Among the numerous technical challenges facing the aviation industry, two stand out as both critical and urgent: the adoption of NDC and the application of AI technology. At the nexus of these, we find particularly promising use cases for AI, enabling aviation businesses to adapt to, design, and create new NDC capabilities faster and more efficiently. While AI will help organizations lower overhead costs and generate operational efficiency, NDC adoption harnessing new intelligent retailing and offering generation capabilities holds the promise of increased revenue generation and lower distribution costs. The perfect pairing.
One of the first and widely touted uses of AI technology continues to be a low effort – high reward opportunity to decrease overhead costs: chatbots. Training an AI chatbot on vetted marketing and sales material creates a reliable lead-nurturing tool. Adding a layer of operational and booking data generates a customer service tool that can assist with new bookings, changes to existing bookings, loyalty program management, and call handling during IROPS. Chatbots can also be trained to deal with rote employee inquiries that usually land in the HR inbox and drain precious resources that could be applied elsewhere.
While not as headline-grabbing, a more advanced, high-effort application carries the potential of even more savings and operational efficiency: AI-powered translation of airline-specific NDC schemas into a standardized version. An otherwise resource-intensive task, this standardization initiative will ease the integration with OTAs and facilitate the adoption of IATA’s NDC framework across the travel industry.
How would it work? Large Language Models (LLM) such as the GPT series, PaLM, LLaMa, and Claude are trained on billions of text files, code, and data points. With this, a LLM can be tasked to create a blueprint structure based on the industry standard NDC format. Then, with advanced prompt engineering, this LLM can convert an airline’s unique JSON structure into the supported schema at far greater speed and scale than would have traditionally been accomplished.
A key strength of NDC is the improved ability to personalize and enhance offers, with dynamic pricing and greater inclusion of ancillary products and services such as accommodation, car rentals, and attractions all included. Rich content can also be integrated into these offers, giving providers better control over product marketing. However, the collection, parsing, and mapping of data needed to produce these personalized offers require a significant upfront investment in, among other things, new tech stacks, changes to workflows, and specialist staff such as business analysts, developers, and NDC experts. These are some of the obstacles on the road that hinder progress for airlines on their journey to expose NDC content to the market. AI can offer a way past them and towards the finish line.
