like XGBoost, LSTM networks, Random Forest, and gradient boosting techniques that have proven effective in aviation applications. It addresses implementation challenges related to data quality, legacy system integration, and organizational change management while providing insights into the return on investment and future technological developments. By leveraging AI-driven predictive strategies, airlines can enhance operational efficiency, improve maintenance practices, reduce unplanned downtime, and ultimately achieve significant cost savings while maintaining safety standards in an increasingly competitive industry.
Keywords: Digital Transformation, Predictive Maintenance, aviation efficiency, flight delay prediction, generative AI, machine learning algorithms
