Global AI in Hospitality Market

Report ID : 1322 | Published : 2022-07-29 | Pages: | Format: PDF/EXCEL

The market size of the Global AI in Hospitality Market is predicted to show an 11.26% CAGR during the projected period.

As artificial intelligence (AI) advances, it becomes increasingly attractive and reliable as a commercial solution. AI is used by companies in the travel and hospitality industry to carry out a range of administrative and customer support activities. Most resorts and hotels rely heavily on offering top-notch customer service to establish their reputations. AI technology can help in many ways, including tailoring recommendations, enhancing personalization, and ensuring quick response times even when executives or staff are not present. The consequences of COVID-19 on the hospitality industry, the need to prevent human-to-human contact, and labour shortages have made incorporating robots into hotels and restaurants more critical. The industry is growing with the spike in demand for real-time improved guest experience management. The employment of cutting-edge technology in the hospitality sector is promoting industrial growth. To increase their security and level of hotel management, many hotels are choosing integrated security solutions, including access control, video surveillance, and emergency incident management systems. This, in turn, supports the market growth of the AI-based hotel industry. Market expansion is quickening due to low operating costs and alluring revenue growth. As IoT and energy management technologies gain traction, the industry is growing.

The sturdy initial implementation costs are impeding the market's capacity to grow. Industry expansion is being hampered by challenging integration across outdated networks and systems. The lack of technically qualified workers hampers the contraction of the market. Worldwide risks of digital data theft and personal data leaks are causing concern among hoteliers. AI-based hospitality solutions consider the guest's preferences and private information. Any data leak could have legal ramifications and undermine the hotel chain's reputation.

Market Segmentation:

The AI in Hospitality Market is segmented on the basis of Technology, Hospitality Type, and Application. Based on Technology, the market is segmented as Machine Learning, Natural Language Processing, Chatbots or Travel bots, Blockchain, Big Data, Virtual Assistants, and Others. Based on Hospitality Type, the market is segmented as Food & Beverage, Lodging-Accommodation, and Others. Based on Application, the market is segmented as Customer Purchases, Travel Choices, Restaurants, Entertainment, Journey Patterns & Itinerary, and Others (Hotel Rating Inquiries, Payment Methods, Smart Controls).

Based on Technology, the Natural Language Processing segment is accounted as a major contributor to the AI in Hospitality Market.

Natural Language Processing is an important industry trending technology. The main benefits provided by these technologies include increased user experience, improved problem-solving abilities, improved customer contact, and advanced comprehension. Natural language processing is anticipated to be driven by rising demand for cloud-based NLP solutions to lower overall costs and improve scalability, as well as increasing smart device usage to support smart environments. Opportunities for NLP providers are anticipated as NLP-based solutions become more widely used across industries to improve customer experiences and as investments in the healthcare sector rise. The market for natural language processing is expanding due to the rising need for advanced text analytics, as well as the increasing use of the internet and linked devices.

Based on Application, the restaurants segment is accounted as a major contributor to the AI in Hospitality Market.

One important Application is restaurants, among others. The market will increase as a result of increasing customer and provider preference for technologically enhanced service solutions. The preference to minimize social contact and maintain social distance to prevent the spread of viruses will probably encourage industry penetration in hotels and restaurants. Additional advantages of adopting artificial intelligence in this sector include enhanced consumer behaviour, patterns, and input analytics. Resource usage will be optimized, and waste will be minimized with a choice that is more well-informed and based on real-time data and analytics. The global restaurant sector is expanding as a result of shifting consumer preferences and a rise in favourable global spending patterns. The discretionary income of those with higher propensities to spend on opulent habits, such as eating out, has increased.

The North America AI in Hospitality Market holds a significant revenue share in the region.

North America will likely dominate the market and will account for more than half of the global market in 2021. Regional growth has been driven by the abundance of suppliers and the swift uptake of digital technologies in consumer-focused firms. Major hotels in countries like the U.S. and Canada have already implemented AI-based hospitality management systems to boost tourist engagement. Market expansion will be aided by the deployment of artificial intelligence to enhance user interface and experience. Additionally, as more large hotel operators in this region adopt cutting-edge Technology like automation, artificial intelligence, and others, the market for AI-based hospitality management in North America is growing.

