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세계의 여행 분야 머신러닝 시장 보고서(2025년)
Machine Learning In Travel Global Market Report 2025
상품코드 : 1888323
리서치사 : The Business Research Company
발행일 : On Demand Report
페이지 정보 : 영문 250 Pages
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한글목차

여행 분야 머신러닝 시장 규모는 최근 급속히 확대되고 있습니다. 2024년 32억 1,000만 달러로 평가되었고, 2025년에는 37억 8,000만 달러에 달할 것으로 추정되며, CAGR 17.9%로 성장이 전망되고 있습니다. 지난 수년간의 성장은 AI 기반 여행 비서 도입 확대, 수요 예측에 있어서 예측 분석 활용 증가, 고객 지원을 위한 챗봇의 통합 진전, 여행 제안의 개인화 강화, 예약 및 가격 설정 시스템의 자동화 진전 등이 요인으로 되어 있습니다.

여행 분야 머신러닝의 규모는 향후 수년간 급속한 성장이 예상됩니다. 2029년에는 72억 2,000만 달러에 달할 것으로 예측되며, CAGR 17.6%로 성장할 전망입니다. 예측 기간의 성장 요인으로는 부정 감지에서 머신러닝의 활용 확대, 동적 가격 설정에 대한 AI 도입 증가, 여행자 피드백용 감정 분석 툴의 보급 확대, 여행사에 의한 데이터 구동형 의사 결정 증가, 루트 및 스케줄 최적화에 대한 AI 활용 확대 등을 들 수 있습니다. 예측 기간 주요 동향으로는 개인화된 여행 계획을 위한 생성형 AI 진보, 자율형 여행 관리 시스템 개발, AI를 활용한 실시간 언어 번역의 혁신, 여행 인프라용 예지보전 기술의 진전, AI 구동형 가상 여행 비서의 개발 등이 있습니다.

개인화된 고객 경험에 대한 수요 급증은 커스터마이즈된 상호작용에 대한 고객 기대 증가로 시장 성장을 가속하고 있습니다. 개인화된 고객 경험에 대한 수요 증가는 향후 여행 분야 머신러닝 시장의 성장을 이끌 것으로 예측됩니다. 개인화된 고객 경험은 데이터 중심의 인사이트를 통해 개별적인 선호와 요구에 맞는 상호작용과 서비스를 제공하며 모든 접점에서 관련성이 높고 매력적인 경험을 실현하는 것을 말합니다. 고객의 디지털 연결성이 높아지고 브랜드가 자신의 선호도를 이해하며 맞춤형 솔루션을 제공할 것으로 기대됨에 따라 이 수요는 증가하고 있습니다. 여행 분야 머신러닝은 여행자의 데이터 및 행동을 분석하여 전체 여정을 통한 만족도와 참여도를 높이는 맞춤형 권장 사항, 동적 가격 설정, 개별화된 서비스를 제공하여 이러한 개인화를 가능하게 합니다. 예를 들어 2023년 1월에 영국에 본사를 둔 출판사 Marketing Tech News가 발표한 보고서에 따르면 세계 여행자의 약 66%가 여행 예약 시 개인화된 쿠폰을 받고, 약 61%의 소비자가 맞춤형 여행 경험에 대해 추가 요금을 지불할 의향이 있음이 밝혀졌습니다. 따라서 개인화된 고객 경험에 대한 수요 증가는 여행 분야 머신러닝의 성장을 가속할 것으로 예측됩니다.

여행 분야 머신러닝 시장에서 사업을 전개하는 주요 기업은 고객 참여, 업무 효율성 및 개인화된 여행 경험을 향상시키기 위해 에이전트형 AI 솔루션의 진화에 주력하고 있습니다. 에이전트형 AI 솔루션은 최소한의 인위적 개입으로 자율적인 의사결정 및 적응적인 행동을 하고 효과적으로 원하는 성과를 달성할 수 있는 첨단 인공지능 시스템입니다. 예를 들어, 2025년 9월에는 미국에 본사를 둔 기술 기업인 세이버 코퍼레이션이 독자적인 모델 컨텍스트 프로토콜(MCP) 서버를 탑재한 에이전트형 AI 대응 API군을 발표했습니다. 서브레모자이크 플랫폼에 통합되어 50페타바이트 이상의 여행 데이터를 활용하는 사브레 IQ 레이어가 지원하는 이러한 API를 통해 여행사는 AI 시스템을 연결하여 항공권 및 호텔 실시간 검색, 예약 및 예약 후 워크플로우를 실현할 수 있습니다. 이 혁신은 복잡한 여행 프로세스의 자동화와 대행사 및 고객에 대한 원활하고 개인화된 경험을 제공하는 에이전트 AI 애플리케이션이 확대되고 있음을 보여줍니다.

