Advances in Artificial intelligence (AI) are reshaping the future of aviation. Application areas include crew management, flight maintenance, ticketing, and passenger identification, and they all center on one objective: improving the customer experience.
Artificial intelligence (AI), among the emerging technologies, remains at an initial stage within the aviation business. To date, we can see AI being implemented by airlines for facial recognition, customer Q&A, baggage check-in, factory operation optimization, and aircraft fuel optimization.
But, AI has far more potential than these use cases. It can fully revolutionize the way airlines do business.
Here are some cases that use Artificial intelligence (AI)
1. Crew Management
Every day airline crew managers have to manage complex networks of people, as well as flight attendants, pilots, and engineers. Rescheduling any of the crew members can be cumbersome. Multiple factors affect the decision of a manager, like availability, credibility, certifications, and qualifications of the crew member.
Jeppesen, a Boing company, has solved this problem using AI. Their AI-based crew rostering system considers all the aspects mentioned above and manages crew members with efficiency.
2. Flight Maintenance
Aircraft maintenance is a tough task, and if done incorrectly, it can cost a fortune to the airlines. It requires extensive planning and scheduling. Unplanned craft maintenance may result in flight delays or perhaps cancellation. experts predict that AI if implemented correctly, can save millions of dollars.
AI-based predictive maintenance is slowly becoming a trend in the global aircraft maintenance market. it’ll facilitate the maintenance engineers to predict failures before they really happen. Delta is planning to reduce its number of flight cancellations via AI-based predictive maintenance. according to IBM Watson’s TV commercial, AI also will guide the on-field repairing employees and tell them their action items.
3. Ticketing Systems
Air ticket costs are calculated based on multiple parameters. Such as oil prices, flight distance, purchase date, competition, seasonality, the brand value of the airline, and more. Some parameters change daily, such as oil costs, which lead to a continuous change in the ticket price.
The AI algorithm is the ultimate answer to this problem. it’ll facilitate airlines to calculate the most efficient costs for every flight. Which will help them remain profitable and provide competitive pricing to their customers?
4. Passenger Identification
Delta Airlines announced in May 2017, that they’re getting to invest $600,000 to make self-service bag drop machines and kiosks for passenger identification.
The kiosk will have an integrated camera that will take photos of the passenger at the time of check-in and match it with their passport. Both the face recognition and self-service baggage drop-off will leverage machine learning algorithms to perform their tasks.
5. Customer Service
United Airlines announced its collaboration with Amazon’s AI, Alexa, in Sep 2017. They designed a skill for Alexa named “United.” Once the customers add the United skill, they can ask any common question through voice commands, such as:
- “Alexa, ask United the status of flight 595.”
- “Alexa, ask United to check me in for my flight.”
- “Alexa, raise United if flight 675 has WiFi.”
Alexa’s natural language makes customers feel as if they’re talking to a human sales rep.
6. Simplify Communication
Air traffic control (ATC) is one of the most crucial aspects of all flights. in the case of international flights, the communication between a pilot and a traffic controller is usually cross-lingual and cross-cultural.
Although both of them use English for communication, their accent could be different, which can create confusion. For instance, it can be difficult for an Indian pilot to understand the heavily accented English of a European controller. Moreover, the communication channels of ATCs are noisy, which makes it more difficult for the pilot to follow.
Thanks to Airbus’s AI-Gym program, they have been able to develop a machine learning algorithm. That will not only clear the noise in real-time but also provide a full transcript of the controller’s audio.
