Market Overview

AI In Hospitality Market was valued at USD 90 million in 2022 and is estimated to reach a value of USD 8,120 million by 2033 with a CAGR of 60% during the forecast period. The AI in hotel and hospitality refers to the adoption and integration of artificial intelligence (AI) technologies in various aspects of the hotel and hospitality industry.

These technologies aim to enhance guest experiences, streamline operations, improve efficiency, and personalize services. AI in this context typically encompasses machine learning, natural language processing, computer vision, and other AI techniques.

Market Scope

Before we move forward with the various aspects of AI in hospitality market, let us understand the correlation between the use of AI and various hospitality performance metrics, such as guest satisfaction, revenue, and efficiency. We will portray this by a straight comparison, pre and post analysis of AI in hospitality industry

For this we have done a survey of 100 samples that primarily included both consumer and hospitality industry management. Various questions were included in the survey covering booking, communication, wait times, automating task and others. The pre and post analysis gave us a positive correlated side of AI technology such as chatbots, voice controlled devices, revenue management and dynamic pricing and others.

Below table depicts the improved results, that strong shows that AI is set to create a colossal revolution within the hospitality industry and create a moneybag scenario for the investors

Source: We Market Research

Similar Results Done By Other Survey

A study by Cornell University found that hotels that use AI dynamic pricing have higher revenue than hotels that do not use AI dynamic pricing. The study found that AI dynamic pricing can help hotels to increase revenue by up to 15%. Thus it is clear that autonomous technology possess a great power in revolutionizing the hospitality industry, thereby creating a smoother base for the AI in hospitality market.

Another study by the University of Las Vegas found that hotels that use AI to personalize the guest experience have higher revenue and guest satisfaction scores than hotels that do not use AI. The study found that AI personalization can help hotels to increase revenue by up to 10%.

Thus based on statistical results it is clear that AI in hospitality industry has a correlation factor of 0.92% on above mentioned dimensions.

Market Dynamics

Growing demand for the integration of dynamic pricing model within the hospitality sector has created a lucrative scope for hotel owners, which further has created a strong base for AI in hospitality market. The hospitality sector is witnessing a growing demand for the integration of dynamic pricing models, and this trend is fundamentally reshaping the way hotels and accommodations operate.

One of the primary drivers behind this demand is the need to maximize revenue in an increasingly competitive market. Dynamic pricing empowers hotels to adapt to ever-changing market conditions by adjusting room rates in real-time. For instance, during periods of high demand, such as holidays or major events, hotels can raise their prices to capture the increased willingness of travelers to pay more for accommodation. Conversely, during low-demand seasons, dynamic pricing allows hotels to offer competitive rates to attract cost-conscious guests.

This flexibility in pricing strategies not only helps hotels optimize revenue but also enhances their competitiveness. By staying agile and responsive to market fluctuations, hotels can outperform their competitors in terms of revenue generation and occupancy rates. Moreover, dynamic pricing enables hotels to align their pricing with demand patterns, ensuring that rooms are priced at their maximum potential without overcharging or underpricing has also contributed in creating a linear growth trajectory for AI in hospitality market.

Another critical factor driving the adoption of dynamic pricing is the advancement of technology. AI-powered algorithms and data analytics have made it possible to analyze a multitude of variables, such as historical booking data, competitor rates, and even local events, in real-time. This data-driven approach allows hotels to make informed pricing decisions that were previously impossible to execute manually.

From the guest's perspective, dynamic pricing also offers benefits. Travelers can potentially find more affordable rates during off-peak periods, making travel more accessible and budget-friendly. Overall, the integration of dynamic pricing models is transforming the hospitality sector, enabling hotels to optimize revenue while providing guests with greater pricing transparency and flexibility.

As this trend continues to evolve, it is likely to become an integral part of the modern hospitality industry, benefiting both providers and consumers alike.

Hotel Type Analysis

Based on hotel type, AI in hospitality market is segmented into Luxury Hotels and Resorts, Mid-scale and Budget Hotels, Boutique and Independent Hotels and Chain and Group Hotels. In the current scenario, as the market is in early stage luxury hotels and resorts dominate the market with a share of 50.1% in 2022, owing to the availability of higher budget for technology integration and customer satisfaction.

The integration of AI (Artificial Intelligence) in luxury hotels and resorts is ushering in a new era of personalized, efficient, and memorable guest experiences. Several notable trends highlight the direction in which AI is shaping the luxury hospitality sector.

