Severe weather events gripping the United States are placing immense pressure on the domestic airline industry, triggering schedule disruptions and route adjustments with global repercussions. In such challenging periods, airlines face an exponential surge in customer inquiries, necessitating rapid operational decisions that adhere to the most stringent safety protocols.
To navigate these complexities and foster greater organizational efficiency and responsiveness, several carriers are increasingly turning to generative artificial intelligence (AI).
Last year, Air France-KLM established a cloud-based generative AI “factory” designed for enterprise-wide application. This initiative aims to standardize and enhance the reusability of AI development efforts. In collaboration with Accenture and Google Cloud, the airline is leveraging this platform to expedite the testing and deployment of generative AI models. Initial results show significant improvements in ground operations, engineering, maintenance, and customer-facing functions, with the partnership reporting a more than 35% increase in development speed for enterprise AI deployments.
This AI factory builds upon prior work by Air France-KLM and Accenture, which included migrating core applications to the cloud. Subsequent advancements have led to the creation of a private AI assistant and Retrieval-Augmented Generation (RAG) tools that connect Large Language Models (LLMs) with internal search capabilities, aiding in tasks such as aircraft damage diagnosis and repair. The factory also serves as a training ground for employees, equipping them with the skills to harness LLMs for business enhancement.
**AI in Action: Responding to Operational Disruptions**
United Airlines is also actively integrating AI into its operations. According to CIO Jason Birnbaum, AI is instrumental in “shortening decision cycles” during irregular operations, such as those caused by recent extreme weather. The airline’s AI journey initially focused on enhancing customer service responses to passenger inquiries.
During flight delays or cancellations, customer service representatives are tasked with providing swift, informative updates while maintaining a consistent brand voice, a practice refined through the company’s “Every Flight Has A Story” program. Sustaining this level of communication during prolonged disruptions presents a significant challenge.
Birnbaum explained the necessity of AI in such scenarios: “Considering the number of delays versus storytellers, we couldn’t have a person write a new message with every event. So we focused on prioritizing the most impactful situations. The data piece was simple: the basic facts of the flight and the running chat between the attendants, pilots, gate agents, and the operations people associated with the flight. We fed that information — with additional data on weather, for example — into the AI model, to generate a good draft customer message.”
The critical step involved teaching the AI to adopt United Airlines’ specific communication nuances and emphasis. This is where prompt engineering proved invaluable, not for training the model on flight data itself, but for guiding its word choice to align with United’s preferred lexicon. For instance, the airline can use AI to emphasize safety without causing undue alarm, and the model is learning to select appropriate language. Birnbaum noted, “The AI model was very good at looking back in time to bring previous flight data into the current situation. Even our human storytellers didn’t include reasons for flight delays, and that kind of information can be very useful to a customer.”
**The Strategic Imperative of AI in Aviation**
Industry analyses indicate that airlines are currently at an “average” stage of AI maturity, a notable improvement from the previous year. Boston Consulting Group’s research found that only one out of 36 surveyed airlines met the highest criteria for AI readiness. Projections suggest that by 2030, carriers that embed AI at the core of their operations could achieve operating margins that are 5% to 6% percentage points higher than their competitors.
Generative AI is poised to become integral to the operational framework of airlines and airports, supporting rapid decision-making in areas such as scheduling, crew allocation, aircraft rotation, and passenger recovery. Microsoft estimates that data-driven AI systems can reduce the root causes of flight delays by up to 35% through enhanced disruption forecasting, thereby mitigating the ripple effects of operational disruptions.
Furthermore, airlines employing AI-driven personalization are reporting revenue increases of approximately 10% to 15% per passenger. Microsoft also highlights that AI-powered tools, including self-service customer interfaces, can lead to cost reductions of up to 30%.
Original article, Author: Samuel Thompson. If you wish to reprint this article, please indicate the source:https://aicnbc.com/16615.html