Competitive Landscape

Some of the major key players in the AI in the Hospitality Market are IBM, KLM Airlines, Lola, Altexsoft, Hilton, Infosys, Cvent, Amadeus IT, Lemax, Sabre Corporation, Tramada System, mTrip, CRS Technologies, Qtech Software, and Navitaire.

Chapter 1. Methodology and Scope

1.1. Research Methodology

1.2. Research Scope & Assumptions

Chapter 2. Executive Summary

Chapter 3. Global AI in the Hospitality Market Snapshot

Chapter 4. Global AI in the Hospitality 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 Technology Estimates & Trend Analysis

5.1. By Technology & Market Share, 2020 & 2030

5.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Technology:

5.2.1. Machine Learning

5.2.2. Natural Language Processing

5.2.3. Chatbots or Travel bots

5.2.4. Blockchain

5.2.5. Big Data

5.2.6. Virtual Assistants

5.2.7. Others  

Chapter 6. Market Segmentation 2: By Hospitality Type Estimates & Trend Analysis

6.1. By Hospitality Type & Market Share, 2020 & 2030

6.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Hospitality Type:

6.2.1. Food & Beverage

6.2.2. Lodging-Accommodation

6.2.3. Others

Chapter 7. Market Segmentation 3: By Application Estimates & Trend Analysis

7.1. By Application & Market Share, 2020 & 2030

7.2. Market Size (Value US$ Mn) & Forecasts and Trend Analyses, 2020 to 2030 for the following By Application:

7.2.1. Customer Purchases

7.2.2. Travel Choices

7.2.3. Restaurants

7.2.4. Entertainment

7.2.5. Journey Patterns & Itinerary

7.2.6. Others (Payment Methods, Smart Controls, Hotel Rating Inquiries)

Chapter 8. AI in the Hospitality Market Segmentation 4: Regional Estimates & Trend Analysis

8.1. North America

8.1.1. North America AI in the Hospitality Market revenue (US$ Million) estimates and forecasts By Technology, 2020-2030

8.1.2. North America AI in the Hospitality Market revenue (US$ Million) estimates and forecasts By Hospitality Type, 2020-2030

8.1.3. North America AI in the Hospitality Market revenue (US$ Million) estimates and forecasts by Application, 2020-2030

8.1.4. North America AI in the Hospitality Market revenue (US$ Million) estimates and forecasts by country, 2020-2030

8.2. Europe

8.2.1. Europe AI in the Hospitality Market revenue (US$ Million) By Technology, 2020-2030

8.2.2. Europe AI in the Hospitality Market revenue (US$ Million) By Hospitality Type, 2020-2030

8.2.3. Europe AI in the Hospitality Market revenue (US$ Million) estimates and forecasts by Application, 2020-2030

8.2.4. Europe AI in the Hospitality Market revenue (US$ Million) by country, 2020-2030

8.3. Asia Pacific

8.3.1. Asia Pacific AI in the Hospitality Market revenue (US$ Million) By Technology, 2020-2030

8.3.2. Asia Pacific AI in the Hospitality Market revenue (US$ Million) By Hospitality Type, 2020-2030

8.3.3. Asia Pacific AI in the Hospitality Market revenue (US$ Million) estimates and forecasts by Application, 2020-2030

8.3.4. Asia Pacific AI in the Hospitality Market revenue (US$ Million) by country, 2020-2030

8.4. Latin America

8.4.1. Latin America AI in the Hospitality Market revenue (US$ Million) By Technology, (US$ Million)

8.4.2. Latin America AI in the Hospitality Market revenue (US$ Million) By Hospitality Type, (US$ Million)

8.4.3. Latin America AI in the Hospitality Market revenue (US$ Million) estimates and forecasts by Application, 2020-2030

8.4.4. Latin America AI in the Hospitality Market revenue (US$ Million) by country, 2020-2030

8.5. Middle East & Africa

8.5.1. Middle East & Africa AI in the Hospitality Market revenue (US$ Million) By Technology, (US$ Million)

8.5.2. Middle East & Africa AI in the Hospitality Market revenue (US$ Million) By Hospitality Type, (US$ Million)

8.5.3. Middle East & Africa AI in the Hospitality Market revenue (US$ Million) estimates and forecasts by Application, 2020-2030