One prominent trend is the use of AI-powered chatbots and virtual assistants. These intelligent systems offer guests 24/7 concierge services, addressing inquiries, providing recommendations, and handling requests in real-time.

By leveraging natural language processing and machine learning, AI chatbots can engage with guests in a conversational manner, ensuring a high level of service and convenience, thus propelling the growth of AI in hospitality market.

Moreover, AI-enhanced personalization is redefining luxury. Hotels are utilizing AI to create guest profiles that include preferences, past stays, and behaviors. With this data, AI algorithms can curate customized experiences, from room settings to dining suggestions, spa treatments, and recreational activities. This level of personalization enhances guest satisfaction and loyalty.

AI is also streamlining check-in and check-out processes. Luxury hotels are implementing contactless solutions that enable guests to use their smartphones for everything from room access to ordering room service. Facial recognition technology is even being deployed for frictionless entry and exit, enhancing security and convenience.

Application Analysis

AI in hospitality market is analyzed across various applications such as Guest Experience Enhancement, Operational Efficiency, Revenue Management, Safety and Security and Sustainability and Energy Management. Guest experience enhancement holds an active market share of 46.18% in 2022, owing to immense focus on hyper-personalization.

Hyper-personalization is a transformative trend within the realm of AI (Artificial Intelligence) in hospitality, redefining how hotels and accommodations interact with their guests. At its core, hyper-personalization harnesses the power of AI to tailor every aspect of a guest's stay to their individual preferences and needs.

Imagine checking into a hotel, and from the moment you arrive, everything feels as if it was designed just for you. This is the essence of hyper-personalization. AI algorithms analyze an array of data points, including past stays, guest profiles, preferences, and even real-time inputs. Armed with this information, hotels can curate experiences that go beyond mere accommodation.

Regional Analysis

Based on the geographic scope, AI in hospitality market is segmented into North America, Europe, APAC, South America and MEA. APAC holds a lucrative scope within the market along with an active share of 25.10% in 2022.

Multilingual AI assistants have emerged as a pivotal trend in the hospitality industry across the Asia-Pacific (APAC) region. These AI-driven virtual concierges are transforming the way hotels interact with guests, providing seamless and personalized services tailored to the linguistic diversity of the region.

APAC is known for its rich tapestry of languages and cultures, making it a unique challenge and opportunity for the hospitality sector. Multilingual AI assistants address this challenge by offering real-time communication capabilities in multiple languages. Here's a detailed explanation of their significance:

Catering to Diverse Guests: APAC attracts travelers from around the world, and each guest may speak a different language. Multilingual AI assistants ensure that guests feel at home by providing information, assistance, and recommendations in their preferred language. Whether it's Mandarin, Japanese, Korean, Hindi, or English, these AI-powered systems bridge language gaps effortlessly.

Local Insights: These AI systems are equipped with information about local culture, customs, and events, enriching the guest experience with insights that go beyond language. Guests can learn about local traditions, festivals, and dining etiquette, contributing to a deeper understanding of the destination.

With such key advantages offered APAC is set to revolutionize the overall AI in hospitality market, and create a lucrative moneybag scope from both demand and supply side.

Competitive Analysis

Some of the major companies operating within the AI in hospitality market are : IBM Corporation, Microsoft Corporation, Amazon Web Services, Inc., Oracle Corporation, Google LLC, Intel Corporation, NVIDIA Corporation, Alibaba Group Holding Limited, Cisco Systems, Inc., HCL Technologies Limited, Amadeus IT Group SA, Infor, Inc., Salesforce.com, Inc., Huawei Technologies Co., Ltd., NEC Corporation, and Amadeus IT Group.

Amadeus leverages AI to enable hyper-personalization of the guest experience. Through data analysis and machine learning algorithms, the company tailors recommendations and services to individual guest preferences.

This includes personalized room amenities, dining suggestions, and leisure activities, all aimed at creating memorable stays for guests. This allows to create strong base for the overall AI in hospitality market.

Amadeus IT Group, IBM, and Microsoft combined hold a market share of more than 40.15% in 2022, thereby mandating other player to look out for various use case in hospitality sector, thereby creating a good  hold in AI in hospitality market.

