Product Code: AS 9429
The AI in aviation market is expected to reach USD 4.86 billion by 2030, from USD 1.75 billion in 2025, with a CAGR of 22.6%. As the aviation industry shifts toward smarter, more connected ecosystems, AI plays a critical role in enabling predictive analytics, real-time decision-making, and autonomous operations. Airlines and airport operators are increasingly adopting AI to optimize flight routes, enhance passenger experience, and improve aircraft maintenance efficiency. Additionally, as governments and regulatory bodies promote net-zero emissions and digital air traffic management, AI is becoming a strategic enabler of next-generation aviation infrastructure. This adoption trend is expected to accelerate as AI technologies mature and integrate more seamlessly with avionics, air traffic systems, and ground operations.
Scope of the Report |
Years Considered for the Study | 2021-2030 |
Base Year | 2024 |
Forecast Period | 2025-2030 |
Units Considered | Value (USD Billion) |
Segments | By Solution, Business mechanism, Technology, and End User |
Regions covered | North America, Europe, APAC, RoW |
"Based on infrastructure, computer hardware is estimated to hold the largest share in 2025."
The computer hardware segment is expected to lead the AI in aviation market for infrastructure due to its fundamental role in enabling real-time processing, high-performance computing, and system integration across flight and ground operations. AI applications in aviation, such as predictive maintenance, autonomous navigation, flight data analysis, and air traffic management, require robust and reliable hardware infrastructure to function effectively. This includes GPUs, CPUs, edge computing devices, sensors, and onboard AI processors that can support intensive workloads in dynamic environments. With modern aircraft increasingly becoming "flying data centers," the demand for advanced hardware that can process large volumes of sensor, telemetry, and operational data in real time is surging. Moreover, hardware is a prerequisite for deploying AI at the edge, especially in safety-critical applications like in-flight decision support, collision avoidance, and UAV operations. Airports are also investing in AI-enabled infrastructure, such as facial recognition systems, baggage scanners, and biometric gates, which rely on specialized hardware components for performance and speed. As airlines and OEMs prioritize AI-driven digital transformation, the need for scalable, aviation-grade computing platforms continues to rise.
"Based on software, AI development tools are expected to exhibit the fastest growth during the forecast period"
AI development tools are expected to be the fastest-growing segment in the AI in aviation market for software due to their critical role in enabling customized, scalable, and domain-specific AI applications. These tools, including machine learning frameworks, data labeling platforms, simulation environments, and model training libraries, enable aviation stakeholders to build, test, and deploy AI solutions tailored to unique operational needs. As the aviation industry moves toward deeper digitalization, the need for adaptable tools to develop AI for predictive maintenance, flight optimization, air traffic control, and passenger analytics is increasing rapidly. Compared to pre-built AI systems, development tools offer flexibility, allowing airlines, OEMs, and airport operators to innovate at their own pace while ensuring regulatory compliance and data security. With a growing emphasis on explainable AI, model testing, and edge deployment, development tools are essential for training AI systems that are not only intelligent but also auditable and certifiable, especially in safety-critical aviation environments.
"Asia Pacific is expected to be the fastest-growing market for AI in aviation during the forecast period."
Asia Pacific is expected to witness rapid growth in the AI in aviation market due to a rise in air traffic, large-scale infrastructure development, and strong government support for digitalization. The region is witnessing a surge in passenger demand, particularly in countries like China, India, Indonesia, and Vietnam, where aviation markets are increasingly expanding to meet domestic and international travel needs. This growth propels the need for smarter, AI-enabled systems to manage congestion, optimize operations, and enhance safety. Governments across Asia Pacific are actively investing in smart airport projects, urban air mobility, and autonomous aviation technologies, creating fertile ground for AI integration. Countries such as China, Japan, and South Korea are leading in AI R&D, while India and Southeast Asia are rapidly adopting AI for air traffic management, predictive maintenance, and biometric security systems. Many regional carriers are also adopting AI to improve fuel efficiency, optimize crew scheduling, and deliver personalized passenger services.
