Reshaping the Aviation Industry with AI

For the past few years, airline companies have been facing growing uncertainty, public scrutiny and ever-increasing competition and commoditization. Despite all of these challenges, experts predict that the aviation industry may be entering into its fourth industrial revolution. Emerging technologies such as AI, machine learning, and IoT are reshaping the aviation business from the inside out.

High-Value Use Cases for the Aviation Industry

Ticket pricing optimization: Flight ticket prices are based on multiple factors such as oil prices, flight distance, time of purchase, competition, seasonality, and more. Some of these parameters change daily, which means that companies need to continuously adapt ticket prices to these changes. Thanks to AI and ML, companies can not only analyze past data but also predict the demand based on multiple indicators. In addition, they can increase sales revenue long-term by incorporating a more balanced flight booking system.

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Crew management: Airline crew managers have to manage complex employee networks, including pilots, flight attendants, engineers, etc. A number of factors affect day-to-day crew management, such as availability, credibility, certifications, and qualifications. Rescheduling any of the crew members can be a cumbersome task. However, by implementing an AI-based crew rostering system, airlines can optimize and partially automate the process, thus reducing costs and errors, and leveraging crew members’ full potential.

Customer service: By using AI, companies can optimize their operational and labor costs at the same time. AI-based tools can provide information on future flights, assist with check-in requests, and resolve basic customer queries.

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AI-based predictive maintenance: Aircraft maintenance is a tough task, and if done incorrectly, it can cost a fortune. Thanks to AI and machine learning, companies can now predict potential failures of maintenance on aircraft before they actually happen with higher accuracy. The use of AI with predictive maintenance analytics can lead to a systematic approach on how and when aircraft maintenance should be completed. Nowadays, airline maintenance teams are dealing with huge amounts of data produced by newer aircrafts and the necessity to generate quick insights and implement accurate predictive models. Centralized data platforms could facilitate better and more efficient data governance and effectively manage model lifecycles.

Data governance and self-service analytics (SSA): With today’s increasing concerns around data privacy, the role of data governance in the realm of self-serve data efforts is an important one. Aviation companies often process private information of its customers which need to stay protected. Centralized data platforms allow to monitor and control all modifications, and access to data which makes the task of working with big amounts of data more manageable for the personnel but also provides higher security for the customers.

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