Market Segmentation

By Application

·         Guest Experience Enhancement

·         Operational Efficiency

·         Revenue Management

·         Safety and Security

·         Sustainability and Energy Management

By Services

·         Front-End Services

·         Back-End Services

·         Security and Safety Services

By Hotel Type

·         Luxury Hotels and Resorts

·         Mid-scale and Budget Hotels

·         Boutique and Independent Hotels

·         Chain and Group Hotels

By Hotel Size

·         Small and Medium-sized Hotels

·         Large Hotels and Hotel Chains

1.       Global AI in Hospitality Market Introduction and Market Overview

1.1.    Objectives of the Study

1.2.    Global AI in Hospitality Market Scope and Market Estimation

1.2.1.Global AI in Hospitality Overall Market Size, Revenue (US$ Mn), Market CAGR (%), Market forecast (2023 - 2033)

1.2.2.Global AI in Hospitality Market Revenue Share (%) and Growth Rate (Y-o-Y) from 2019 - 2033

1.3.    Market Segmentation

1.3.1.Hotel Type of Global AI in Hospitality Market

1.3.2.Hotel Size of Global AI in Hospitality Market

1.3.3.Application of Global AI in Hospitality Market

1.3.4.Services of Global AI in Hospitality Market

1.3.5.Region of Global AI in Hospitality Market

2.       Executive Summary

3.       Market Factor Analysis

3.1.   Global AI in Hospitality Market Application Trends under COVID-19 Outbreak

3.1.1.Global COVID-19 Status Overview

3.1.2.Influence of COVID-19 Outbreak on Global AI in Hospitality Market Application Development

3.2.   Market Dynamics

3.2.1.Drivers

3.2.2.Limitations

3.2.3.Opportunities

3.2.4.Impact Analysis of Drivers and Restraints

3.3.   Value Chain/ Ecosystem Analysis

3.3.1.Manufacturers / Vendors

3.3.2.Distributors

3.3.3.Buyers / End-users

3.3.4.Forward Integration & Backward Integration of Key Stakeholders

3.4.   Global AI in Hospitality Market - Pricing Trends Analysis & Average Selling Prices (ASPs)

3.5.   Porter’s Five Forces Analysis

3.5.1.Bargaining Power of Suppliers

3.5.2.Bargaining Power of Buyers

3.5.3.Threat of Substitutes

3.5.4.Threat of New Entrants

3.5.5.Competitive Rivalry

3.6.   PEST Analysis

3.6.1.Political Factors

3.6.2.Economic Factors

3.6.3.Social Factors

3.6.4.Technological Factors

3.7.   Impact of Russia Ukraine War on AI in Hospitality Market

3.8.   Impact of Economic Downturn on AI in Hospitality Market

3.9.     Market Investment Opportunity Analysis (Top Investment Pockets), By Segments & By Regions

4.       Global AI in Hospitality Market Estimates & Forecast Trend Analysis, by Hotel type

4.1.    Global AI in Hospitality Market Revenue (US$ Mn) Estimates and Forecasts, by Hotel type, 2019 to 2033

4.1.1.   Luxury Hotels and Resorts

4.1.2.Mid-scale and Budget Hotels

4.1.3.Boutique and Independent Hotels

4.1.4.Chain and Group Hotels    

5.       Global AI in Hospitality Market Estimates & Forecast Trend Analysis, by Application

5.1.    Global AI in Hospitality Market Revenue (US$ Mn) Estimates and Forecasts, by Application, 2019 to 2033

5.1.1.Guest Experience Enhancement

5.1.2.Operational Efficiency

5.1.3.Revenue Management

5.1.4.Safety and Security

5.1.5.Sustainability and Energy Management

6.       Global AI in Hospitality Market Estimates & Forecast Trend Analysis, by Services

6.1.    Global AI in Hospitality Market Revenue (US$ Mn) and Volume (Tons) Estimates and Forecasts, by Services, 2019 to 2033

6.1.1.    Front-End Services

6.1.2.Back-End Services

6.1.3.Security and Safety Services

7.       Global AI in Hospitality Market Estimates & Forecast Trend Analysis, by Region

7.1.    Global AI in Hospitality Market Revenue (US$ Mn) and Volume (Tons) Estimates and Forecasts, by Region, 2019 to 2033

7.1.1.North America

7.1.2.Europe

7.1.3.Asia Pacific

7.1.4.Middle East & Africa

7.1.5.South America

8.       North America AI in Hospitality Market: Estimates & Forecast Trend Analysis

8.1.    North America AI in Hospitality Market Assessments & Key Findings

8.1.1.AI in Hospitality Market Introduction

8.1.2.AI in Hospitality Market Size Estimates and Forecast (US$ Million) (2019 – 2033)