The break-up of the profile of primary participants in the AI in aviation market:
- By Company Type: Tier 1 - 49%, Tier 2 - 37%, and Tier 3 - 14%
- By Designation: C-Level - 55%, D-Level - 27%, and Others - 18%
- By Region: North America - 32%, Europe - 22%, Asia Pacific - 16%, Middle East - 10%, Africa - 10%, and Latin America - 10%
Major companies profiled in the report include Amadeus IT Group S.A. (Spain), Honeywell International Inc. (US), Microsoft (US), Amazon Web Services, Inc. (US), and General Electric Company (US), among others.
Research Coverage:
This market study covers the AI in aviation market across various segments and subsegments. It aims to estimate this market's size and growth potential across different parts based on region. This study also includes an in-depth competitive analysis of the key players in the market, their company profiles, key observations related to their product and business offerings, recent developments, and key market strategies they adopted.
Reasons to buy this report:
The report will provide both market leaders and new entrants with accurate revenue estimates for the overall AI in aviation market. It aims to help stakeholders understand the competitive landscape, enabling them to position their businesses more effectively and develop appropriate go-to-market strategies. Additionally, the report offers insights into market trends and includes information on key drivers, challenges, constraints, and opportunities within the market.
The AI in aviation market experiences growth and evolution driven by various factors. The report provides insights on the following pointers:
- Market Drivers (Surge in global air traffic, Shift in passenger expectations, Rapid adoption of AI-powered predictive maintenance in aviation), Restraints (Costly implementation and maintenance, Complex regulatory landscape), Opportunities (AI in predictive and prescriptive analytics, AI in traffic management and urban air mobility, Rise of AI-powered air cargo) Challenges (Cybersecurity and data integrity risks, Regulatory fragmentation and ethical uncertainity) that could contribute to an increase in the AI in aviation market
- Market Penetration: Comprehensive information on AI in aviation offered by the top players in the market
- Product Development/Innovation: Detailed insights on upcoming technologies, R&D activities, and product launches in the AI in aviation market
- Market Development: Comprehensive information about lucrative markets; the report analyses the AI in aviation market across varied regions
- Market Diversification: Exhaustive information about new products, untapped geographies, recent developments, and investments in the AI in aviation market
