항공 분야 인공지능(AI) 시장 : 기회, 성장요인, 업계 동향 분석 및 예측(2026-2035년)

The Global Artificial Intelligence in Aviation Market was valued at USD 1.9 billion in 2025 and is estimated to grow at a CAGR of 19.3% to reach USD 10.9 billion by 2035.

The strong growth reflects the accelerating adoption of AI technologies across airlines, airports, and aviation service providers seeking to improve operational efficiency, enhance safety standards, and deliver more personalized passenger experiences. AI-driven aviation systems are increasingly built on machine learning models, predictive analytics, and real-time data processing capabilities that support optimized flight scheduling, intelligent routing, and predictive maintenance strategies. The growing emphasis on cost efficiency, automation, and data-driven decision-making is further reinforcing adoption across both commercial and operational aviation functions. In parallel, regulatory bodies are strengthening guidelines around aviation safety, cybersecurity, and operational transparency, encouraging wider deployment of AI systems that improve situational awareness and reduce human error. The technology landscape is also expanding beyond traditional applications into autonomous flight operations, advanced air traffic management systems, and passenger behavior analytics, where AI enables rapid interpretation of large-scale datasets to improve fuel efficiency, reduce turnaround time, and enhance operational reliability across global aviation networks.

The software segment held a 45.6% share in 2025 and is expected to grow at a CAGR of 20.5% through 2035. This segment continues to grow rapidly as aviation stakeholders adopt AI-enabled platforms for analytics, automation, and operational optimization across multiple functions. The increasing reliance on cloud-based AI infrastructure and aviation-specific digital solutions is strengthening integration across flight operations, maintenance systems, and passenger-facing applications, supporting more efficient and scalable deployment models.

The machine learning segment accounted for 35.2% share in 2025 and is projected to grow at a CAGR of 19.9% from 2026 to 2035. Machine learning plays a central role in aviation AI systems by processing large volumes of historical and real-time operational data to improve forecasting accuracy and decision-making. It is widely used for predictive maintenance scheduling, fuel consumption optimization, demand forecasting, and flight path efficiency improvements, enabling aviation operators to reduce operational costs while enhancing performance reliability.

Asia Pacific Artificial Intelligence in Aviation Market held a 43.2% share, generating USD 157.5 million in 2025. The country's leadership is supported by rapid aviation sector expansion, large passenger volumes, and significant investments in digital airport infrastructure. AI technologies are being widely deployed across airport operations, including biometric-enabled passenger processing, intelligent security systems, and automated baggage handling solutions, strengthening overall operational efficiency and throughput capacity.

Companies operating in the global artificial intelligence in aviation industry include Airbus, Honeywell International, GE Aerospace, Microsoft, Thales, Amazon (AWS), The Boeing Company, IBM, SAP, and Siemens. Leading companies in the artificial intelligence in aviation market are focusing on strengthening cloud-based AI ecosystems that enable seamless integration across airline operations, airport systems, and maintenance platforms. They are investing heavily in machine learning and predictive analytics capabilities to enhance operational efficiency, safety management, and cost optimization. Strategic collaborations with airlines, airports, and defense organizations are being used to co-develop customized AI solutions tailored to specific operational needs. Companies are also expanding their digital infrastructure through cloud deployment and edge computing to improve scalability and real-time processing capabilities. In addition, strong emphasis is placed on cybersecurity, data governance, and regulatory compliance to meet evolving aviation safety standards.

Table of Contents

Chapter 1 Methodology

• 1.1 Research approach
• 1.2 Quality Commitments
  • 1.2.1 GMI AI policy & data integrity commitment
    • 1.2.1.1 Source consistency protocol
• 1.3 Research Trail & Confidence Scoring
  • 1.3.1 Research Trail Components
  • 1.3.2 Scoring Components
• 1.4 Data Collection
  • 1.4.1 Partial list of primary sources
• 1.5 Data mining sources
  • 1.5.1 Paid sources
    • 1.5.1.1 Sources, by region
• 1.6 Base estimates and calculations
  • 1.6.1 Base year calculation
• 1.7 Forecast model
  • 1.7.1 Quantified market impact analysis
    • 1.7.1.1 Mathematical impact of growth parameters on forecast
• 1.8 Research transparency addendum
  • 1.8.1 Source attribution framework
  • 1.8.2 Quality assurance metrics
  • 1.8.3 Our commitment to trust

