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Many market elements are influencing the AI in aviation, which is entering a revolutionary period. These forces work together to define the trajectory of the AI in aviation. The aviation industry's growing need for efficiency and automation is one of the main drivers. Airlines and aviation businesses are realizing more and more how AI can improve safety, save money, and streamline operations. The demand for cutting-edge technology is growing as the aviation sector expands internationally, which is good news for the development of AI applications. The increased emphasis on risk management and safety is another important market aspect.
Artificial intelligence (AI) is essential to defect identification, predictive maintenance, and real-time aviation system monitoring, all of which raise safety standards. Airlines are spending money on AI-driven systems that analyze enormous volumes of data and spot any problems before they become serious, reducing the chance of mishaps and improving aviation safety overall. In the aviation sector, cost effectiveness is a critical factor, and artificial intelligence (AI) provides solutions to improve a range of airline operations.
AI applications can save costs and improve operational efficiency in a variety of ways, from staff scheduling and predictive maintenance to route planning and fuel usage optimization. AI-driven algorithms are being used by airlines more and more to make data-driven decisions that, over time, save a lot of money. In addition, the competitive environment in the aviation sector is pushing businesses to use AI to gain a competitive advantage. Airlines are personalizing passenger experiences with AI by providing individualized services and suggestions based on preferences. This helps airlines stand apart in a competitive industry while also improving consumer happiness. Airlines may now adjust ticket rates depending on demand, seasonality, and competitor activities thanks to the use of AI in marketing and pricing tactics.
The use of AI in aviation is also impacted by the regulatory framework. Regulations are changing to allow for the integration of these technologies as aviation authorities realize how AI can increase efficiency and safety. Governments and regulatory agencies are collaborating closely with industry players to develop standards and recommendations for the safe and responsible application of AI in aviation.
AI In Aviation Market Highlights:
AI In Aviation Market Overview
Rapid development of artificial intelligence technology and increased incorporation of AI in aviation is aiding the aviation industry in optimizing various processes from passenger experience to flight operations. Market Research Future's report on the global AI in aviation market is projected to grow from USD 3.41 Billion in 2023 to USD 31.01 Billion by 2032, exhibiting a compound annual growth rate (CAGR) of 27.80% during the forecast period (2024 - 2032).
Figure1: AI In Aviation Market, 2018 - 2032 (USD Billion)
Source: Secondary Research, Primary Research, MRFR Database, and Analyst Review
Increasing adoption of automation and utilization of big data in aerospace industry are majorly driving the growth of the global AI in aviation market. High demand for process optimization and automatic improvement of performance through machine learning are also fueling the expansion of the global AI in aviation market.
MRFR's research report provides a comprehensive and unbiased analysis of market dynamics in the global AI in aviation market. It includes discussion on historical and current market dynamics, growth opportunities and challenges backed by fact-based feedbacks. The primary types of technologies that are covered in the report are hardware, software and services. The types of equipment that are assessed in the report on the global AI in aviation market are machine learning, natural language processing, context awareness computing and computer vision. For opportunity analysis, the application areas of AI in aviation are surveillance, virtual assistance, flight operations, smart logistics, smart maintenance, training, dynamic pricing, manufacturing and others. The sub-markets that are included in the report are profiled under major regions such as North America, Asia Pacific (APAC), Europe and the rest of the world, with respective country-level analysis.
AI is quickly changing the aviation sector and has the potential to completely redefine air travel from the cockpit to the ground. AI-powered systems are already being utilised to boost sustainability, effectiveness, and safety, and their influence is only anticipated to increase in the coming years.
Companies Covered
The major players operating in the global AI in aviation market are,
- Intel Corporation
- Garmin Ltd.
- IBM Corporation
- Airbus SE
- Boeing
- General Electric
- Amazon
- Microsoft Corporation
- NVIDIA Corporation
- Neurala Inc
- Samsung Electronics
- Micron Technology
- Xilinx
- Thales S.A.
- Lockheed Martin Corporation
- Northrop Grumman Corporation
- Pilot AI Labs
- IRIS Automation
- Innovative Binaries
- Cognitive Code
- Searidge Technologies
Research Methodology
MRFR utilizes its multi-layered and iterative research module to ensure the deliverance of highly qualitative and quantitative analysis of growth opportunities, current market trends, market challenges and consumer as well as competitive landscape. The primary and secondary research processes are utilized to ensure accuracy and objectivity of the research findings. Primary data is collected by carrying out surveys and personal interviews with the key opinion makers across the value chain. Secondary research processes verify the primary data through inputs from white paper references, SEC filings, paid databases, research publications and other sources available in public domain. The top-down and bottom-up approaches are utilized for delivering highly accurate market insights.
Latest Industry News:
November 2023:
The collaboration between Riyadh Air and IBM signifies a pivotal integration of AI in aviation, aiming to revolutionize travel experiences. Riyadh Air plans to leverage AI for personalized services and seamless journeys, while IBM's support in establishing a hybrid cloud integration platform underscores AI's role in optimizing operations and enhancing in-flight entertainment systems. This partnership highlights AI's transformative potential in driving innovation, efficiency, and customer satisfaction within the aviation industry.
March 2023:
Airbus and its subsidiary, Airbus UpNext, have achieved in-flight autonomous guidance and control of a drone using an A310 MRTT, marking a milestone towards Autonomous Formation Flight and Autonomous Air-to-Air refueling (A4R). The technology aims to reduce crew fatigue, minimize human error, and enhance operational efficiency, potentially revolutionizing future aerial operations involving manned and unmanned assets. The project, named Auto'Mate, integrates AI algorithms for accurate relative navigation, intra-flight communication, and cooperative control algorithms.
March 2023:
Boeing and Shield AI have signed a memorandum of understanding to explore collaboration in autonomous capabilities and artificial intelligence for defense programs. Shield AI's AI pilot, Hivemind, has flown various aircraft and can enable autonomous operation of drones and aircraft without GPS or human pilots. The collaboration aims to integrate Shield AI's AI pilot technology with Boeing aircraft to enhance their capabilities significantly. Shield AI is a venture-backed defense technology company focused on developing intelligent systems for protecting service members and civilians.
Segmental Analysis
For the scope of report, MRFR offers a in-depth segmental analysis of the global AI in aviation market
By Type
-
Hardware
- Processors
- Memory
- Networks
-
Software
- AI Platform
- AI Solution
-
Services
- Integration & Deployment
- Support & Maintenance
By Equipment
- Autocollimator
- Measuring Microscope
- Profile Projector
- Optical Digitizers & Scanners (ODS)
- Co-ordinate Measuring Machine (CMM)
- Vision Measuring Machine (VMM)
By Technology
-
Machine Learning (Leading)
- Deep Learning
- Supervised Learning
- Unsupervised Learning
- Semi-supervised learning
- Reinforcement Learning
-
Natural Language Processing
-
Context Awareness Computing
-
Computer Vision
By Application
- Surveillance
- Virtual Assistance
- Flight Operations
- Smart Logistics
- Smart Maintenance
- Training
- Dynamic Pricing
- Manufacturing
- Material Movement
- nspection & Predictive Maintenance
- Production Planning
- Quality Control
- Waste Reclamation
- Others
By Region
- North America
- Europe
- Asia Pacific
- Rest of the world
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