Over the next five years, the global Artificial Intelligence in Aviation market is expected to grow from 120.1 (USD Million) in 2019 to 2,357.5 (USD Million) by 2026, with a CAGR of nearly 47%.
On the basis of global and regional level, the report provides valuation and analysis of the Artificial Intelligence in Aviation market. In-depth analysis of industry competition, limitations, sales estimates, potentials, current and emerging trends, and industry-validated market data are all part of this study’s findings and conclusions. Using data from 2016 to 2019, the report makes predictions for the years 2020 to 2026. (USD Million).
Growth Dynamics of Artificial Intelligence In Aviation Market
The use of big data in the aviation industry has contributed to the growth of artificial intelligence in that industry over the course of the evaluation period. This is further influenced by the increase in terrorist attacks and bombings, which has resulted in increased bag-monitoring of travellers. Artificial intelligence in the aviation market is expected to grow in size over the next few years due to the growing need for passenger identification, identifying identity thefts, and increasing the efficiency of aircraft operations. As a result, the aviation industry will see an increase in the use of artificial intelligence over the assessment period.
It is also possible to improve the efficiency of pilots by reducing monotonous tasks, accurately forecasting weather and providing information about aircraft locations to passengers on board through the use of AI-enabled virtual assistants for airlines. All these aforementioned aspects will steer the surge of artificial intelligence in aviation market over the predicted timeframe. In the near future, AI will help to increase market profitability by improving customer service.
Artificial Intelligence (AI) is being used in the aviation industry in a variety of ways.
An EASA executive director’s principal advisor said during a webinar that “artificial intelligence is coming with a fast pace and is being adopted widely, including in the aviation domain.” As long as AI has existed, the development of this technology has significantly accelerated in the last decade due to three concurrent factors: the capacity for collecting and storing massive amounts of data, the increase in computing power, and the development of increasingly powerful algorithms and architectures.” According to SESAR JU executive director Florian Guillermet: “AI has been around for more than 60 years but has gained ground recently, thanks to advances in computing and access to data.” Automated applications that can learn and advise on complex problems are being developed thanks to the power of machine learning and deep learning. The benefits of AI in aviation are well-known.
Romaric Redon, head advisor on AI at Airbus, said during the panel that Airbus is using AI for observation tasks such as computer vision, time series analysis, and natural language processing, predictions such as hybrid modelling, and decision making.
AI and machine learning are key components of Airbus’ Skywise data analytics platform, which collects data on aircraft operations. The COVID-19 pandemic has been a challenge for Airbus, and Skywise has been a valuable tool in helping the company prepare for and respond to it.
Our COVID response is a good example of what we do with artificial intelligence and advanced data analytics, according to Redon. For this purpose, we have built a variety of analytics, including those that examine how airlines change their planes, as well as the correlations between traffic and restrictions, which are applied worldwide.” In the end, we’d like to know ahead of time how the traffic will be stopped. This is critical information for the services that are still reeling from COVID-19.”
Airlines Improve Operations Through Artificial Intelligence and Data Science
Businesses’ interactions with customers, strategic decisions, and workflows are all impacted by technology. For example, behaviours like as purchasing a flight over the phone or conducting entirely offline surveys seem odd in the modern era. Real-time data access — the oil of the twenty-first century — enables enterprises to make educated decisions about operational efficiency. Analytics, machinery maintenance, customer support, and a variety of other internal processes and jobs can all be streamlined and automated using artificial intelligence and cognitive technologies that make sense of data. As a result, AI technology can help with a variety of elements of airline operations management.
Marginal seat revenue is anticipated (EMSR). After WTP is defined, this optimization model is computed. The metric, which entails assigning a seat to a specific fare class, can be interpreted as the expected value of the current seat (FC). EMSR is calculated by multiplying sales profit by the likelihood of selling an additional (marginal) seat belonging to a specific FC. “There comes a point when the sales probability of a higher-priced ticket is so low that the expected revenue in a lower fare class is greater.” “With these probabilities, you can determine the fare-class allocation for each day prior to departure,” Konstantin adds.
According to this expert, in the best-case scenario, specialists must know the sell-up probabilities for different fare classes and days before departure in order to accurately determine WTP and EMSR. If a customer’s request is denied, the sell-up probability indicates whether they are likely to purchase a higher-priced ticket. Flights must be clustered based on their destinations and flight dates. A clickstream analysis is also performed by the revenue management team to determine how many customers saw a web page displaying a specific price. When determining willingness to pay and expected marginal seat revenue, airlines rely on historical sales data.
Among the potential applications of AI in the aviation industry are
In order to maximise revenue, airlines are adjusting their advertised fare based on the passenger’s journey, flight path, and broad segmentation in order to optimise the base fare. Following an analysis of client demographics and current market situations, fares are further adjusted. Flight ticket rates are affected by a wide range of factors, including the time of year, the quantity of available seats on the plane, and so on. As a result, several airlines have already implemented dynamic pricing on some ticket search results, according to John McBride, director of product management at the software vendor PROS.
Airline revenue management, often known as dynamic pricing, is a form of pricing optimization. Machine learning algorithms are constantly looking for ways to increase long-term revenue to ensure that all flights are filled to their fullest capacity. Past reservations, flight distances, and willingness to pay are only some of the historical data that is included in this.
Airlines can use predictive analytics and cutting-edge technology to analyse large real-time data to predict flight delays and update departure times, as well as re-book clients’ flights on time, in order to keep their customers happy.