AI in Hospitality and Tourism Market- By Type
AI in Hospitality and Tourism Market- By Application
AI in Hospitality and Tourism Market- By End User
AI in Hospitality and Tourism Market- By Region
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & Africa-
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global Artificial Intelligence in the Hospitality and Tourism Market Snapshot
Chapter 4. Global Artificial Intelligence in the Hospitality and Tourism Market Variables, Trends & Scope
4.1. Market Segmentation & Scope
4.2. Drivers
4.3. Challenges
4.4. Trends
4.5. Investment and Funding Analysis
4.6. Industry Analysis – Porter’s Five Forces Analysis
4.7. Competitive Landscape & Market Share Analysis
4.8. Impact of Covid-19 Analysis
Chapter 5. Market Segmentation 1: by Type Estimates & Trend Analysis
5.1. by Type & Market Share, 2024 & 2034
5.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Type:
5.2.1. Natural Language Processing (NLP) Applications
5.2.2. Machine Learning Algorithms
5.2.3. Computer Vision and Image Recognition
5.2.4. Chatbots and Virtual Assistants
5.2.5. Recommendation Systems
5.2.6. Sentiment Analysis
Chapter 6. Market Segmentation 2: by End-user Estimates & Trend Analysis
6.1. by End-user & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by End-user:
6.2.1. Hotels and Resorts
6.2.2. Airlines and Airports
6.2.3. Travel Agencies and Tour Operators
6.2.4. Restaurants and Food Service Providers
6.2.5. Cruise Lines and Maritime Tourism
6.2.6. Online Travel Platforms and Booking Websites
Chapter 7. Market Segmentation 3: by Application Estimates & Trend Analysis
7.1. by Application & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:
7.2.1. Customer Service and Support
7.2.2. Personalized Marketing and Advertising
7.2.3. Hotel and Room Booking Systems
7.2.4. Virtual Concierge Services
7.2.5. Smart Guest Room Automation
7.2.6. Data Analytics and Business Intelligence
7.2.7. Revenue Management and Pricing Optimization
Chapter 8. Artificial Intelligence in the Hospitality and Tourism Market Segmentation 4: Regional Estimates & Trend Analysis
8.1. North America
8.1.1. North America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.1.2. North America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by End-user, 2021-2034
8.1.3. North America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.1.4. North America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.2. Europe
8.2.1. Europe Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.2.2. Europe Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by End-user, 2021-2034
8.2.3. Europe Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.2.4. Europe Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.3. Asia Pacific
8.3.1. Asia Pacific Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.3.2. Asia Pacific Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by End-user, 2021-2034
8.3.3. Asia-Pacific Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.3.4. Asia Pacific Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.4. Latin America
8.4.1. Latin America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.4.2. Latin America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by End-user, 2021-2034
8.4.3. Latin America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.4.4. Latin America Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
8.5. Middle East & Africa
8.5.1. Middle East & Africa Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
8.5.2. Middle East & Africa Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by End-user, 2021-2034
8.5.3. Middle East & Africa Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
8.5.4. Middle East & Africa Artificial Intelligence in the Hospitality and Tourism Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 9. Competitive Landscape
9.1. Major Mergers and Acquisitions/Strategic Alliances
9.2. Company Profiles
9.2.1. IBM Corporation
9.2.2. Google LLC
9.2.3. Amazon Web Services (AWS)
9.2.4. Microsoft Corporation
9.2.5. Oracle Corporation
9.2.6. Salesforce.com, Inc.
9.2.7. SAP SE
9.2.8. Intel Corporation
9.2.9. NVIDIA Corporation
9.2.10. Alibaba Group Holding Limited
9.2.11. Huawei Technologies Co., Ltd.
9.2.12. Accenture PLC
9.2.13. Cisco Systems, Inc.
9.2.14. Travelport Worldwide Limited
9.2.15. Amadeus IT Group S.A.
9.2.16. Expedia Group, Inc.
9.2.17. Airbnb, Inc.
9.2.18. Tripadvisor, Inc.
9.2.19. Booking Holdings Inc.
9.2.20. Agoda Company Pte. Ltd.
9.2.21. Ctrip.com International, Ltd.
9.2.22. MakeMyTrip Limited
9.2.23. TripAdvisor, Inc.
9.2.24. Kayak Software Corporation
9.2.25. Trivago N.V.
9.2.26. Other Market Players
This study employed a multi-step, mixed-method research approach that integrates:
This approach ensures a balanced and validated understanding of both macro- and micro-level market factors influencing the market.
Secondary research for this study involved the collection, review, and analysis of publicly available and paid data sources to build the initial fact base, understand historical market behaviour, identify data gaps, and refine the hypotheses for primary research.
Secondary data for the market study was gathered from multiple credible sources, including:
These sources were used to compile historical data, market volumes/prices, industry trends, technological developments, and competitive insights.
Primary research was conducted to validate secondary data, understand real-time market dynamics, capture price points and adoption trends, and verify the assumptions used in the market modelling.
Primary interviews for this study involved:
Interviews were conducted via:
Primary insights were incorporated into demand modelling, pricing analysis, technology evaluation, and market share estimation.
All collected data were processed and normalized to ensure consistency and comparability across regions and time frames.
The data validation process included:
This ensured that the dataset used for modelling was clean, robust, and reliable.
The bottom-up approach involved aggregating segment-level data, such as:
This method was primarily used when detailed micro-level market data were available.
The top-down approach used macro-level indicators:
This approach was used for segments where granular data were limited or inconsistent.
To ensure accuracy, a triangulated hybrid model was used. This included:
This multi-angle validation yielded the final market size.
Market forecasts were developed using a combination of time-series modelling, adoption curve analysis, and driver-based forecasting tools.
Given inherent uncertainties, three scenarios were constructed:
Sensitivity testing was conducted on key variables, including pricing, demand elasticity, and regional adoption.