5 Ways Airline Industry Improve Business Operations Using Artificial Intelligence

Ways Airline Industry Improve Business Operations Using Artificial Intelligence

Technology has completely transformed the way business enterprises interact with their customers, make business decisions, and build workflows. For example, activities like booking a flight over phone or conducting offline surveys seem strange these days. Real-time data access allows business organizations to take proper steps towards operational efficiency.

How AI Is Used in Aviation?

Artificial Intelligence is managing flight operations smoothly and transforming the commercial Aviation industry in a huge way. The most popular Airline companies are implementing AI and other innovative technologies in collaboration with Mobile app development company to offer personalized services and boost customer experience. Self-service kiosks are automating operations and security checks at the airport. Going forward, AI will play a significant role in the Airline industry. The airline industry integrates AI with Machine Learning, Robotics, Computer Vision, and Natural Language Processing.

Successful AI Applications in Aviation

1. Revenue Management

Revenue management is the methodology that is aimed to describe how to sell a product to those who require it the most, at a reasonable price at the right time and using the right channel. It’s suggested that customers perceive product value differently hence the price they are ready to pay for it depends on target groups. The specialists make good use of AI to define destinations and manage prices for particular markets, find proper distribution channels, and manage seats for airline to stay competitive and customer-friendly.

2. Passenger Identification

Many renowned airports have implemented AI to identify possible threats at major airports. Similarly a number of several airport plans to launch the biometric terminal. AI-enabled tools help to boost the passenger identification method. Security scanners, biometric identification and Machine learning tools, in collaboration with Travel app development will ease operations at the ground staff level.

3. Air Safety and Airplane Maintenance

Airlines bear high costs all because of delays and cancellations comprising expenses on maintenance and compensations. With around 40 percent of the total delay time caused by unplanned maintenance, predictive analytics applied to fleet technical support. The implementation of predictive maintenance solutions helps for better data management aircraft health monitoring sensors. Usually, these solutions are compatible with both desktop and mobile devices, offering technicians access to real-time and historical data. With applied predictive maintenance, an airline can lower down the expenses connected with overtime compensation for crews, and unplanned maintenance. If a technical problem happens, maintenance teams could react faster with workflow software.

4. Robotics for Data Delivery

Duty-free stores engage travelers thanks to low prices. But it is not appropriate to risk public safety all because of the shopping. Store owners know the importance of implementing new norms to implement social distancing. For instance, Dubai Duty-Free stores resume operations posts the lockdown with the help of concierge service to fill the cart. Robots helped to deliver the carts to the customers. The advantages of AI in the travel industry post COVID-19 are evident from such use cases. In other locations, customers are taking full benefit of Chatbot development service for desired business results. Even before arriving at the airport, travelers can order items waiting for them when they board-off the plane.

5. Optimized Routes

A number of long-duration flights usually have a mid-range landing spot, where passengers have to undergo security process to check-in to a new flight. In layman terms, it is termed as a layover. The process is quite annoying from a traveler perspective, forces human-human contact, and increases the risk of infection. One of the benefits of implementing AI over here in the post COVID world is that it can re-route and manage long-duration flights. Till such time when the carriers reach full-capacity the shortest routes can be recommended by AI saving fuel and other capital-intensive resources.

Wrapping Up

The integration of AI in the world of Airlines in the post COVID world is about to happen. The situation is quite far when airports start bustling with people rush and packed closed to each other at the departures. Artificial Intelligence when integrated with Augmented reality app development will help airlines to engage travelers with proper security. Inclusion of AI will attract business-interest not only of the airports, but hospitality sector as well be it hotels, restaurants, or mobile food vans. With arguably the most talented developers under one roof, Mobibiz is your perfect technological partner.

Frequently Asked Questions


1. How is AI used in Aviation Industry?

The aviation industry leverages AI with Machine Learning, Computer Vision, Robotics, and Natural Language Processing. Key benefits include – Predictive Maintenance, Pattern Recognition, Auto-Scheduling, Targeted Advertising, and Customer Feedback analysis to improve overall customer experience.

2. How do you implement AI?

Seven key steps to implementing AI in your business

Step 1: Understand the difference between AI and ML.
Step 2: Define your business needs.
Step 3: Prioritize the main driver(s) of value.
Step 4: Evaluate your internal capabilities.
Step 5: Consider consulting a domain specialist.
Step 6: Prepare your data.

3. How can we improve artificial intelligence?

Here are some strategies that would aid in the effectiveness of your AI deployment:
• Combine machine learning automation & human evaluation with your data.
• Marry the data research efforts with project management best practices.
• Develop a flexible development methodology.
• Centralize your AI and ML data.

4. How does AI work?

AI combines data with rapid, iterative processing and smart algorithms, enabling the application to take decision automatically using the data patterns or features. It strives to facilitate human-like interactions with machines.

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