8.5.4. Middle East & Africa AI in the Hospitality Market revenue (US$ Million) by country, 2020-2030

Chapter 9. Competitive Landscape

9.1. Major Mergers and Acquisitions/Strategic Alliances

9.2. Company Profiles

9.2.1. IBM

9.2.2. KLM Airlines

9.2.3. Lola

9.2.4. Altexsoft

9.2.5. Hilton

9.2.6. Infosys

9.2.7. Cvent

9.2.8. Amadeus IT

9.2.9. Lemax

9.2.10. Sabre Corporation

9.2.11. Tramada System,

9.2.12. mTrip

9.2.13. CRS Technologies

9.2.14. Qtech Software

9.2.15. Navitaire

Other Prominent Players

Segmentation of AI in the Hospitality Market-

By Technology

  • Machine Learning
  • Natural Language Processing
  • Chatbots or Travel bots
  • Blockchain
  • Big Data
  • Virtual Assistants
  • Others

By Hospitality Type

  • Food & Beverage
  • Lodging-Accommodation
  • Others

By Application

  • Customer Purchases
  • Travel Choices
  • Restaurants
  • Entertainment
  • Journey Patterns & Itinerary
  • Others (Payment Methods, Smart Controls, Hotel Rating Inquiries)

By Region-

North America-

  • The US
  • Canada
  • Mexico

Europe-

  • Germany
  • The UK
  • France
  • Italy
  • Spain
  • Rest of Europe

Asia-Pacific-

  • China
  • Japan
  • India
  • South Korea
  • South East Asia
  • Rest of Asia Pacific

Latin America-

  • Brazil
  • Argentina
  • Rest of Latin America

 Middle East & Africa-

  • GCC Countries
  • South Africa

Rest of Middle East and Africa

InsightAce Analytic follows a standard and comprehensive market research methodology focused on offering the most accurate and precise market insights. The methods followed for all our market research studies include three significant steps – primary research, secondary research, and data modeling and analysis - to derive the current market size and forecast it over the forecast period. In this study, these three steps were used iteratively to generate valid data points (minimum deviation), which were cross-validated through multiple approaches mentioned below in the data modeling section.

Through secondary research methods, information on the market under study, its peer, and the parent market was collected. This information was then entered into data models. The resulted data points and insights were then validated by primary participants.

Based on additional insights from these primary participants, more directional efforts were put into doing secondary research and optimize data models. This process was repeated till all data models used in the study produced similar results (with minimum deviation). This way, this iterative process was able to generate the most accurate market numbers and qualitative insights.

Secondary research

The secondary research sources that are typically mentioned to include, but are not limited to:

  • Company websites, financial reports, annual reports, investor presentations, broker reports, and SEC filings.
  • External and internal proprietary databases, regulatory databases, and relevant patent analysis
  • Statistical databases, National government documents, and market reports
  • Press releases, news articles, and webcasts specific to the companies operating in the market

The paid sources for secondary research like Factiva, OneSource, Hoovers, and Statista

Primary Research:

Primary research involves telephonic interviews, e-mail interactions, as well as face-to-face interviews for each market, category, segment, and subsegment across geographies

The contributors who typically take part in such a course include, but are not limited to: 

  • Industry participants: CEOs, CBO, CMO, VPs, marketing/ type managers, corporate strategy managers, and national sales managers, technical personnel, purchasing managers, resellers, and distributors.
  • Outside experts: Valuation experts, Investment bankers, research analysts specializing in specific markets
  • Key opinion leaders (KOLs) specializing in unique areas corresponding to various industry verticals
  • End-users: Vary mainly depending upon the market

Data Modeling and Analysis:

In the iterative process (mentioned above), data models received inputs from primary as well as secondary sources. But analysts working on these models were the key. They used their extensive knowledge and experience about industry and topic to make changes and fine-tuning these models as per the product/service under study.

The standard data models used while studying this market were the top-down and bottom-up approaches and the company shares analysis model. However, other methods were also used along with these – which were specific to the industry and product/service under study.

To know more about the research methodology used for this study, kindly contact us/click here.

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11.26% CAGR during the projected period.

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