全球航空人工智能市场 - 行业趋势及 2031 年预测

航空人工智能市场分析及规模

航空市场的人工智能用于加强安全措施、优化运营和改善航空业各个领域的乘客体验。人工智能应用于飞行运营，以优化航线、进行预测性维护以最大限度地减少停机时间，以及进行空中交通管理以实现高效导航。例如，空客利用人工智能算法分析飞机数据并预测潜在故障，从而实现主动维护行动，从而提高安全性并降低运营成本。人工智能航空的多功能性延伸到地面自动化、客户服务聊天机器人和行李处理优化，从而改变了整个行业。

2023 年全球航空人工智能市场规模价值为 43.3 亿美元，预计到 2031 年将达到 903.8 亿美元，2024 年至 2031 年预测期内的复合年增长率为 46.2%。除了对市场价值、增长率、细分、地理覆盖范围和主要参与者等市场情景的见解外，Data Bridge Market Research 策划的市场报告还包括深入的专家分析、按地理位置表示的公司生产和产能、分销商和合作伙伴的网络布局、详细和更新的价格趋势分析以及供应链和需求的赤字分析。

报告范围和市场细分       

Market Definition

Artificial intelligence in aviation refers to the use of computer systems to perform tasks that generally require human intelligence, such as piloting aircraft, managing air traffic, and analyzing data. AI enhances safety, efficiency, and decision-making in aviation by automating processes, detecting anomalies, and providing insights from vast amounts of information. It enables advancements such as autonomous flight, predictive maintenance, and personalized passenger experiences, transforming the industry.

Artificial Intelligence in Aviation Market Dynamics

Drivers

• Enhanced Safety Measures Through AI Integration

AI algorithms analyze vast amounts of data from various sources including sensors, weather patterns, and historical flight data to predict potential safety hazards and mitigate risks proactively. These systems offer real-time monitoring of aircraft systems, airspace conditions, and pilot behavior, enabling rapid response to potential threats.  Through leveraging AI, airlines and aviation authorities can identify safety issues before they escalate, leading to fewer accidents, improved incident response, and ultimately, a safer environment for passengers, crew, and assets, thereby fostering trust and driving the adoption of AI technologies in the aviation industry.

For instance,

• Major Germam airlines such as Lufthansa use AI algorithms to predict aircraft component failures, enhancing safety. Their predictive maintenance system analyzes data from sensors and historical records to preemptively address issues, reducing accidents and improving response
• Streamlined Air Traffic Management Systems

AI technologies optimize airspace usage, route planning, and traffic flow management, reducing congestion and delays. AI enables more efficient and flexible decision-making by air traffic controllers by analyzing vast amounts of data, including flight trajectories, and airport operations. This results in enhanced safety, reduced fuel consumption and minimized environmental impact. In addition, AI-driven automation streamlines communication and coordination among stakeholders, improving overall operational efficiency. As air travel demand continues to grow, the adoption of AI in air traffic management becomes essential for managing increasing complexity, ensuring smoother operations, and driving market growth.

For instance,

• NASA's Advanced Air Mobility project represents a development in urban air transportation. The project aims to optimize routes, minimize congestion, and reduce environmental impact by leveraging AI algorithms to analyze flight trajectories and airspace data. This initiative underscores the potential of AI-driven solutions to revolutionize air mobility, ensuring safer and more efficient transportation in increasingly congested urban environments

Opportunities

• Technological Advancement in Barcode Reading

Airlines can optimize routes to avoid hazardous weather conditions, reducing the risk of turbulence, lightning strikes, and other weather-related incidents by integrating these forecasts into flight planning and decision-making processes. This proactive approach enhances flight safety, minimizes disruptions, and improves passenger experience. As airlines prioritize safety and efficiency, the demand for AI-powered weather forecasting solutions continues to grow, driving innovation and investment in the aviation industry.

• Crew Training and Simulation

Use AI-driven simulations and training systems for pilot and crew training. AI can simulate various scenarios, environments, and emergencies to train pilots and crew members effectively, improve decision-making skills, and enhance safety measures. AI enables dynamic scenario generation, providing tailored training experiences for different skill levels and aircraft types. Moreover, continuous data analysis from training sessions empowers personalized feedback and performance evaluation, fostering continuous improvement. Ultimately, AI-driven training solutions contribute to elevated safety standards, ensuring aviation professionals are well-prepared to handle any challenge they may encounter in the skies.

Restraints/Challenges

• Dependency on Reliable Internet Connectivity

AI systems thrive on real-time data processing and communication, they are inherently reliant on uninterrupted internet access. In remote or airspace-constrained regions, where connectivity may be limited or intermittent, the effectiveness of AI applications can be compromised. This dependency introduces vulnerabilities to critical functions such as flight planning, weather monitoring, and communication with ground control. Moreover, in-flight connectivity solutions may not always guarantee the level of reliability required for seamless AI operations. As a result, the aviation industry faces challenges in fully leveraging AI technologies across its operations, hindering widespread adoption and innovation.

• Limited Availability of Skilled AI Professionals

Developing and implementing AI solutions tailored to aviation require specialized expertise in both AI technologies and aviation operations. However, the intersection of these domains remains relatively niche, resulting in a scarcity of qualified professionals. This shortage hampers the timely deployment and optimization of AI applications in aviation, leading to delays, increased costs, and suboptimal performance. Furthermore, competition for AI talent from other industries exacerbates the challenge, making it difficult for aviation companies to attract and retain top-tier AI experts. As a result, the pace of AI adoption in aviation lags behind its potential, impeding innovation and competitiveness.