- Competitive Assessment: In-depth assessment of market shares, growth strategies, products, and manufacturing capabilities of leading players in the AI in aviation market
TABLE OF CONTENTS
1 INTRODUCTION
- 1.1 STUDY OBJECTIVES
- 1.2 MARKET DEFINITION
- 1.3 STUDY SCOPE
- 1.3.1 MARKETS COVERED AND REGIONAL SCOPE
- 1.3.2 INCLUSIONS AND EXCLUSIONS
- 1.3.3 YEARS CONSIDERED
- 1.4 CURRENCY CONSIDERED
- 1.5 STAKEHOLDERS
- 1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
- 2.1 RESEARCH DATA
- 2.1.1 SECONDARY DATA
- 2.1.1.1 Key data from secondary sources
- 2.1.2 PRIMARY DATA
- 2.1.2.1 Key data from primary sources
- 2.1.2.2 Breakdown of primary interviews
- 2.2 MARKET SIZE ESTIMATION
- 2.2.1 BOTTOM-UP APPROACH
- 2.2.1.1 Demand-side methodology
- 2.2.1.2 Supply-side methodology
- 2.2.1.3 Forecasting techniques
- 2.2.2 TOP-DOWN APPROACH
- 2.3 DATA TRIANGULATION
- 2.4 RESEARCH ASSUMPTIONS
- 2.5 RESEARCH LIMITATIONS
- 2.6 RISK ASSESSMENT
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
- 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI IN AVIATION MARKET
- 4.2 AI IN AVIATION MARKET, BY SOLUTION
- 4.3 AI IN AVIATION MARKET, BY TECHNOLOGY
- 4.4 AI IN AVIATION MARKET, BY END USER
5 MARKET OVERVIEW
- 5.1 INTRODUCTION
- 5.2 MARKET DYNAMICS
- 5.2.1 DRIVERS
- 5.2.1.1 Surge in global air traffic
- 5.2.1.2 Shift in passenger expectations
- 5.2.1.3 Rapid adoption of AI-powered predictive maintenance in aviation
- 5.2.2 RESTRAINTS
- 5.2.2.1 Costly implementation and maintenance
- 5.2.2.2 Complex regulatory landscape
- 5.2.3 OPPORTUNITIES
- 5.2.3.1 AI in predictive and prescriptive analytics
- 5.2.3.2 AI in air traffic management and urban air mobility
- 5.2.3.3 Rise of AI-powered air cargo
- 5.2.4 CHALLENGES
- 5.2.4.1 Cybersecurity and data integrity risks
- 5.2.4.2 Regulatory fragmentation and ethical uncertainty
- 5.3 TRENDS AND DISRUPTIONS IMPACTING CUSTOMER BUSINESS
- 5.4 PRICING ANALYSIS
- 5.4.1 AVERAGE SELLING PRICE OF AI IN AVIATION SOLUTIONS OFFERED BY KEY PLAYERS
- 5.4.2 AVERAGE SELLING PRICE, BY REGION
- 5.5 ECOSYSTEM ANALYSIS
- 5.5.1 AI SOLUTION PROVIDERS
- 5.5.2 SYSTEM INTEGRATORS
- 5.5.3 END USERS
- 5.6 VALUE CHAIN ANALYSIS
- 5.7 KEY STAKEHOLDERS AND BUYING CRITERIA
- 5.7.1 KEY STAKEHOLDERS IN BUYING PROCESS
- 5.7.2 BUYING CRITERIA
- 5.8 KEY CONFERENCES AND EVENTS, 2025-2026
- 5.9 REGULATORY LANDSCAPE
- 5.10 INVESTMENT AND FUNDING SCENARIO
- 5.11 TECHNOLOGY ANALYSIS
- 5.11.1 KEY TECHNOLOGIES
- 5.11.1.1 Flight data analytics
- 5.11.1.2 Computer vision
- 5.11.2 COMPLEMENTARY TECHNOLOGIES
- 5.11.2.1 Edge AI Hardware
- 5.11.2.2 Real-time connectivity
