How three pillars of AI are revolutionising the aviation sector

In 2013 when I designed the first hybrid cloud computing and first Big Data sentiment analytics in the global aviation industry, it seemed significant at the time and an innovation that would have lasted for a decade. Only few years later phenomenon of artificial intelligence, AI, came to surface and has penetrated every part of the business and aviation in an important part of the AI revolution.

AI predominately depends on three pillars which are data, algorithm, and development, which when combined improve human’s capabilities in several areas. AI helps by analysing accurately, forecasting continuously, and expediting decisions. These seem to be some of the key areas that aviation industry lacks, especially post the pandemic.

Let us look at few of the key initiatives that AI can contribute to the industry. Maximising revenues, airlines are optimising 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.

Furthermore, the price optimisation like dynamic pricing which uses these algorithms are looking for ways to optimise the sales revenue in the longer run to ensure all flights are optimally booked.

Flight delays are dependent on a huge number of factors, including weather conditions and what is happening on other airports. An intelligent system can be applied to analyse huge data sets in real time to predict delays and re-book customers’ flights in time.

Machine learning-enabled systems can find optimal flight routes, leading to optimally timed and booked flights, lower operational costs, and higher customer retention. Various route characteristics, such as flight efficiency, air navigation charges and expected level of congestion can be analysed.

Flying personnel of major US 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 US carriers. AI has an optimal way to schedule an airline’s crew to maximise their time and increase employee retention.

AI, ML can significantly contribute by analysing specific customers’ flight and purchase patterns and coupling it with historic data. Algorithms can point out suspicious credit card transaction and eliminate fraudulent cases, saving airline and travel companies millions of dollars every year.

Artificial Intelligence can also be applied to optimise pricing strategies, increase customer engagement, and improve the overall flight experience. AI recommends engines for tailored offers by behaviour-tracking techniques, metadata, and purchase history making highly personalised offers to customers, increasing retention and a customer’s lifetime value.

As I mentioned above our first Big Data Sentiment analysis on social media, now can be 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.

Another important implementation of AI is in chatbots and customer service automation which allows you to plan your next trip for example, directly from your Facebook Messenger app. This type of chatbot is human-like, understands simple questions and responds in a casual, conversational style.

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

Finally, a quote to remember:

“Understanding the world around you without understanding Artificial Intelligence will no longer be possible. Get involved on time, this is your chance.”

Key takeaways:

• AI predominately depends on three pillars which are data, algorithm, and development.
• Machine learning-enabled systems can find optimal flight routes, leading to lower operational costs.
• AI helps by analysing accurately, forecasting continuously, and expediting decisions.