Introduction

It is routine to hear that competition fosters innovation. While the phrase is frequented by many, it continues to hold true in today’s technological landscape—and especially in the economic sense. Arguably one of the most operatively challenging and competitive industries, the Airline Industry carries this notion at its forefront. Proliferating in enterprise use, AI is thus becoming a cornerstone to airlines’ competitive strategies, allowing companies to necessarily optimize and further digitize themselves ahead of stringent competition.

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Airlines and AI Sentiment

Sita’s Air Transport IT Insights Report, with respondents who collectively represent the views of 93 airports, analyzed the emerging post-Covid aviation landscape and found that 76% of airlines and 68% of airports are planning major AI adoption programs, or R&D for AI, by 2025. This quantitatively high aim reflects this ambitious stride towards AI adoption; Senior Manager of AI Strategy at American Airlines confirms this as he notes, “A recent study showed that this is worth one to two percent of revenue. So, [for] a large airline with $50 billion of revenue, this could be worth a billion

dollars in productivity.”

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In fact, these benefits are already seen. For instance, Swiss International Air Lines saved 5 million CHF in 2022 through optimized flight operation models, and Lufthansa reported a 40% relativ improvement in accuracy in light of a wind pattern forecast model that solved the underperformance of capacity at Zurich Airport.

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However, with such strict factors to industry performance, the airline sector faces a net profit margin of 2.7%. This comes in view of the expected 4.7 billion travelers in 2024—a historic high exceeding 2019’s previous peak at 4.5 billion. With these record numbers, airlines are faced with upkeeping increased demand. However, capacity constraints, delays, cancellations, rerouting, and maintenance have become afflicting costs that impact the narrow value capture—ultimately necessitating AI’s adoption and cost-cutting capabilities.

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Most Common AI use-cases in Airlines: the Rise of Generative AI

Generative AI (GenAI) has found itself hosting a plethora of uses within aviation. As identified by IATA, the International Air Transport Association, during its Innovation Day last year, the following use-cases were at the forefront of discussion:

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- Maintenance, Repair, and Overhaul (MRO): complements aircraft manufacturers and service technicians in their work to streamline assembly and MRO processes with GenAI’s ability to create digital twins allowing for predictive maintenance with minimized costs;

- Customized Offers and Orders: aids airlines in creating personalized customer communications based on user data;

- Flight Route and Schedule Optimization: combines GenAI’s ability to provide natural language outputs with seasonal traffic, weather, and events to overall optimize travel routes and experiences.

- Dynamic Pricing and Revenue Management: comprehensive interpretation and support of ML analyses;

- Back-Office Customer Support: knowledge management via comprehensive LLM systems to aid in optimizing routine processes and aid as-needed;

- Disruption Management: enhances passenger experience through the provision of detailed information on strikes and delays, as well as offering personalized perks to enhance overall customer experience.

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Prospects of Generative AI in the Sector

Generative AI is argued to be transformative for the sector—enabling optimization and efficiency increases at its core. As aforementioned, GenAI’s creation capabilities are not only limited to customer-facing benefits, but in vital internal maintenance and testing practices.

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Such sentiments are echoed by industry participants themselves. Rishi Ranjan, CEO of a software provider for U.S. defense contractors in the aerospace industry, stated, “We strongly believe that generative AI will really start helping scale these things to the much bigger aerospace industry in the next two to 10 years”. Noting the importance of verticalizing the technology, he continued to explain that GenAI’s true use will be further propelled when firms integrate it with their own IP and thorough data sets.

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Undeniably, adherence to safety remains at the forefront of aviation. With the adoption of models increasingly becoming commonplace, the European Union Aviation Safety Agency (EASA) released the Artificial Intelligence Roadmap 2.0, setting the principles and framework to build upon the development of Trustworthy AI in the sector, in coordination with the prospective EU AI Act.

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Moving Forward: Validation and Risk Management

The indeterministic and occasionally unpredictable nature of Generative AI can give way to numerous risks; as a result of their unstructured nature, continuous validation of the model and its data application is vital to ensure technical, ethical, and regulatory risks are within an airline’s governance standards.

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As follows, a new method of validation and audit must be adopted in order to adapt to these growing

concerns across the sector.

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At Calvin, we look forward to guiding the potential of AI in this technical field - optimizing and alleviating the concerns of risk management through our modularized range of assessments amidst firms’ dynamic AI portfolios. With our quantitative, holistic approach, our goal is to guide your firm towards AI excellence, ensuring safety both on the internal and external stages.

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Interested in increasing your models’ fairness and explainability for your client base? Book a demo with us, and let us show you how we can enhance your AI systems' effectiveness and trustworthiness in today’s ever-changing landscape.