- 5.11.3 ADJACENT TECHNOLOGIES
- 5.11.3.1 Electric propulsion
- 5.11.3.2 More electric aircraft
- 5.12 CASE STUDY ANALYSIS
- 5.12.1 HEATHROW AIRPORT: AI FOR PASSENGER FLOW OPTIMIZATION
- 5.12.2 AIRBUS: AI IN SKYWISE FOR PREDICTIVE MAINTENANCE
- 5.12.3 FAA: AI FOR AIR TRAFFIC FLOW MANAGEMENT
- 5.12.4 DELTA AIR LINES: AI FOR BAGGAGE AND IRREGULAR OPERATIONS
- 5.13 TECHNOLOGY ROADMAP
- 5.14 PATENT ANALYSIS
- 5.15 MACROECONOMIC OUTLOOK
- 5.15.1 NORTH AMERICA
- 5.15.2 EUROPE
- 5.15.3 ASIA PACIFIC
- 5.15.4 MIDDLE EAST
- 5.15.5 LATIN AMERICA
- 5.15.6 AFRICA
- 5.16 IMPACT OF MEGATRENDS
- 5.16.1 DIGITAL TRANSFORMATION AND INDUSTRY 4.0
- 5.16.2 SUSTAINABILITY AND GREEN AVIATION
- 5.16.3 AUTONOMOUS AND URBAN AIR MOBILITY
- 5.17 BUSINESS MODELS
6 AI IN AVIATION MARKET, BY BUSINESS FUNCTION
- 6.1 INTRODUCTION
- 6.2 FLIGHT OPERATIONS
- 6.2.1 FOCUS ON STREAMLINING DECISION-MAKING AND ENHANCING SITUATIONAL AWARENESS
- 6.2.1.1 Use case: Etihad Airways uses Google Cloud AI to enhance route planning
- 6.2.1.2 Use case: British Airways applies AI to predict congestion delays
- 6.2.1.3 Use case: Singapore Airlines employs AI to monitor and mitigate in-flight track deviations
- 6.2.2 CREW SCHEDULING & DUTY TIME OPTIMIZATION
- 6.2.3 ROUTE PLANNING & AIRSPACE OPTIMIZATION
- 6.2.4 FUEL EFFICIENCY MODELING
- 6.2.5 WEATHER DISRUPTION REROUTING
- 6.2.6 ON-TIME PERFORMANCE MONITORING
- 6.3 MAINTENANCE & SAFETY
- 6.3.1 NEED TO ENSURE OPERATIONAL CONTINUITY AND REGULATORY COMPLIANCE
- 6.3.1.1 Use case: Lufthansa Technik offers AVIATAR platform for operational efficiency
- 6.3.1.2 Use case: Air New Zealand implements AI-powered computer vision to automate aircraft inspections
- 6.3.1.3 Use case: Rolls-Royce employs AI in engine health monitoring
- 6.3.2 PREDICTIVE MAINTENANCE
- 6.3.3 VISUAL INSPECTION & DEFECT DETECTION
- 6.3.4 FAULT ISOLATION & DIAGNOSTICS
- 6.3.5 SPARE PARTS DEMAND FORECASTING
- 6.3.6 WARRANTY CLAIM OPTIMIZATION
- 6.3.7 REAL-TIME AIRCRAFT HEALTH MONITORING
- 6.3.8 AUTOMATED TROUBLESHOOTING & DIGITAL WORKFLOWS
- 6.4 AIRPORT OPERATIONS & GROUND HANDLING
- 6.4.1 INCREASE IN GLOBAL AIR TRAVEL AND CONGESTION
- 6.4.1.1 Use case: Changi Airport uses AI to optimize passenger flow management
- 6.4.1.2 Use case: Heathrow Airport deploys AI for baggage tracking and rerouting
- 6.4.1.3 Use case: Zurich Airport implements computer vision AI for tarmac safety
- 6.4.2 PASSENGER FLOW PREDICTION & CHECK-IN ALLOCATION
- 6.4.3 BAGGAGE TRACKING & REROUTING
- 6.4.4 TARMAC SAFETY REROUTING
- 6.4.5 GATE & GSE ASSIGNMENT OPTIMIZATION
- 6.4.6 AIRSIDE CONGESTION FORECASTING
- 6.5 PASSENGER EXPERIENCE & SERVICE
- 6.5.1 PASSENGER DEMAND FOR CONVENIENCE AND TRANSPARENCY