Chapter 2 Executive Summary

• 2.1 Industry 360° synopsis, 2022 - 2035
• 2.2 Key market trends
  • 2.2.1 Regional
  • 2.2.2 Component
  • 2.2.3 Application
  • 2.2.4 Technology
• 2.3 TAM Analysis, 2026-2035
• 2.4 CXO perspectives: Strategic imperatives

Chapter 3 Industry Insights

• 3.1 Industry ecosystem analysis
  • 3.1.1 Supplier landscape
  • 3.1.2 Profit margin analysis
  • 3.1.3 Cost structure
  • 3.1.4 Value addition at each stage
  • 3.1.5 Factor affecting the value chain
  • 3.1.6 Disruptions
• 3.2 Industry impact forces
  • 3.2.1 Growth drivers
    • 3.2.1.1 Growing adoption of smart airports.
    • 3.2.1.2 Increasing use of big data in aerospace industry.
    • 3.2.1.3 Growing adoption of artificial intelligence to enhance customer services.
    • 3.2.1.4 The rapidly increasing investments by the aerospace companies.
  • 3.2.2 Industry pitfalls and challenges
    • 3.2.2.1 Lack of skilled professionals.
    • 3.2.2.2 Data privacy and security.
  • 3.2.3 Market opportunities
    • 3.2.3.1 Smart Airport Development
    • 3.2.3.2 Predictive Maintenance Expansion
    • 3.2.3.3 Autonomous Aviation Systems Growth
    • 3.2.3.4 AI-powered Passenger Experience Enhancement
• 3.3 Growth potential analysis
• 3.4 Pricing Analysis (Driven by Primary Research)
  • 3.4.1 Historical Price Trend Analysis
  • 3.4.2 Pricing Strategy by Player Type
• 3.5 Cost breakdown analysis
• 3.6 Regulatory landscape
  • 3.6.1 North America
    • 3.6.1.1 Federal Aviation Administration
    • 3.6.1.2 National Aeronautics and Space Administration
    • 3.6.1.3 U.S. Department of Transportation
    • 3.6.1.4 Transport Canada
  • 3.6.2 Europe
    • 3.6.2.1 European Union Aviation Safety Agency
    • 3.6.2.2 European Commission
    • 3.6.2.3 Eurocontrol
    • 3.6.2.4 European Committee for Standardization
  • 3.6.3 Asia Pacific
    • 3.6.3.1 Civil Aviation Administration of China
    • 3.6.3.2 Ministry of Civil Aviation India
    • 3.6.3.3 Directorate General of Civil Aviation India
    • 3.6.3.4 Ministry of Land Infrastructure Transport and Tourism Japan
  • 3.6.4 Latin America
    • 3.6.4.1 National Civil Aviation Agency Brazil
    • 3.6.4.2 Directorate General of Civil Aviation Mexico
    • 3.6.4.3 National Civil Aviation Administration Argentina
  • 3.6.5 Middle East and Africa
    • 3.6.5.1 General Authority of Civil Aviation Saudi Arabia
    • 3.6.5.2 General Civil Aviation Authority UAE
    • 3.6.5.3 Qatar Civil Aviation Authority
• 3.7 Porter's analysis
• 3.8 PESTEL analysis
• 3.9 Technology and Innovation Landscape
  • 3.9.1 Current technological trends
    • 3.9.1.1 Machine Learning-based Predictive Maintenance
    • 3.9.1.2 Natural Language Processing for Virtual Assistants
    • 3.9.1.3 Computer Vision for Airport Surveillance and Inspection
    • 3.9.1.4 Cloud-based AI Analytics Platforms
  • 3.9.2 Emerging technologies
    • 3.9.2.1 Autonomous Flight Systems and AI Copilots
    • 3.9.2.2 Generative AI for Aviation Simulation and Training
    • 3.9.2.3 Digital Twin Technology for Aircraft and Airports
    • 3.9.2.4 Edge AI for Real-time Onboard Decision Making
• 3.10 Patent Landscape (Driven by Primary Research)
• 3.11 Cost breakdown analysis
• 3.12 Impact of AI & Generative AI on the Market (Driven by Primary Research)
  • 3.12.1 AI-Driven Disruption of Existing Business Models
  • 3.12.2 GenAI Use Cases & Adoption Roadmap by Segment
  • 3.12.3 Risks, Limitations & Regulatory Considerations
• 3.13 Sustainability and environmental aspects
  • 3.13.1 Sustainable practices
  • 3.13.2 Waste reduction strategies
  • 3.13.3 Energy efficiency in production
  • 3.13.4 Eco-friendly initiatives
  • 3.13.5 Carbon footprint considerations
• 3.14 Forecast assumptions & scenario analysis (Driven by Primary Research)
  • 3.14.1 Base Case- Key Macro & Industry Variables Driving CAGR
  • 3.14.2 Optimistic Scenarios- Favorable macro and industry tailwinds
  • 3.14.3 Pessimistic Scenario - Macroeconomic slowdown or industry headwinds

