Aerospace Artificial Intelligence Market Size, Share & Trends Analysis Report By Offering (Software, Hardware, Services), By Technology (Machine Learning, Natural Language Processing, Computer Vision, Context Awareness Computing), By Applications (Customer Service, Smart Maintenance, Manufacturing, Training, Flight Operations, Others) and By Region(North America, Europe, APAC, Middle East and Africa, LATAM) Forecasts, 2022-2030

Market Overview

The global aerospace artificial intelligence market size was valued at USD 467 million in 2021. It is projected to reach USD 11.67 billion by 2030, growing at a CAGR of 43% during the forecast period (2022–2030).

Artificial intelligence (AI) technologies like machine learning, natural language processing, computer vision, and context awareness computing are used to increase the efficiency of several aerospace-related tasks, including flight operations, improved customer service, proactive aircraft maintenance, and the production of aircraft parts. The aerospace industry is in its initial phase of adoption of AI. The aerospace sector is witnessing an increase in applications, and more disruptive AI models are expected to be developed over the years.

Artificial intelligence plays an essential role in minimizing design process duration, prototyping, manufacturing, and cutting costs. It is projected to result in numerous future enhancements in the aerospace industry. However, stringent airline regulations and the high price of adoption of AI in aerospace are anticipated to hamper the growth of the aerospace AI market over the forecast period.

Market Dynamics

Global Aerospace Artificial Intelligence Market Drivers

Increasing Fuel Efficiency using Artificial Intelligence

Aircraft consume billions of gallons of fuel every year. Although fuel consumption witnessed a drastic decline in 2020 owing to the COVID-19 pandemic, an increase in air traffic is expected to lead to a rise in consumption over the years. In order to reduce costs brought on by the surge in fuel consumption, several companies are already using 3D printing technology to create lightweight components.

Artificial intelligence can also help aerospace firms improve aircraft fuel efficiency. The climb phase of an aircraft is when fuel consumption is highest. By examining the climb phase of many aircraft and the aircraft operations of various pilots, AI designs can assist in analyzing fuel consumption data by creating climb phase profiles for each aircraft model and pilot. These profiles may aid in reducing fuel usage. Hence, a rise in the use of AI technologies to make aircraft more fuel-efficient is expected to drive the growth of the global aerospace artificial intelligence market during the forecast period.

Growing Use of AI at Airports to Ensure Safety

Airport authorities have significantly increased security in recent years in response to an increase in airport-related threats. Systems created by artificial intelligence assist airport authorities in addressing safety concerns. The Los Angeles International International Airport, Phoenix International Airport, and John F. Kennedy International Airport have scanners that use AI to detect threats. To increase safety, numerous airports are implementing cutting-edge systems.

Additionally, machine learning models can automatically assess data for various threats. They can assist in detecting explosives and firearms while disregarding other things like keys and belt buckles, which passengers typically carry. Hence, the rise in the usage of AI for improving security at airports is expected to propel the growth of the global aerospace AI market over the forecast period.

Global Aerospace Artificial Intelligence Market Restraint

High Cost of Adoption of AI and Stringent Airline Regulations in Aerospace

Although using AI models in the aerospace industry might be highly beneficial, there are also specific threats. Stringent laws and standards govern the aerospace industry. Airport security, aircraft design, ground operations, and other factors must adhere to these standards. Therefore, to adopt AI in aerospace, organizations must develop systems that go hand-in-hand with all the global standards. This leads to an increase in time for the implementation of AI by the global aerospace industry.

The high cost of AI systems is another factor that is anticipated to contribute to the low adoption of AI models in the aerospace industry. For instance, an airline must spend thousands of dollars to deploy a chatbot to manage consumer inquiries. As a result, small airline companies would find it extremely difficult to invest in the same, which creates a barrier to adopting AI technologies for airlines.

Global Aerospace Artificial Intelligence Market Opportunities

Rising Use of AI to Ensure Operational Efficiency and Maintenance of Airplanes

Numerous aircraft sensors allow pilots to measure air pressure, altitude, and speed. AI models can assist in spotting unusual behavior in aircraft components so that sensors' computed parameters can be used more effectively. For example, turbines' sensors can collect necessary information, including temperature, air pressure, and rotation speed. This information can be used to teach AI models about typical turbine performance. AI models also use these data to check whether turbines operate normally and alert worried personnel to potential problems. The rise in the adoption of AI to guarantee operational efficiency and supervision of airplanes is anticipated to drive the development of the global aerospace artificial intelligence market.

Regional Analysis

The global aerospace artificial intelligence market is bifurcated into four regions, namely North America, Europe, Asia-Pacific, and LAMEA.

North America Dominates the Global Market

North America is the most significant shareholder in the global aerospace artificial intelligence market and is expected to grow at a CAGR of 44.5% during the forecast period. The rising investments by airports in the U.S. and Canada and the adoption of A.I. technologies by airlines are expected to boost the growth of the aerospace artificial intelligence market in the region. Moreover, big aerospace players such as The Boeing Company and A.I. startups such as Sparkcognition are expected to boost the deployment of A.I. technologies in aerospace engineering in the region. The U.S. and Canada are the early adopters of integrating A.I. technologies with aerospace systems. A rise in investments by companies and initiatives by governments to boost the use of A.I. technologies in the airline industry are anticipated to drive the growth of the aerospace artificial intelligence market in the region during the forecast period. Canada is one of the global leaders in adopting A.I. and is home to numerous aerospace A.I. companies.