- 6.5.1.1 Use case: All Nippon Airways employs NLP-driven chatbots to handle passenger inquiries
- 6.5.1.2 Use case: KLM deploys AI-based real-time feedback analytics to enhance service recovery
- 6.5.1.3 Use case: Air France uses AI video analytics for risk monitoring
- 6.5.2 NLP-BASED VIRTUAL ASSISTANTS
- 6.5.3 EMOTION RECOGNITION & SENTIMENT ANALYSIS
- 6.5.4 REAL-TIME FEEDBACK ANALYTICS
- 6.5.5 PERSONALIZED IN-FLIGHT EXPERIENCE
- 6.5.6 PASSENGER SAFETY & RISK MONITORING
- 6.6 REVENUE MANAGEMENT
- 6.6.1 IMPLEMENTATION OF REAL-TIME FORECASTING AND DYNAMIC PRICING STRATEGIES
- 6.6.1.1 Use case: Lufthansa Group deploys AI-based fare optimization engines to adjust ticket pricing
- 6.6.1.2 Use case: American Airlines uses predictive AI models for route-level demand
- 6.6.1.3 Use case: Ryanair integrates AI to upsell baggage and ancillary products
- 6.6.2 DYNAMIC FARE OPTIMIZATION
- 6.6.3 ROUTE-LEVEL DEMAND FORECASTING
- 6.6.4 ANCILLARY REVENUE OPTIMIZATION
- 6.6.5 REVENUE LEAKAGE DETECTION
- 6.7 TRAINING & HUMAN CAPITAL
- 6.7.1 COMPLIANCE WITH EVOLVING AVIATION REGULATIONS
- 6.7.1.1 Use case: CAE integrates AI into full-flight simulators to enhance pilot training
- 6.7.1.2 Use case: Singapore Airlines adopts AI-based speech analysis to improve communication training
- 6.7.1.3 Use case: JetBlue uses predictive fatigue management tools to support crew wellness
- 6.7.2 AI-ENHANCED FLIGHT SIMULATION TRAINING
- 6.7.3 CABIN CREW CRM & LANGUAGE TRAINING AI
- 6.7.4 FATIGUE & WELLNESS PREDICTION
- 6.8 R&D & PRODUCT DEVELOPMENT
- 6.8.1 ESCALATING DEMAND FOR LIGHTER, GREENER, AND MORE EFFICIENT AIRCRAFT
- 6.8.1.1 Use case: Airbus incorporates AI into simulation frameworks to accelerate aircraft prototyping
- 6.8.1.2 Use case: Boeing leverages AI to reduce aircraft structural weight
- 6.8.1.3 Use case: Textron employs AI for component testing
- 6.8.2 DIGITAL TWIN MODELING FOR PROTOTYPING
- 6.8.3 AI FOR STRUCTURAL DESIGN & PERFORMANCE SIMULATION
- 6.8.4 AI-POWERED COMPONENT TESTING & QUALIFICATION
- 6.9 SUSTAINABILITY & EMISSION MANAGEMENT
- 6.9.1 REGULATORY PUSH FOR DECARBONIZATION
- 6.9.1.1 Use case: Heathrow Airport employs AI to optimize taxi time
- 6.9.1.2 Use case: Air France employs AI-based tools to monitor emissions
- 6.9.1.3 Use case: Lufthansa Group implements AI for taxi optimization and green routing
- 6.9.2 TAXI OPTIMIZATION & GREEN ROUTING
- 6.9.3 CARBON MONITORING & ESG REPORTING
7 AI IN AVIATION MARKET, BY SOLUTION
- 7.1 INTRODUCTION
- 7.2 INFRASTRUCTURE
- 7.2.1 SURGE IN INVESTMENTS DUE TO INCREASED COMPLEXITY OF AI
- 7.2.2 COMPUTER HARDWARE
- 7.2.3 MEMORY & STORAGE
- 7.2.4 NETWORKING
- 7.3 SOFTWARE
