Summary
This document is a guide that outlines essential considerations for implementing artificial intelligence (AI) in the aviation data sector. It discusses the growing reliance on data and connectivity within the Air Transport Industry (ATI) and emphasizes the importance of integrating diverse data sources to enhance AI applications. The guide presents five key tips for using AI effectively, starting with identifying specific business problems that AI can address. It highlights the significance of combining internal and external data to improve predictive analytics and operational efficiency. Furthermore, the document stresses the necessity of maintaining high-quality data, as AI models depend heavily on the data quality behind them. It also addresses the evolving nature of AI in aviation, advocating for decentralized data processing and the integration of privacy and security measures from the outset. Lastly, the guide suggests that aviation companies should invest in a range of AI skills to effectively develop and maintain AI models.