목차

제1장 주요 요약

제2장 시장 특징

제3장 시장 동향 및 전략

제4장 시장 : 금리, 인플레이션, 지정학, 무역전쟁 및 관세, 그리고 코로나 및 회복이 시장에 미치는 영향을 포함한 거시경제 시나리오

제5장 세계의 성장 분석 및 전략 분석 프레임워크

제6장 시장 세분화

제7장 지역별 및 국가별 분석

제8장 아시아태평양 시장

제9장 중국 시장

제10장 인도 시장

제11장 일본 시장

제12장 호주 시장

제13장 인도네시아 시장

제14장 한국 시장

제15장 서유럽 시장

제16장 영국 시장

제17장 독일 시장

제18장 프랑스 시장

제19장 이탈리아 시장

제20장 스페인 시장

제21장 동유럽 시장

제22장 러시아 시장

제23장 북미 시장

제24장 미국 시장

제25장 캐나다 시장

제26장 남미 시장

제27장 브라질 시장

제28장 중동 시장

제29장 아프리카 시장

제30장 경쟁 구도 및 기업 프로파일

제31장 기타 주요 기업 및 혁신 기업

제32장 세계 시장 경쟁 벤치마킹 및 대시보드

제33장 주요 인수합병(M&A)

제34장 최근 시장 동향

제35장 시장의 잠재력이 높은 국가, 부문 및 전략

제36장 부록

AJY
영문 목차

영문목차

Machine learning in the travel industry involves the application of advanced algorithms and data-driven models to process and analyze large volumes of travel-related information, identify patterns, and generate intelligent predictions or automated decisions without the need for explicit programming. It enables travel companies to better understand customer behavior, optimize pricing strategies, forecast travel demand, enhance operational efficiency, and deliver personalized experiences to travelers.

The key components of machine learning in travel include software, hardware, and services. This technology utilizes artificial intelligence and data analytics to improve travel operations, enhance customer experiences, and support strategic business decision-making. Deployment modes include on-premises and cloud-based solutions. Core applications encompass personalized recommendations, dynamic pricing, fraud detection, customer service optimization, and predictive analytics. The primary end users include travel agencies, airlines, car rental companies, online travel platforms, and other organizations operating within the travel ecosystem.

Note that the outlook for this market is being affected by rapid changes in trade relations and tariffs globally. The report will be updated prior to delivery to reflect the latest status, including revised forecasts and quantified impact analysis. The report's Recommendations and Conclusions sections will be updated to give strategies for entities dealing with the fast-moving international environment.

The rapid escalation of U.S. tariffs and the resulting trade tensions in spring 2025 are significantly impacting the information technology sector, particularly in hardware manufacturing, data infrastructure, and software deployment. Higher duties on imported semiconductors, circuit boards, and networking equipment have raised production and operational costs for tech firms, cloud service providers, and data centers. Companies relying on globally sourced components for laptops, servers, and consumer electronics are facing longer lead times and increased pricing pressures. In parallel, tariffs on specialized software tools and retaliatory measures from key international markets have disrupted global IT supply chains and reduced overseas demand for U.S.-developed technologies. To navigate these challenges, the sector is accelerating investments in domestic chip fabrication, diversifying supplier bases, and adopting AI-driven automation to enhance operational resilience and cost efficiency.

The machine learning in travel market research report is one of a series of new reports from The Business Research Company that provides machine learning in travel market statistics, including machine learning in travel industry global market size, regional shares, competitors with a machine learning in travel market share, detailed machine learning in travel market segments, market trends and opportunities, and any further data you may need to thrive in the machine learning in travel industry. This machine learning in travel market research report delivers a complete perspective of everything you need, with an in-depth analysis of the current and future scenario of the industry.