- 7.3.1 EMPHASIS ON SAFETY, EXPLAINABILITY, AND REAL-TIME RESPONSIVENESS
- 7.3.2 AI DEVELOPMENT TOOLS
- 7.3.3 AI APPLICATION PLATFORMS
- 7.4 SERVICES
- 7.4.1 SHIFT TOWARD OUTCOME-BASED DEPLOYMENTS IN AVIATION INDUSTRY
- 7.4.2 CORE DATA SERVICES
- 7.4.3 INTEGRATED SERVICES
8 AI IN AVIATION MARKET, BY TECHNOLOGY
- 8.1 INTRODUCTION
- 8.2 MACHINE LEARNING
- 8.2.1 EXTENSIVE USE IN FLIGHT DELAY PREDICTION AND ANOMALY DETECTION
- 8.2.2 SUPERVISED LEARNING
- 8.2.3 UNSUPERVISED LEARNING
- 8.2.4 REINFORCEMENT LEARNING
- 8.3 NATURAL LANGUAGE PROCESSING
- 8.3.1 IMPROVED OPERATIONS DUE TO VOICE-ENABLED INTERFACES AND TEXT AUTOMATION
- 8.4 COMPUTER VISION
- 8.4.1 ABILITY TO INTERPRET AND ACT ON VISUAL INPUTS FROM AIR AND GROUND SYSTEMS
- 8.4.2 OBJECT DETECTION
- 8.4.3 FACIAL RECOGNITION
- 8.4.4 OTHERS
- 8.5 GENERATIVE AI
- 8.5.1 CRITICAL ROLE IN TRAINING AND AUTOMATION
- 8.6 SENSOR FUSION AI
- 8.6.1 MULTI-SENSOR INTEGRATION TO ENHANCE SITUATIONAL AWARENESS
9 AI IN AVIATION MARKET, BY END USER
- 9.1 INTRODUCTION
- 9.2 AIRLINES & OPERATORS
- 9.2.1 SIGNIFICANT IMPACT OF AI ON COST SAVINGS AND OPERATIONAL EFFICIENCY
- 9.3 AIRPORT AUTHORITIES
- 9.3.1 REGULATORY PUSH FOR SECURITY AND EMPHASIS ON PASSENGER SATISFACTION
- 9.4 AIRCRAFT MANUFACTURERS & SYSTEM INTEGRATORS
- 9.4.1 INCLINATION TOWARD REAL-TIME DECISION-MAKING AND AUTOMATED FLIGHT OPERATIONS
- 9.5 MRO PROVIDERS
- 9.5.1 REDUCED AIRCRAFT-ON-GROUND TIME AND SPARE INVENTORY COSTS
10 AI IN AVIATION MARKET, BY REGION
- 10.1 INTRODUCTION
- 10.2 NORTH AMERICA
- 10.2.1 PESTLE ANALYSIS
- 10.2.2 US
- 10.2.2.1 Focus on operational efficiency and cost savings to drive market
- 10.2.3 CANADA
- 10.2.3.1 Rapid adoption of AI to promote sustainability to drive market
- 10.3 EUROPE
- 10.3.1 PESTLE ANALYSIS
- 10.3.2 UK
- 10.3.2.1 Strong regulatory framework and advanced aviation infrastructure to drive market
- 10.3.3 FRANCE
- 10.3.3.1 Targeted investments from government and industry leaders to drive market
- 10.3.4 GERMANY
- 10.3.4.1 Increased adoption of AI to enhance automation across airports and airlines to drive market
- 10.3.5 ITALY
- 10.3.5.1 Compliance with European Union's digital and environmental goals to drive market
- 10.3.6 SPAIN
- 10.3.6.1 Need to manage high passenger volumes to drive market
- 10.3.7 REST OF EUROPE
- 10.4 ASIA PACIFIC
- 10.4.1 PESTLE ANALYSIS
- 10.4.2 CHINA
- 10.4.2.1 Favorable government policies to drive market
- 10.4.3 INDIA
- 10.4.3.1 Rise in passenger traffic and rapid digitalization to drive market
- 10.4.4 JAPAN
- 10.4.4.1 Domestic push for safety and enhanced passenger experience to drive market
- 10.4.5 SOUTH KOREA
