How generative AI could transform the aviation industry

The airline industry is under pressure to improve operational efficiency while improving the sustainability of air travel in an increasingly complex market still recovering from the impact of the COVID-19 pandemic. In an industry where safety is paramount and new technologies require rigorous scrutiny, generative AI promises to strengthen aviation businesses and their industry partners.

There are countless potential use cases for generative AI. While some use cases require time to integrate with existing business systems and processes, industry leaders must move forward in areas that best suit the strengths of generative AI. This includes improving customer experience through better personalized travel recommendations and promotions, as well as improving customer service by creating more powerful virtual assistants. Generative AI can also help maintenance, repair and overhaul (MRO) technicians more effectively retrieve relevant information for repairs or automate the creation of parts and equipment orders so repairs or maintenance can begin as soon as an airplane lands. You can help by doing this.

Some airlines are already using generative AI in their communications and customer service operations, including automating text translation, creating marketing materials, and writing copy. AAR Corp, a civil aviation service provider, is considering using generative AI to optimize inventory management, provide predictive maintenance, improve warehouse operations, and automate parts ordering.

A thoughtful first step toward safe adoption of generative AI

The aviation industry is taking a cautious approach to AI adoption. Last May, the European Union Aviation Safety Agency (EASA) published the second version of its AI Roadmap, which provides a comprehensive plan for integrating AI into aviation, focusing on safety, security, AI assurance, human factors and ethical considerations. I did. .

These actions indicate that the industry recognizes the risks of AI. This includes biases in the data used by machine learning systems to train AI models, which can skew recommendations or analysis. Generative AI can also sometimes identify patterns or objects in the data that don’t exist, producing meaningless or downright inaccurate results. These phenomena are called “hallucinations.”

Because of the fallibility of generative AI, airlines should start by exploring use cases where some variability can be tolerated, such as customer service bots. You also need to ensure that customer data is secure and that data use complies with data privacy regulations.

More advanced generative AI use cases

With a focus on safety, data integrity, and security, the industry can move forward with testing more advanced generative AI implementations. Aviation companies will realize the value of the technology in a variety of ways, integrating generative AI with existing intellectual property and systems.

• Personalized pilot training: Generative AI can take current training simulators to a new level of realism, modifying training scenarios to suit each pilot’s performance.
• Optimize flight routes and schedules: The technology can also merge data from historical and real-time flights, weather patterns and air traffic to find the most fuel- and time-efficient routes and schedules to minimize delays and fuel consumption.
• Predictive maintenance support: Generative AI can analyze data from sensors and other sources and compare it to historical data to predict potential failures and optimize maintenance schedules.
• Create a digital twin: Aircraft designers and MRO companies often create virtual models of their products or systems. These digital twins allow you to test designs faster and improve performance without having to build a physical model. Generative AI can build digital twins much faster than current methods.
• Airport Planning and Optimization: Complex digital twins of existing or new airports allow designers and planners to simulate and enhance passenger flows and aircraft ground traffic, as well as building infrastructure and amenities.

These examples are just scratching the surface of the possibilities, as a variety of players in the aviation industry can leverage generative AI to help with aircraft design and prototyping, supply chain optimization, advanced flight planning, and more.

Ultimately, airlines must find a middle ground between innovation and caution on their path to becoming industry leaders in AI. With so many use cases, generative AI is poised to transform every aspect of the industry.

Learn how to find and seize generative AI opportunities View IBM solutions on the Travel and Transportation page