AI in aviation and airlines : recent reports and key findings

Kristjan Jansons
August 1st, 2021

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plane in the sky

The revenue of commercial airlines worldwide is predicted to reach $471 billion in 2021. In this highly competitive industry, the corporations with the highest technological advancement will prevail


Revenue of airlines predicted to reach $471 billion in 2021


AI in aviation is disrupting the way companies approach their data, operations, and revenue stream.

The world’s leading airlines are already using artificial intelligence in aviation to improve operational efficiency, avoid costly mistakes, and increase customer satisfaction.


There are many different areas where machine learning can empower the aviation industry. They can mainly be broken down into three categories:

  • Fleet & operations management

  • Customer service and retention

  • Autonomous machines and processes

  • How to bring AI to your business?

Fleet & operations management


Airlines and flight operators can significantly reduce their operational costs and overhead by optimizing their fleets and operations with AI-powered systems.


Potential areas for applying AI in aviation industry include:


  • Dynamic pricing – to maximize revenue, airlines are optimizing their base published fare that has already been calculated based on journey characteristics and broad segmentation, and further adjusting the fare after evaluating details about the travellers and current market conditions. Airline companies are using many different variables to determine the flight ticket prices: indicator whether the travel is during the holidays, the number of free seats in the plane, etc.
  • Pricing optimization – similarly to dynamic pricing, these algorithms are looking for ways to optimize the sales revenue in the longer term to ensure all flights are optimally booked.
  • Flight delay prediction – as flight delays are dependent on a huge number of factors, including weather conditions and what’s happening in other airports, an intelligent system can be applied to analyze huge data sets in real-time to predict delays and re-book customers’ flights in time.


Airline companies are using many different variables to determine the flight ticket prices.


  • Flight route optimization – think machine learning-enabled systems that can find optimal flight routes, leading to optimally timed and booked flights, lower operational costs and higher customer retention. For this use case, various route characteristics, such as flight efficiency, air navigation charges and expected level of congestion can be analyzed.
  • Avoiding travel disruption – Amadeus, one of the leading global distribution systems (GDS), has introduced a Schedule Recovery system, aiming to help airlines mitigate the risks of travel disruption.
  • Crew scheduling –  flying personnel of major U.S. carriers have grown and now often exceed $1.3 billion a year and are the second largest item (next to fuel cost) of the total operating cost of major U.S. carriers. what’s the optimal way to schedule an airline’s crew to maximize their time and increase employee retention?
  • Fraud detection – by analyzing specific customers’ flight and purchase patterns and coupling them with historic data, algorithms are able to point out suspicious credit card transactions and eliminate fraudulent cases, saving airline and travel companies millions of dollars every year. According to John McBride, director of product management for PROS, a software provider that works with airlines including Lufthansa, Emirates and Southwest, a number of operators have already introduced dynamic pricing on some ticket searches.
  • Machine learning can also benefit the air freight industry. For example, predictive models help to forecast whether a product will be shipped on time, and find the most optimal shipping routes. In addition, intelligent systems can help identify problematic incidents and solve them in time.

Customer service and retention

Enhanced customer experience is an area where both aviation and travel companies can strongly benefit.

Artificial intelligence can be applied to optimize pricing strategies, increase customer engagement, and improve the overall flight experience. Here’s a list of potential AI use cases for the travel industry:

  • Recommendation engines for tailored offers – behavior-tracking techniques, metadata, and purchase history allow making highly personalized offers to customers, increasing retention and a customer’s lifetime value.
  • Sentiment analysis on social media – when paired with intelligent algorithms, social media feedback can be used to evaluate customer reactions close to real-time, giving valuable insight for improving customer experience.
  • Chatbots and customer service automation – Kayak, a popular travel booking service, allows you to plan your next trip directly from your Facebook Messenger app. Their type of chatbot is human-like, understands simple questions and responds in a casual, conversational style.
  • Call Center automation – that allows to fully automate calls or semi-automate the process in the contact center by improving the efficiency of the agents.

Gartner predicts that as much as  25% of customer service and support operations will rely on the virtual assistant technology by 2018.

Facial recognition and biometrics pave the way to seamless airport security processes. A similar approach could be applied to track how people move across the airport, getting a better sense of the flow of travellers.

Autonomous machines and processes

While completely autonomous self-flying planes lie still in a distant future, there’s an opportunity to automate other types of airport processes, such as ground handling, loading, fueling, cleaning, and aircraft safety checks.

Airbus, one of the leading aerospace companies, is currently using AI to analyse data coming from various factories, predicting when variations in the manufacturing processes occur. This allows them to tackle the problems earlier, when it’s easier and less costly, or even prevent them completely.

How to bring AI to your business?

When working with companies in the aviation industry, we usually see a lot of low-hanging fruits for personalizing customer service and optimizing operations.

Before you take the first step to bring artificial intelligence into your company, we recommend that you consider the following questions:

  • What are the key areas where you’d like to see improvement? Is it in-flight optimization, customer service or some other department?
  • Are you sure that AI is the optimal solution to these problems?
  • Do you have the required data for the algorithms to learn from or do you need to first set up a data infrastructure?

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Konstantin Sadekov

Konstantin has graduated from the Estonian Business School major in economics and finance and is currently doing his MBA degree in the USA. Before joining MindTitan he had international business management experience for more than 5 years and overall more than 9 years of international B2B sales and marketing experience

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