Most major airlines have been early adopters of data and analytics aimed at a variety of use cases such as optimizing of operations, revenues, and costs. Many airlines have increasingly transitioned to using artificial intelligence (AI) techniques, including machine learning, in furthering these use cases. Not only has this transition resulted in meaningfully improved benefits for airlines, but it has also proven increasingly beneficial for all stakeholders, including consumers, airports, and regulators. However, airlines that have not pursued the adoption of AI innovations aggressively enough are risking a loss of competitive positioning that could affect their respective long-term credit profiles.

This is the second of a multi-part series on the impact of artificial intelligence on corporates and customers, and the associated impact on the credit risk of issuers.

Key Concept #1: Traditional Challenges — More Effective Solutions

Airlines have always faced some perennial challenges related to maximizing revenues, expense management, fuel use optimization, and maintenance issues. Many have transitioned to applying AI solutions to these problems and have achieved meaningfully increased benefits.

Revenue Management
Revenue management for airlines essentially involves forecasting demand for each flight, estimating the price a passenger would be willing to pay and accordingly setting optimal prices for each booking class. AI-powered applications have been proven superior compared with traditional statistical methods. While the latter capably incorporates historical demand patterns, AI techniques have enabled a significant expansion on this approach through their capacity to incorporate additional data parameters such as multiple booking classes; high data volume; complexity of travel networks; different passenger types; varying reward systems; and differentiated product offerings such as extra baggage or seating types, social media feeds, upcoming weather forecasts, and real-time events. These applications provide for highly efficient pricing and demand forecasting algorithms on a level not previously attainable, which is translating into advanced revenue maximization. Airlines such as Lufthansa and Singapore Airlines use these applications for continuously pricing airfares.

Air Safety and Maintenance
Airlines are typically responsible for all costs related to maintaining aircrafts and also for any delays or cancellations caused due to maintenance or safety issues. Furthermore, delays or cancellations typically affect the entire network as passengers need to be rebooked on later flights, which can cause congestion and/or lost revenues in real time and loss of reputation in the longer-term. Given the critical importance of such issues, many airlines have gravitated towards applying AI-powered solutions for predictive maintenance with positive results. A good example is Air France-KLM, which has partnered with Donecle (a French aircraft manufacturer) to use automated drones with advanced image analysis and machine learning algorithms for detecting defects in aircrafts.

Expenses and Fuel Management
Labour and fuel tend to be the major operating expenses for airlines. Many airlines are relying on AI-driven applications to manage fuel burn. For example, airlines like Air France, Alaska Airlines, Norwegian, Go Air, Indigo Airlines, and Malaysia Airlines are using SkyBreathe, an AI-powered fuel-efficiency solution that helps in deriving fuel related savings. Similarly, many airlines, such as American Airlines and Abu Dhabi’s Etihad Airways, are also leveraging AI to manage crew scheduling and availability. This allows them to comply with legal and labour requirements and manage network disruptions in the most cost-effective and efficient manner.

Key Concept #2: Data is King

Airlines are progressively collecting higher volumes of data from increasingly diverse sources. Owing to easy availability of greater computational power, this data is processed and used to train AI solutions at a rapid rate. A good example of this is Southwest Airlines. The Company leverages AI applications trained on its historical aircraft servicing data to increasingly improve the efficacy of its maintenance operations. Overall, data is the fuel for AI powered applications and increased availability of data leads to better applications and, in turn, improved outcomes

Key Concept #3: AI Never Sleeps — Continuous Improvement Is a Game-Changing Feature

Trained AI solutions not only beat traditional methods but also keep incrementally improving. Increased data capture and availability enables better depth and breadth of data for training AI-powered applications. This leads to continuously improving AI-applications which produce superior outputs in real time. For example, Scandinavian Airlines (SAS) uses Microsoft Azure Machine Learning-based models to continuously improve everyday operations and cut down fraud in its EuroBonus loyalty program. Similarly, Delta has partnered with Airbus to make use of the Skywise Core Platform and Skywise Predictive Maintenance Application to regularly improve aircraft reliability. Delta will the use the application for its A320 and A330 aircrafts to track and analyse their operations and performance data and assess failure probabilities in order to anticipate maintenance tasks. Others, like Etihad Airways, are leaning on AI applications to minimize food wastage on flights. At Etihad, economy-class meals are tracked using automatic digital photography, image recognition, and machine learning. The results are used to track constantly changing consumer preferences, persistently reduce food waste, improve meal planning, and lower operating costs.

Key Concept #4: For Customers, Ignorance is Bliss While Benefits Accrue

Accelerated adoption of AI powered applications by airlines has also benefited customers. AI-powered applications have led to travel personalization, marketing offers, improved customer service, and an improved travel experience overall. For example, numerous airlines have resorted to AI-powered chatbots to provide 24/7 multilingual support, assist travelers with booking and managing flights, track baggage, conduct check-ins, answer questions, and provide additional assistance. Airlines like United Airlines have even deployed AI-powered virtual assistants to further provide customer service by phone. Other airlines, including Singapore Airlines, are leveraging AI to personalize marketing communications before, during, and after the traveller’s trip and to also promote sale of ancillary products and services. Some Airlines like Delta are using AI to offer facial recognition services for travelers. Passengers who have opted for this service remain unfazed by privacy issues and use it to do everything from bag check-in to security and boarding of domestic flights in the U.S. Overall, even as customers are not fully aware of the extent that AI technology has permeated the airline industry, they are increasingly benefitting from the trickle-down of efficiency and cost savings, and are also receiving improved service

Key Concept #5: Leveraging AI Has Become Table Stakes

Airline operations tend to be complex; involve multiple stakeholders; require compliance with cross-border legal requirements; and need to be delivered in a safe, time critical, and cost-efficient manner. It is nearly impossible for any airline to achieve all of these targets without leveraging technology and data with modern processes. Airlines that do not invest sufficiently in data and AI may suffer market share losses. A case in point is Southwest Airlines’ winter 2022 scheduling crisis. Due to the failure of the airline’s dated crew-scheduling system, thousands of passengers were left stranded, and more than 16,000 flights were cancelled. The whole crisis cost Southwest more than a billion dollars in lost revenue and compensation payments, disrupted holiday plans for many customers, and caused significant
reputational damage. Eventually, the company’s COO publicly apologized at a U.S. Senate hearing and committed to investing more in technology and making additional operational changes. This incident serves to highlight that advanced AI technology is sine qua non for airlines to achieve their operational and financial goals. Deficiencies could lead to meaningful operating and financial deterioration over time and, ultimately, ratings could be affected.

Source : DBRS Morningstar