The machine learning in the travel market size has grown rapidly in recent years. It will grow from $3.21 billion in 2024 to $3.78 billion in 2025 at a compound annual growth rate (CAGR) of 17.9%. The growth in the historic period can be attributed to the increasing adoption of AI-based travel assistants, the growing use of predictive analytics for demand forecasting, the rising integration of chatbots for customer support, the increasing personalization in travel recommendations, and the growing automation in booking and pricing systems.

The machine learning in the travel market size is expected to see rapid growth in the next few years. It will grow to $7.22 billion in 2029 at a compound annual growth rate (CAGR) of 17.6%. The growth in the forecast period can be attributed to the rising use of machine learning for fraud detection, the growing implementation of AI in dynamic pricing, the increasing deployment of sentiment analysis tools for traveler feedback, the rise in data-driven decision-making by travel companies, and the growing utilization of AI for route and schedule optimization. Key trends in the forecast period include advancements in generative AI for personalized trip planning, the development of autonomous travel management systems, innovations in real-time language translation using AI, advancements in predictive maintenance for travel infrastructure, and the development of AI-driven virtual travel assistants.

The surge in demand for personalized customer experiences is fueling the growth of the market due to increasing customer expectations for tailored interactions. The growing demand for personalized customer experiences is expected to propel the growth of machine learning in the travel market going forward. Personalized customer experiences involve tailoring interactions and services to meet individual preferences and needs through data-driven insights that deliver relevant and engaging experiences across touchpoints. This demand is increasing as customers become more digitally connected and expect brands to understand their preferences and provide customized solutions. Machine learning in travel enables such personalization by analyzing traveler data and behavior to offer tailored recommendations, dynamic pricing, and customized services that enhance satisfaction and engagement throughout the journey. For instance, in January 2023, according to a report published by Marketing Tech News, a UK-based publishing company, about 66% of travelers globally preferred receiving personalized offers when booking trips, and around 61% of consumers worldwide were willing to pay extra for tailored travel experiences. Therefore, the growing demand for personalized customer experiences is expected to drive the growth of machine learning in the travel market.

Major companies operating in the machine learning in travel market are focusing on advancements in agentic AI solutions to enhance customer engagement, operational efficiency, and personalized travel experiences. Agentic AI solutions are advanced artificial intelligence systems capable of autonomous decision-making and adaptive behavior with minimal human intervention to achieve desired outcomes effectively. For instance, in September 2025, Sabre Corporation, a US-based technology company, launched a set of agentic AI-ready APIs powered by its proprietary Model Context Protocol (MCP) server. Integrated into the SabreMosaic platform and supported by the Sabre IQ layer leveraging over 50 petabytes of travel data, these APIs enable travel agencies to connect their AI systems for real-time shopping, booking, and post-booking workflows for flights and hotels. This innovation highlights the growing application of agentic AI in automating complex travel processes and delivering seamless, personalized experiences for agencies and customers.

In April 2023, Navan, Inc., a US-based technology company, acquired Tripeur for an undisclosed amount. This acquisition aimed to strengthen Navan's presence in the Indian business travel market by integrating Tripeur's advanced travel and expense management platform. It enhances Navan's localized offerings, leverages Tripeur's AI-driven automation capabilities, and provides a seamless, end-to-end travel experience for enterprises in the region. Tripeur is an India-based corporate travel management platform that provides machine learning solutions in the travel industry.

Major players in the machine learning in travel market are Amazon.com Inc., Microsoft Corporation, Hitachi Ltd., Accenture plc, International Business Machines Corporation, Oracle Corporation, Salesforce Inc. , SAP SE, Tata Consultancy Services Limited , NEC Corporation, Booking Holdings Inc., Tencent Holdings Limited , Infosys Limited, DXC Technology Company, Expedia Group Inc., Wipro Limited, Trip.com Group Limited, AMADEUS IT GROUP SOCIEDAD ANONIMA, LG CNS Co. Ltd., Sabre Corporation.

North America was the largest region in the machine learning in travel market in 2024. Asia-Pacific is expected to be the fastest-growing region in the forecast period. The regions covered in machine learning in travel report are Asia-Pacific, Western Europe, Eastern Europe, North America, South America, Middle East and Africa.