This market report provides details of new recent developments, trade regulations, import-export analysis, production analysis, value chain optimization, market share, impact of domestic and localized market players, analyses opportunities in terms of emerging revenue pockets, changes in market regulations, strategic market growth analysis, market size, category market growths, application niches and dominance, product approvals, product launches, geographic expansions, technological innovations in the market. To gain more info on the market, contact data bridge market research for an analyst brief, our team will help you take an informed market decision to achieve market growth.

Impact and Current Market Scenario of Raw Material Shortage and Shipping Delays

Data Bridge Market Research offers a high-level analysis of the market and delivers information by keeping in account the impact and current market environment of raw material shortage and shipping delays. This translates into assessing strategic possibilities, creating effective action plans, and assisting businesses in making important decisions.

Apart from the standard report, we also offer in-depth analysis of the procurement level from forecasted shipping delays, distributor mapping by region, commodity analysis, production analysis, price mapping trends, sourcing, category performance analysis, supply chain risk management solutions, advanced benchmarking, and other services for procurement and strategic support.

Expected Impact of Economic Slowdown on the Pricing and Availability of Products

When economic activity slows, industries begin to suffer. The forecasted effects of the economic downturn on the pricing and accessibility of the products are taken into account in the market insight reports and intelligence services provided by DBMR. With this, our clients can typically keep one step ahead of their competitors, project their sales and revenue, and estimate their profit and loss expenditures.

Recent Developments

• In October 2022, Searidge Technologies created an AI powered software using NVIDIA GPUs. Its digital tower and apron solutions, use vision AI to manage traffic control for the airports and alert users of safety concern in real time. This innovative technology not only improves airport operations but also boosts market growth by increasing the attractiveness of airports as safer, more efficient hubs, consequently driving demand for Searidge's cutting-edge solutions
• In April 2022, Banglore International Airport Limited (BIAL) collaborated with Amazon to establish a Joint Innovation Center (JIC) and accelerated innovation in aviation. This collaboration fosters the development of new technologies and solutions tailored to the aviation industry's needs, enhancing operational efficiency, passenger experience, and safety standards. As a result, it stimulates market growth by driving innovation, attracting investment, and positioning BIAL as a leader in aviation advancement

Artificial Intelligence in Aviation Market Scope

The artificial intelligence in aviation market is segmented into three notable segments which are based on offering, technology, and application. The growth amongst these segments will help you analyze meagre growth segments in the industries and provide the users with a valuable market overview and market insights to help them make strategic decisions for identifying core market applications.

Offering

• Services
• Hardware
• Software

Technology

• Computer Vision
• Machine Learning
• Context Awareness Computing
• Natural Language Processing

Application

• Dynamic Pricing
• Virtual Assistants
• Flight Operations
• Smart Maintenance
• Manufacturing
• Surveillance
• Training
• Other Applications

Artificial Intelligence in Aviation Market Regional Analysis/Insights

The market is analyzed and market size insights and trends are provided by offering, technology, and application as referenced above.

The countries covered in the market report are U.S., Canada, Mexic, Germany, Sweden, Poland, Denmark, Italy, U.K., France, Spain, Netherlands, Belgium, Switzerland, Turkey, Russia, Rest of Europe in Europe, Japan, China, India, South Korea, New Zealand, Vietnam, Australia, Singapore, Malaysia, Thailand, Indonesia, Philippines, Rest of Asia-Pacific, Brazil, Argentina, Rest of South America as a part of South America, U.A.E, Saudi Arabia, Oman, Qatar, Kuwait, South Africa, and Rest of Middle East and Africa.

North America dominates the artificial intelligence in aviation market and will continue to flourish its trend of dominance due to the swift industrialization and presence of major key players in this region.

Asia-Pacific is expected to be the fastest-growing region in the artificial intelligence in aviation market  due to the growing demand for AI technologies in the aviation sector. A significant presence in the top market player in the region which provides all the services and products in the market to the vast market size

报告的国家部分还提供了影响单个市场因素和国内市场监管变化，这些因素和变化会影响市场的当前和未来趋势。下游和上游价值链分析、技术趋势和波特五力分析、案例研究等数据点是用于预测单个国家市场情景的一些指标。此外，在提供国家数据的预测分析时，还考虑了全球品牌的存在和可用性以及由于来自本地和国内品牌的大量或稀缺竞争而面临的挑战、国内关税和贸易路线的影响。

竞争格局 航空人工智能市场份额分析

市场 竞争格局按竞争对手提供详细信息。详细信息包括公司概况、公司财务状况、产生的收入、市场潜力、研发投资、新市场计划、全球影响力、生产基地和设施、生产能力、公司优势和劣势、产品发布、产品宽度和广度、应用主导地位。以上提供的数据点仅与公司对市场的关注有关。

市场上的一些主要参与者 包括：

• IBM（美国）
• 微软 (美国)
• 亚马逊网络服务公司（美国）
• 空中客车公司（美国）
• Xilinx（美国）
• NVIDIA 公司（美国）
• 英特尔公司（美国）
• 通用电气（美国）
• 美光科技有限公司（美国）
• 洛克希德·马丁公司（美国）
• 三星（韩国）
• 泰雷兹(法国)
• MINDTITAN（爱沙尼亚）
• 三菱电机株式会社（日本）

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