Europe is expected to grow at a CAGR of 43.6%, generating USD 3,320 million during the forecast period. An increase in initiatives to propel the adoption of A.I. in the aerospace and defense sector is one of the significant factors that is anticipated to boost the growth of the aerospace artificial intelligence market in the region. Europe is home to technologically advanced nations such as the UK, France, Germany, and Russia, which have a developed aerospace industry.

In addition, the broad adoption of A.I. technologies in the region is anticipated to result in the exponential growth of the global market during the forecast period. U.K. is taking significant steps to integrate A.I. technology with aerospace applications. The government of the U.K. provides monetary support for A.I. & Aerospace development. The objective is to strengthen U.K. manufacturing businesses to deliver materials and resources for upcoming sustainable developments. Such initiatives are expected to boost the growth of the global market in the country during the forecast period.

Asia-Pacific includes China, Japan, India, South Korea, and the rest of the Asia-Pacific. An increase in research regarding A.I. adoption in aerospace, along with the rise in acceptance of A.I. in aviation, is expected to drive the growth of the global market in the region during the forecast period. The rising investments by significant countries such as China, Japan, South Korea, and India, along with a rapid increase in the adoption of A.I., are the factors that are anticipated to boost the growth of the global market in the region during the forecast period.

China is a dominant player in the global market. Its various airlines and airports have embraced A.I. technologies. Around 88% of Chinese airlines and airports have strategies for programs or studies related to A.I. and aim to use virtual assistants and chatbots. Japanese airlines are leveraging A.I. to earn more profits and offer better services to their customers. These factors are anticipated to drive market growth during the forecast period.

An increase in the adoption of A.I. in various countries in the LAMEA is anticipated to propel the growth of the global market during the forecast period. The adoption of artificial intelligence is on the rise in Latin America. Several airlines and airports are leveraging A.I. to offer cutting-edge solutions. Moreover, an increase in technological advancements by aerospace players is anticipated to propel the growth of the market. Middle East countries invest significantly in new technologies, and their spending on A.I. is expected to increase over the years. Implementation of A.I. and machine learning in aerospace is going up, which is boosting the market growth.

Segmental Analysis

The global aerospace artificial intelligence market is segmented by offering, technology, and application.

Based on the offering, the global market is segmented into software, hardware, and services.

The software segment is the highest contributor to the market and is expected to grow at a CAGR of 45.5% during the forecast period. Artificial intelligence software is a computer program designed to imitate human intelligence by studying numerous insights and data patterns. The foremost features of AI software comprise speech and voice recognition, machine learning, and virtual assistant. AI and machine learning are collectively used to offer users essential usability to simplify business operations more efficiently. Several aerospace companies are implementing AI software to build cutting-edge solutions that make several functions seamless and efficient. Moreover, the rise in the use of AI software to assist companies in manufacturing, predictive maintenance of aircraft, and pilot training is anticipated to drive demand for AI software during the forecast period.

AI hardware or chips are specifically designed accelerators for artificial neural network (ANN) built applications. The hardware structure of an AI chip comprises three parts, which include networking, computing, and storage. Major AI hardware manufacturing companies such as IBM, Intel, and Nvidia are contending in the market to advance networking and storage modules of hardware systems. AI hardware experts are exploring new architectures and algorithms to enhance processing effectiveness and enable the shift from narrow AI to broad AI.

Based on technology, the global market is segmented into machine learning, natural language processing, computer vision, and context awareness computing.

The machine learning segment owns the highest market share and is anticipated to grow at a CAGR of 45.4% during the forecast period. Machine learning, a subset of artificial intelligence, is focused on making applications that learn from data and advance their accuracy over time without being programmed. It includes algorithms capable of creating systems by understanding input and output information. Airlines use AI systems with in-built machine learning algorithms to gather and study flight data for aircraft type and mass, route distance and heights, and weather conditions.

Natural language processing (NLP) is a subdivision of artificial intelligence that assists computers in comprehending, inferring, and manipulating human language. NLP incorporates various fields of study, comprising computational linguistics and computer science, to fill the gap between computer understanding and human communications. Natural language processing-powered systems, when installed in the cockpit, could study the pilot’s sentiments and may be able to give alerts or take control of the aircraft.

Based on application, the global market is segmented into customer service, smart maintenance, manufacturing, training, flight operations, and others.

The flight operations segment is the highest contributor to the market and is expected to grow at a CAGR of 45.7% during the forecast period. Artificial Intelligence technology is optimizing flight operations and hugely impacting commercial aviation. A rise in the number of flights is forcing aviation agencies to implement AI-based air traffic control systems to assist air traffic controllers in performing certain functions. Air traffic management is expected to benefit considerably from artificial intelligence by the feature of its dependence on iterative activity. Machine learning and computer vision technologies enable safer air traffic control across activities such as flow management, flight planning, conflict prediction, and safety assessments.

Customer satisfaction is predominantly significant in the commercial aviation sector. AI can help improve customer experience and offer enhanced customer service. There are various areas where artificial intelligence can be applied to deliver superior customer service, like chatbots. Chatbots are AI-powered digital tools that can respond to questions from customers instantaneously and in a human-like style. They can reduce time and effort by automating customer support.

Impact of covid-19

Recent Developments

• April 2022- TruWeather Solutions teamed up with Iris Automation for ground-based weather surveillance for uncrewed aircraft. This meshed network will provide operators with real-time integrated communications, collision avoidance, and micro-weather data.
• May 2022- Microsoft partnered with Volocopter to build an aerospace cloud system. The system will address the nascent cloud computing requirements for electric vertical takeoff and landing aircraft, UAM, and autonomous aviation.