- 10.4.5.1 Focus on smart airport infrastructure and traffic management to drive market
- 10.4.6 NEW ZEALAND
- 10.4.6.1 Rising government investments to drive market
- 10.4.7 AUSTRALIA
- 10.4.7.1 Robust infrastructure and strong government backing to drive market
- 10.4.8 REST OF ASIA PACIFIC
- 10.5 MIDDLE EAST
- 10.5.1 PESTLE ANALYSIS
- 10.5.2 SAUDI ARABIA
- 10.5.2.1 Aviation modernization initiatives under Vision 2030 to drive market
- 10.5.3 UAE
- 10.5.3.1 Substantial investments in AI to elevate airport efficiency to drive market
- 10.5.4 TURKEY
- 10.5.4.1 Rapid integration of AI across aerospace operations to drive market
- 10.5.5 REST OF MIDDLE EAST
- 10.6 LATIN AMERICA
- 10.6.1 PESTLE ANALYSIS
- 10.6.2 BRAZIL
- 10.6.2.1 Presence of major aircraft manufacturers and airports to drive market
- 10.6.3 MEXICO
- 10.6.3.1 Ongoing modernization of airspace infrastructure to drive market
- 10.6.4 CHILE
- 10.6.4.1 Rapid integration of AI for dynamic positioning and demand forecasting to drive market
- 10.6.5 ARGENTINA
- 10.6.5.1 Advancements in predictive maintenance to drive market
- 10.6.6 REST OF LATIN AMERICA
- 10.7 AFRICA
- 10.7.1 PESTLE ANALYSIS
- 10.7.2 SOUTH AFRICA
- 10.7.2.1 Emphasis on strengthening national airspace capacity to drive market
- 10.7.3 EGYPT
- 10.7.3.1 Integration of AI across airport operations and airline management to drive market
- 10.7.4 REST OF AFRICA
11 COMPETITIVE LANDSCAPE
- 11.1 INTRODUCTION
- 11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2020-2024
- 11.3 REVENUE ANALYSIS, 2021-2024
- 11.4 MARKET SHARE ANALYSIS, 2024
- 11.5 BRAND/PRODUCT COMPARISON
- 11.6 COMPANY VALUATION AND FINANCIAL METRICS
- 11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2024
- 11.7.1 STARS
- 11.7.2 EMERGING LEADERS
- 11.7.3 PERVASIVE PLAYERS
- 11.7.4 PARTICIPANTS
- 11.7.5 COMPANY FOOTPRINT
- 11.7.5.1 Company footprint
- 11.7.5.2 Region footprint
- 11.7.5.3 Solution footprint
- 11.7.5.4 Technology footprint
- 11.7.5.5 Business function footprint
- 11.8 COMPANY EVALUATION MATRIX: START-UPS/SMES, 2024
- 11.8.1 PROGRESSIVE COMPANIES
- 11.8.2 RESPONSIVE COMPANIES
- 11.8.3 DYNAMIC COMPANIES
- 11.8.4 STARTING BLOCKS
- 11.8.5 COMPETITIVE BENCHMARKING
- 11.8.5.1 List of start-ups/SMEs
- 11.8.5.2 Competitive benchmarking of start-ups/SMEs
- 11.9 COMPETITIVE SCENARIO
- 11.9.1 PRODUCT LAUNCHES
- 11.9.2 DEALS
- 11.9.3 OTHERS
12 COMPANY PROFILES
- 12.1 KEY PLAYERS
- 12.1.1 AMADEUS IT GROUP S.A.
- 12.1.1.1 Business overview
- 12.1.1.2 Products offered
- 12.1.1.3 Recent developments
- 12.1.1.4 MnM view
- 12.1.1.4.1 Right to win
- 12.1.1.4.2 Strategic choices
- 12.1.1.4.3 Weaknesses and competitive threats
- 12.1.2 HONEYWELL INTERNATIONAL INC.
