AI in aviation and airlines: Use cases relevant for 2024

The revenue of commercial airlines worldwide is predicted to recover in 2023, according to the trade organization International Air Transport Association (IATA). Airlines’ financial losses are expected to contract to $12 billion in 2022 compared with $52 billion in 2021.

Although recovery was already present in the recent years it had been slow due to ongoing border restrictions. And it seems Artificial intelligence for aviation and airlines is the one crucial element that actually helps to improve the situation.

But how can you actually implement AI in aviation and airlines and what actually works? Well, we are going to talk about this in this article.

With increasing vaccinations and better pandemic management this year, IATA expects the aviation industry to recover in all regions, with North America actually turning in profits for the first time since the pandemic.

An important metric in the industry, the revenue passenger kilometers (RPK, or the number of kilometers paid by customers), is estimated to have improved 18% in 2021 and is forecast to improve 51% this year. This corresponds to about 61% of pre-pandemic RPK.

As the aviation sector bounces back, competition is bound to intensify as airlines take advantage of customers eager to travel after nearly two years of lockdowns. Firms that innovate and incorporate new technologies will be the clear winners.

In particular, the use of artificial intelligence (AI) is fast becoming a game-changer in the industry.

AI in aviation

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. These areas can be broken down into four main categories:

Customer service & retention
Artificial intelligence in fleet & operations management
Air traffic control & air traffic management
Autonomous machines & processes

Customer service and retention

Aside from predictive maintenance and increased efficiencies, enhanced customer experience and customer satisfaction are areas where AI in aviation is breaking new ground.

Artificial intelligence can be applied to optimize pricing strategies, increase customer satisfaction and 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 can create highly personalized offers, increasing customer retention and lifetime value.
Sentiment analysis on social media – when paired with intelligent algorithms, social media feedback can evaluate customer reactions close to real-time, giving valuable insights for improving customer experience.
Chatbot software and customer service automation – Kayak, a popular travel booking service, allows flight planing for your next trip directly from your Facebook Messenger app. Their type of chatbot is humanlike, understands simple questions, and responds in a casual, conversational style.
Conversational IVR – that allows to fully automate calls or semi-automate the process in contact centers by improving the agents’ efficiency.

According to research firm Gartner‘s “Emerging Technologies and Trends Impact Radar for 2021” report, advanced virtual assistants (AVA) are the next big step from today’s chatbots. AVAs will be powered by NLP solution, resulting in conversational and intuitive sessions, and semantic and deep learning techniques such as deep neural networks (DNNs).

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

Artificial intelligence in fleet & operations management

Aviation companies and flight operators can significantly lower operating 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 according to passenger journey, flight path, and broad segmentation. Fares are further adjusted after evaluating details about the customers and current market conditions. Airline companies use many different variables to determine flight ticket prices: whether the travel is during the holidays, the number of free seats on the plane, etc. According to John McBride, director of product management for PROS, a software provider that works with airlines including Lufthansa, Emirates, and Southwest, some operators have already introduced dynamic pricing on some ticket searches.
Pricing optimization – also known as airline revenue management, this is similar to dynamic pricing. Machine learning algorithms look for ways to maximize sales revenue in the longer term to ensure all flights are optimally booked. These include historical data analytics such as past bookings, flight distance, willingness to pay, etc.
Flight delay prediction – as flight delays are dependent on a huge number of factors, including weather conditions and what’s happening in other airports, predictive analytics and technology can be applied to analyze massive real-time data to predict flight delays, update departure time, and re-book customers’ flights on time.

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

Flight route optimization – is done through machine learning-enabled systems that can find optimal flight routes, save money through lower operational costs, and result in higher customer retention. For this use case, various route characteristics, such as flight efficiency, air navigation charges, fuel consumption, and expected congestion level, can be analyzed.
Avoiding travel disruption – Amadeus, one of the leading global distribution systems (GDS), has introduced a Schedule Recovery system to help airlines mitigate the risks of travel disruption and flight delays.
Crew scheduling – the labor costs of the crew members and flight attendants of major U.S. aircraft have grown (often exceeding $1.3 billion a year) and are the second-largest item (next to fuel cost) of the total operating cost of major airlines. Big data analysis can find the best 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 historical data, algorithms are able to identify passengers with suspicious credit card transactions and eliminate fraudulent cases, saving airline and travel companies millions of dollars every year.

Machine learning can also benefit the air freight industry. For example, predictive models help forecast whether a product will be shipped on time and find the most optimal routes. In addition, intelligent systems can help identify problematic incidents and increase operational efficiency.

Air traffic control and air traffic management (ATM)

The increasing benefits of AI in aviation extend to critical tasks such as air traffic management. Machine learning is not meant to replace human air traffic controllers. Instead, it aims to automate repetitive, predictive tasks to free up human employees to focus on more complex and important tasks.