The countries covered in the machine learning in travel market report are Australia, Brazil, China, France, Germany, India, Indonesia, Japan, Russia, South Korea, UK, USA, Canada, Italy, Spain.

The machine learning in travel market consists of revenues earned by entities by providing services such as revenue management services, voice and language translation services, automated customer segmentation services, operational efficiency and route optimization services, and automated baggage handling services. The market value includes the value of related goods sold by the service provider or contained within the service offering. The machine learning in the travel market also includes kayak AI platform, mindtrip, sabre travel AI, citymapper, and navan concierge. Values in this market are 'factory gate' values; that is, the value of goods sold by the manufacturers or creators of the goods, whether to other entities (including downstream manufacturers, wholesalers, distributors, and retailers) or directly to end customers. The value of goods in this market includes related services sold by the creators of the goods.

The market value is defined as the revenues that enterprises gain from the sale of goods and/or services within the specified market and geography through sales, grants, or donations in terms of the currency (in USD unless otherwise specified).

The revenues for a specified geography are consumption values that are revenues generated by organizations in the specified geography within the market, irrespective of where they are produced. It does not include revenues from resales along the supply chain, either further along the supply chain or as part of other products.

Machine Learning In Travel Global Market Report 2025 from The Business Research Company provides strategists, marketers and senior management with the critical information they need to assess the market.

This report focuses on machine learning in travel market which is experiencing strong growth. The report gives a guide to the trends which will be shaping the market over the next ten years and beyond.

Reasons to Purchase

Where is the largest and fastest growing market for machine learning in travel ? How does the market relate to the overall economy, demography and other similar markets? What forces will shape the market going forward, including technological disruption, regulatory shifts, and changing consumer preferences? The machine learning in travel market global report from the Business Research Company answers all these questions and many more.

The report covers market characteristics, size and growth, segmentation, regional and country breakdowns, competitive landscape, market shares, trends and strategies for this market. It traces the market's historic and forecast market growth by geography.

The forecasts are made after considering the major factors currently impacting the market. These include the technological advancements such as AI and automation, Russia-Ukraine war, trade tariffs (government-imposed import/export duties), elevated inflation and interest rates.

Scope

Table of Contents

1. Executive Summary

2. Machine Learning In Travel Market Characteristics

3. Machine Learning In Travel Market Trends And Strategies

4. Machine Learning In Travel Market - Macro Economic Scenario Including The Impact Of Interest Rates, Inflation, Geopolitics, Trade Wars and Tariffs, And Covid And Recovery On The Market

5. Global Machine Learning In Travel Growth Analysis And Strategic Analysis Framework

6. Machine Learning In Travel Market Segmentation

7. Machine Learning In Travel Market Regional And Country Analysis

8. Asia-Pacific Machine Learning In Travel Market

9. China Machine Learning In Travel Market

10. India Machine Learning In Travel Market

11. Japan Machine Learning In Travel Market

12. Australia Machine Learning In Travel Market

13. Indonesia Machine Learning In Travel Market

14. South Korea Machine Learning In Travel Market

15. Western Europe Machine Learning In Travel Market

16. UK Machine Learning In Travel Market

17. Germany Machine Learning In Travel Market

18. France Machine Learning In Travel Market

19. Italy Machine Learning In Travel Market

20. Spain Machine Learning In Travel Market

21. Eastern Europe Machine Learning In Travel Market

22. Russia Machine Learning In Travel Market

23. North America Machine Learning In Travel Market

24. USA Machine Learning In Travel Market

25. Canada Machine Learning In Travel Market

26. South America Machine Learning In Travel Market

27. Brazil Machine Learning In Travel Market

28. Middle East Machine Learning In Travel Market

29. Africa Machine Learning In Travel Market

30. Machine Learning In Travel Market Competitive Landscape And Company Profiles

31. Machine Learning In Travel Market Other Major And Innovative Companies

32. Global Machine Learning In Travel Market Competitive Benchmarking And Dashboard

33. Key Mergers And Acquisitions In The Machine Learning In Travel Market

34. Recent Developments In The Machine Learning In Travel Market

35. Machine Learning In Travel Market High Potential Countries, Segments and Strategies

36. Appendix

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