Chapter 4 Competitive Landscape, 2025

• 4.1 Introduction
• 4.2 Company market share analysis
  • 4.2.1 North America
  • 4.2.2 Europe
  • 4.2.3 Asia Pacific
  • 4.2.4 LATAM
  • 4.2.5 MEA
• 4.3 Competitive analysis of major market players
• 4.4 Competitive positioning matrix
• 4.5 Key developments
  • 4.5.1 Mergers & acquisitions
  • 4.5.2 Partnerships & collaborations
  • 4.5.3 New Product Launches
  • 4.5.4 Expansion Plans and funding
• 4.6 Company tier benchmarking
  • 4.6.1 Tier classification criteria & qualifying thresholds
  • 4.6.2 Tier positioning matrix by revenue, geography & innovation

Chapter 5 Market Estimates & Forecast, By Component, 2022 - 2035 (USD Mn)

• 5.1 Key trends
• 5.2 Hardware
• 5.3 Software
• 5.4 Service

Chapter 6 Market Estimates & Forecast, By Technology, 2022 - 2035 (USD Mn)

• 6.1 Key trends
• 6.2 Context awareness computing
• 6.3 Machine Learning
• 6.4 Natural Language Processing
• 6.5 Computer Vision

Chapter 7 Market Estimates & Forecast, By Application, 2022 - 2035 (USD Mn)

• 7.1 Key trends
• 7.2 Virtual assistance
• 7.3 Smart maintenance
• 7.4 Manufacturing
• 7.5 Training

Chapter 8 Market Estimates & Forecast, By Region, 2022 - 2035 (USD Mn)

• 8.1 Key trends
• 8.2 North America
  • 8.2.1 US
  • 8.2.2 Canada
• 8.3 Europe
  • 8.3.1 Germany
  • 8.3.2 UK
  • 8.3.3 France
  • 8.3.4 Italy
  • 8.3.5 Spain
  • 8.3.6 Russia
  • 8.3.7 Norway
  • 8.3.8 Netherlands
  • 8.3.9 Sweden
• 8.4 Asia Pacific
  • 8.4.1 China
  • 8.4.2 India
  • 8.4.3 Japan
  • 8.4.4 Australia
  • 8.4.5 South Korea
  • 8.4.6 Singapore
  • 8.4.7 Thailand
  • 8.4.8 Indonesia
  • 8.4.9 Vietnam
• 8.5 Latin America
  • 8.5.1 Brazil
  • 8.5.2 Mexico
  • 8.5.3 Argentina
• 8.6 MEA
  • 8.6.1 South Africa
  • 8.6.2 Saudi Arabia
  • 8.6.3 UAE
  • 8.6.4 Turkey

Chapter 9 Company Profiles

• 9.1 Global Players
  • 9.1.1 Airbus
  • 9.1.2 Boeing
  • 9.1.3 GE Aerospace
  • 9.1.4 Honeywell International
  • 9.1.5 IBM
  • 9.1.6 NVIDIA
  • 9.1.7 Intel
  • 9.1.8 Thales
  • 9.1.9 Samsung Electronics
  • 9.1.10 Safran
  • 9.1.11 Collins Aerospace
  • 9.1.12 Leonardo
• 9.2 Regional Players
  • 9.2.1 Garmin
  • 9.2.2 Hindustan Aeronautics
  • 9.2.3 Lufthansa Technik
  • 9.2.4 Indra Sistemas
• 9.3 Emerging Players / Disruptors
  • 9.3.1 Tata Advanced Systems
  • 9.3.2 Palantir Technologies
  • 9.3.3 SkyGrid
  • 9.3.4 Aireon
  • 9.3.5 Uptake Technologies