Key Takeaways:
- Generative AI is gaining real traction in the aviation industry
- Customer insights can inform better service and experiences
- Maintenance, flight paths and operations can be optimized
- Data analysis can maximize revenue generation
7 Key Advantages of Generative AI for Airline Operations
It may not seem like the most natural candidate for transformation, but generative AI is having a real positive effect in aviation, from enhanced customer experience through employee productivity to business operations.
Almost all airline companies - 97% - are now planning to develop Generative AI, along with more than 70% of airports. This blog will explore some of the biggest advantages that generative AI is delivering for aviation.
You may also be interested in reading our blog on: 12 steps to getting started with GenAI
Understanding Generative AI in Airlines
Generative AI could not have arrived at a better time for an aviation industry dealing with a range of pressing challenges.
According to official data from regulators, complaints against airlines post-pandemic have reached record levels in several countries, underlining the need to improve customer service. Behind the scenes, pressures on profitability and high competition mean that airlines have increasingly turned to GenAI to inform their sales and marketing strategies, improve staff skills, and even help them optimize their routes and schedules.
Advantages of Generative AI in the Travel Industry
1: Personalized Customer Experiences
Generative AI can be instrumental in delivering the personalized service and experiences that customers are increasingly expecting.
For example, GenAI-driven chatbots and virtual assistants can deliver updates on flight information, scheduling and gate changes in real time, so that customers always feel in control and fully informed. The same technology can extend to customer service centers, where more customers can get resolutions to queries faster, even when staff are at full capacity.
Furthermore, the service touchpoints that customers engage with can also be tailored to individual customers or demographics, based on analysis of travel and behavioral data.
2: Operational & Asset Utilisation Efficiency
Generative AI can drive operational improvements in many different areas, such as streamlined scheduling, ground handling, onboard catering, and asset optimisation. easyJet, one of Europe’s biggest airlines, has already used the technology to inform its strategies around predictive maintenance and flight schedule optimization.
Analysis of vast amounts of data and flight schedules enables better coordination of all of the related services on the ground, including refueling, maintenance, resupplying catering, and even using photo recognition to reconcile baggage and predict baggage loads for future flights.
3: Disruption Management
Customer frustration and lost revenue quickly mount up when unexpected disruption occurs. Generative AI can help smooth out the problems caused by disruption, informing the redesign of disruption management processes across crew planning, aircraft planning, and alternative travel for passengers.
GenAI algorithms can make the optimization of crew rosters automated, accounting for variables such as rest periods, availability and training commitments. AI can also be combined with machine learning to make sense of unstructured data and improve forecasting, helping further mitigate disruption proactively.
Why not read our guide to practical generative AI examples.
4: Loyalty Program Enhancement
Loyalty programs have a huge part to play in building customer satisfaction and trust, in a sector where customers can easily shop around to find better deals, and where members of loyalty schemes are typically high spenders.
Generative AI can support hyper-personalization, predicting which customers are most likely to respond to particular offers, and ensure that they’re targeted with them. According to Oliver Wyman, 63% of elite loyalty members would select a booking channel based on its GenAI capabilities, compared with 44% of travelers who aren’t members of any loyalty scheme.
5: Predictive Maintenance & Scenario Planning
Aircraft generate huge quantities of data every single second that they’re in the air. Being able to analyze this data in detail can help predict maintenance needs before breakdowns can cause downtime and disruption.
Beyond just predicting failures, generative AI can also support scenario planning, enabling organizations to anticipate various future conditions—such as fluctuating technician availability or unexpected equipment failures—and plan accordingly.
For instance, generative AI can help inform more efficient use of maintenance personnel time, from rationalizing their schedules against aircraft availability, to matching them to material planning and inventory control. This proactive approach not only addresses current needs but also helps companies prepare for potential future challenges, such as the predicted shortage of 70,000 aircraft technicians worldwide by 2033.
6: Flight Path & Fuel Optimization
Generative AI can also support optimization of aircraft during their flights, as well as before and after. Detailed analysis of weather patterns, other air traffic and a range of variables can inform the most efficient flight paths, which helps save fuel, cut costs and reduce the large emissions burden the industry faces.
These insights don’t have to be reactive: analysis of data in real-time can help pilots make changes during flights to avoid bad weather or other disruption, and minimize delays for passengers.
7: Revenue Management & Ancillary Revenue
With passenger experience demands increasing all the time, AI can help improve the in-flight service through tailored entertainment, meals and real-time travel updates.
Additionally, machine learning algorithms in revenue management systems that look for patterns in customers’ data can support greater sales of ancillary services, from WiFi services to seat selection and additional baggage allowances. These processes can also take internal and external influences into account, such as wider economic conditions, to enable dynamic pricing that can help maximize revenue.
In Summary: Using GenAI to Navigate Key Airline Challenges and Considerations
As with any artificial intelligence technology, there are a number of challenges that airlines will have to navigate in order to make a GenAI deployment a success.
Good GenAI relies on large volumes of available, good quality data; integration with existing systems; staff training and skills; and use of the technology that is both ethical and safe. This is especially important in aviation, where the complexity of aircraft systems and the sensitivity of customer data have to be factored in, without disrupting existing activities.
This is where the help of an expert partner, experienced in generative AI deployments for the aviation industry, can be so invaluable. Not only can they address all these issues along the way, but they can also help keep costs under control to maximize return on investment. Get in touch with the Ciklum team today to explore our approach to airline GenAI in more detail.