- 12.1.2.1 Business overview
- 12.1.2.2 Products offered
- 12.1.2.3 Recent developments
- 12.1.2.3.1 Product launches
- 12.1.2.3.2 Deals
- 12.1.2.3.3 Others
- 12.1.2.4 MnM view
- 12.1.2.4.1 Right to win
- 12.1.2.4.2 Strategic choices
- 12.1.2.4.3 Weaknesses and competitive threats
- 12.1.3 MICROSOFT
- 12.1.3.1 Business overview
- 12.1.3.2 Products offered
- 12.1.3.3 Recent developments
- 12.1.3.3.1 Deals
- 12.1.3.3.2 Others
- 12.1.3.4 MnM view
- 12.1.3.4.1 Right to win
- 12.1.3.4.2 Strategic choices
- 12.1.3.4.3 Weaknesses and competitive threats
- 12.1.4 AMAZON WEB SERVICES, INC. (AWS)
- 12.1.4.1 Business overview
- 12.1.4.2 Products offered
- 12.1.4.3 Recent developments
- 12.1.4.4 MnM view
- 12.1.4.4.1 Right to win
- 12.1.4.4.2 Strategic choices
- 12.1.4.4.3 Weaknesses and competitive threats
- 12.1.5 GENERAL ELECTRIC COMPANY
- 12.1.5.1 Business overview
- 12.1.5.2 Products offered
- 12.1.5.3 Recent developments
- 12.1.5.4 MnM view
- 12.1.5.4.1 Right to win
- 12.1.5.4.2 Strategic choices
- 12.1.5.4.3 Weaknesses and competitive threats
- 12.1.6 COLLINS AEROSPACE
- 12.1.6.1 Business overview
- 12.1.6.2 Products offered
- 12.1.7 SITA
- 12.1.7.1 Business overview
- 12.1.7.2 Products offered
- 12.1.7.3 Recent developments
- 12.1.7.3.1 Product launches
- 12.1.7.3.2 Deals
- 12.1.8 PALANTIR TECHNOLOGIES INC.
- 12.1.8.1 Business overview
- 12.1.8.2 Products offered
- 12.1.8.3 Recent developments
- 12.1.9 LUFTHANSA TECHNIK
- 12.1.9.1 Business overview
- 12.1.9.2 Products offered
- 12.1.9.3 Recent developments
- 12.1.10 THALES
- 12.1.10.1 Business overview
- 12.1.10.2 Products offered
- 12.1.10.3 Recent developments
- 12.1.11 IBM CORPORATION
- 12.1.11.1 Business overview
- 12.1.11.2 Products offered
- 12.1.11.3 Recent developments
- 12.1.12 ACCENTURE
- 12.1.12.1 Business overview
- 12.1.12.2 Products offered
- 12.1.12.3 Recent developments
- 12.1.13 RAMCO SYSTEMS
- 12.1.13.1 Business overview
- 12.1.13.2 Products offered
- 12.1.13.3 Recent developments
- 12.1.13.3.1 Product launches
- 12.1.13.3.2 Others
- 12.1.14 TATA CONSULTANCY SERVICES LIMITED (TCS)
- 12.1.14.1 Business overview
- 12.1.14.2 Products offered
- 12.1.14.3 Recent developments
- 12.1.14.3.1 Deals
- 12.1.14.3.2 Others
- 12.1.15 WIPRO
- 12.1.15.1 Business overview
- 12.1.15.2 Products offered
- 12.1.15.3 Recent developments
- 12.1.15.3.1 Deals
- 12.1.15.3.2 Others
- 12.1.16 INFOSYS LIMITED
- 12.1.16.1 Business overview
- 12.1.16.2 Products offered
- 12.1.16.3 Recent developments
- 12.2 OTHER PLAYERS
- 12.2.1 AVATHON, INC.
- 12.2.2 ELENIUM AUTOMATION
- 12.2.3 ASSAIA INTERNATIONAL LTD.
- 12.2.4 OPTYM
- 12.2.5 EMBROSS
- 12.2.6 SYNAPTIC AVIATION
- 12.2.7 AEROCLOUD SYSTEMS LTD.
- 12.2.8 AIRNGURU S.A.
- 12.2.9 GRAYMATTER SOFTWARE SERVICES PVT LTD
- 12.2.10 DEDRONE
13 APPENDIX
- 13.1 DISCUSSION GUIDE
- 13.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
- 13.3 CUSTOMIZATION OPTIONS
- 13.4 RELATED REPORTS
- 13.5 AUTHOR DETAILS