AI in aviation and airlines

Artificial Intelligence changes the way business operates and interacts with its customers. In making business decisions and building workflows AI has a huge role to play. For example, real-time access to data enables businesses to take sophisticated steps towards operational efficiency. In the aviation industry, airlines and flight operators make use of AI solutions to reduce their operational costs and optimize their fleets and operations. Airlines use cognitive technologies to reach new heights. Digital transformation has led to the applications of AI in aviation and airlines. AI and its cognitive technologies that bring out the insights from data can smoothen and automate analytics, machinery maintenance, customer service, as well as many other internal processes and tasks. So, AI solutions are useful for various aspects of airline operation management. 

How are AI solutions being used in airlines around the world?

Revenue Management

Revenue management includes the process of defining how to sell a product to the required people at a rational cost at the right time via the right medium. The revenue management specialists of the aviation industry optimize AI in determining destinations and regulating prices for specific markets. AI also helps in identifying effective channels of distribution. 

Ancillary Price Optimization

Through analytics-driven pricing, the revenues of airlines are increased. It enables data scientists to recognize a traveler’s tendency to buy ancillaries like baggage. AI helps to determine in which markets and on what days people are likely to pay more to check their bags. 

Maintenance and Safety

Due to delays and cancellations, airlines endure high costs because it includes expenses on maintenance and compensations to passengers stuck in airports. AI helps technicians access real-time and historical data from any location. Through predictive analytics, if the employees get to know about the current technical condition of the aircraft, they can locate issues and replace parts proactively. Additionally, the executives and team leads can obtain updates on maintenance operations, get data on the tool and part inventory, and expenses via dashboards.

With the implementation of predictive analytics, an airline can reduce expenses connected with stimulated transportation of parts and unplanned maintenance. AI provides directions in real-time to the technicians in the field and the logistics team that supports them.

Feedback Analysis

AI is largely used by airlines for feedback analysis and market research because AI enables airlines to make proper decisions and meet customers’ expectations. 

AI solutions enable airlines to find ways to positively mediate in the passengers’ journey and turn a bad experience into an enjoyable one. It also enables companies to respond faster in an aligned way following the business’s values.

Passenger Interaction

The speed of response to passenger queries is important as actual steps are taken to solve an issue. If passengers don't get an immediate response to a problem they likely won’t select this airline for their next journey. AI solutions in such situations help in speeding up and simplifying customer service. This is done with the use of algorithms and natural language processing. AI chatbots and voice assistants are used to give real-time responses to passengers.

Crew Management

Crew management is a critical task as there are many legal restraints to it. Using AI predictive models that can collect data from various sources, employees can get insights into daily operations. AI helps the employees to make a favorable schedule related to working time, aircraft utilization, and expenses. Further, AI solutions can address risks, for example, if pilots are in danger because of frequent changes of weather, constant change of time zone, etc all these can be identified by AI. 

Optimal Amount of Fuel 

Airlines use AI solutions with built-in machine learning algorithms to gather and inspect flight data related to each route distance and altitudes, aircraft type and weight, weather, etc. Based on these findings from data, systems evaluate the favorable amount of fuel needed for a flight.