In August 2021, the UK government approved a £3-million budget in partnership with The Alan Turing Institute (UK’s data science research organization) and NATS (National Air Traffic Services) to implement live trials of the first-ever AI system in airspace control called Project Bluebird.

Assembling an interdisciplinary team of data scientists, engineers, and mathematicians, Project Bluebird aims to study how AI systems can work side-by-side with humans to create an ATM that is intuitive, sustainable, and risk-free.

In this project, machine learning algorithms and data science are used to recommend collaborative actions with air traffic control teams, including tackling climate change policies such as achieving net-zero carbon emissions by 2050 through better routing and lower fuel consumption.

Autonomous machines and processes

While completely self-flying planes still lie in the distant future, there are already studies from both Airbus and Boeing to drive autonomous aircraft forward. In December 2020, Boeing completed its test flights of five uncrewed aircraft using AI algorithms, which reached speeds of 270 kilometers per hour. The company is confident that this successful test run will propel autonomous technology to the forefront in the coming years. Meanwhile, 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, uses AI to analyze 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 altogether. Predictive maintenance will also help the airline industry and aircraft manufacturers save money in the long term as there would be fewer parts replacements and overhauls.

Generative AI in aviation

Generative AI is reshaping the aviation industry, offering transformative potential across a spectrum of operations, from customer service to technical maintenance. Here’s a straightforward breakdown of what this technology means for business leaders in aviation, its benefits, and the challenges it presents.

Understanding Generative AI in Aviation

Generative AI refers to advanced algorithms capable of generating content, from text to simulations, that have been trained on vast datasets. In aviation, these capabilities translate into a variety of practical applications that can enhance efficiency, reduce operational costs, and improve the overall passenger experience.

Key Benefits of Generative AI

Enhanced Operational Efficiency: AI chatbots and virtual assistants manage routine inquiries, significantly reducing the need for large customer support teams. This allows airlines to allocate resources more strategically and focus on more complex service issues.
Personalization at Scale: Through data analysis, generative AI tailors services and recommendations to individual customer preferences, which can enhance the travel experience and increase revenue through targeted upselling.
Multilingual Communication: AI-driven tools break language barriers, offering multilingual support to facilitate seamless communication with passengers from diverse linguistic backgrounds.
Real-time Information Dissemination: AI systems provide passengers with relevant information, such as up-to-the-minute updates about a flight status, , thus enhancing customer satisfaction and reducing personnel workload.

Applications of Generative AI

Travel and Booking Assistance: From handling bookings to managing loyalty programs, AI automates and personalizes interactions, making processes more efficient.
Operational Support: AI assists in predictive maintenance and inventory management, helping airlines minimize downtime and optimize stock levels.
Advanced Simulations: For training purposes, AI can create realistic scenarios tailored to individual pilot needs, enhancing training outcomes without physical constraints.
Document Navigation: Generative AI can act as a sophisticated search engine, swiftly navigating through extensive technical documents and manuals to retrieve and contextualize critical information, thereby boosting decision-making efficiency and accuracy.

Challenges in Implementation

Despite these benefits, generative AI poses challenges that need careful management:

Data Security and Privacy: Since AI systems process significant amounts of personal data, ensuring privacy and securing data against breaches is critical.
Accuracy and Reliability: Since AI’s effectiveness depends on the quality of the data it learns from, inaccurate or biased data can lead to unreliable outputs, potentially compromising decision-making processes.
Integration Complexity: Merging AI with existing systems may require substantial changes to current infrastructures and processes.
Regulatory and Ethical Concerns: AI technologies are racing past the regulatory frameworks that govern their use, necessitating ongoing compliance efforts.
Cultural Impact: The human factor must also be considered. Cultural reactions to the automation of tasks previously done by people are hard to predict.

Strategic Adoption of Generative AI

To determine if generative AI integration is suitable for your specific needs, we recommend a systematic approach:

Proof-of-concept: Implement AI in a controlled environment to assess its impact and effectiveness.
Evaluate and Adapt: Consider the feasibility of integrating AI with your existing systems and whether adjustments are needed to optimize performance.
Risk Assessment: Understand the potential for errors and determine the tolerability of these risks in your operational context.

Generative AI presents a revolutionary tool for the aviation industry, promising substantial gains in efficiency and customer service. However, it requires a balanced approach to leverage its benefits while fully mitigating associated risks. By carefully evaluating its applications and integrating them thoughtfully, aviation leaders can harness the power of AI to set new standards in airline operations and passenger service.

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 set up a data infrastructure first?

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AI in aviation and airlines: Use Cases for 2024