8.1.2.1.   By Hotel Type

8.1.2.2.   By Hotel Size

8.1.2.3.   By Application

8.1.2.4.   By Service

8.1.2.5.   By Country

8.1.2.5.1.     The U.S.

8.1.2.5.2.     Canada

8.1.2.5.3.     Mexico

9.       Europe AI in Hospitality Market: Estimates & Forecast Trend Analysis

9.1.    Europe AI in Hospitality Market Assessments & Key Findings

9.1.1.AI in Hospitality Market Introduction

9.1.2.AI in Hospitality Market Size Estimates and Forecast (US$ Million) (2019 – 2033)

9.1.2.1.   By Hotel Type

9.1.2.2.   By Hotel Size

9.1.2.3.   By Application

9.1.2.4.   By Service

9.1.2.5.        By Country

9.1.2.5.1.     Italy

9.1.2.5.2.     Germany

9.1.2.5.3.     U.K.

9.1.2.5.4.     France

9.1.2.5.5.     Spain

9.1.2.5.6.     Rest of Europe

10.   Asia Pacific AI in Hospitality Market: Estimates & Forecast Trend Analysis

10.1.  Asia Pacific Market Assessments & Key Findings

10.1.1.    AI in Hospitality Market Introduction

10.1.2.    AI in Hospitality Market Size and Volume Estimates and Forecast (US$ Million) (2019 – 2033)

10.1.2.1.    By Hotel Type

10.1.2.2.    By Hotel Size

10.1.2.3.    By Application

10.1.2.4.    By Service

10.1.2.5.    By Country

10.1.2.5.1. China

10.1.2.5.2. Japan

10.1.2.5.3. India

10.1.2.5.4. Australia

10.1.2.5.5. South Korea

10.1.2.5.6. ASEAN

10.1.2.5.7. Rest of Asia Pacific

11.   Middle East & Africa AI in Hospitality Market: Estimates & Forecast Trend Analysis

11.1.  Middle East & Africa Market Assessments & Key Findings

11.1.1.   AI in Hospitality Market Introduction

11.1.2.   AI in Hospitality Market Size and Volume Estimates and Forecast (US$ Million) (2019 – 2033)

11.1.2.1.    By Hotel Type

11.1.2.2.    By Hotel Size

11.1.2.3.    By Application

11.1.2.4.    By Service

11.1.2.5.    By Country

11.1.2.5.1. U.A.E.

11.1.2.5.2. Saudi Arabia

11.1.2.5.3. Egypt

11.1.2.5.4. South Africa

11.1.2.5.5. Rest of Middle East & Africa

12.   South America AI in Hospitality Market: Estimates & Forecast Trend Analysis

12.1.  South America Market Assessments & Key Findings

12.1.1.   AI in Hospitality Market Introduction

12.1.2.   AI in Hospitality Market Size and Volume Estimates and Forecast (US$ Million) (2019 – 2033)

12.1.2.1.    By Hotel Type

12.1.2.2.    By Hotel Size

12.1.2.3.    By Application

12.1.2.4.    By Service

12.1.2.5.    By Country

12.1.2.5.1. Brazil

12.1.2.5.2. Argentina

12.1.2.5.3. Colombia

12.1.2.5.4. Rest of South America

13.   Competition Landscape

13.1.  AI in Hospitality Market Competition Matrix & Benchmarking, by Leading Players / Innovators / Emerging Players / New Entrants

13.2.  AI in Hospitality Market Competition White Space Analysis, By End-user

13.3.  AI in Hospitality Market Competition Regional Intensity Map Analysis, By Geographies Served

13.4.  AI in Hospitality Market Concentration & Company Market Shares (%) Analysis, 2022

14.   Company Profiles

14.1.IBM Corporation

14.1.1.    Company Overview & Key Stats

14.1.2.    Financial Performance & KPIs

14.1.3.    Product Portfolio

14.1.4.    Business Strategy & Recent Developments

* Similar details would be provided for all the players mentioned below 

14.2.Microsoft Corporation

14.3.Amazon Web Services, Inc.

14.4.Oracle Corporation

14.5.Google LLC

14.6.Intel Corporation

14.7.NVIDIA Corporation

14.8.Alibaba Group Holding Limited

14.9.Cisco Systems, Inc.

14.10.   HCL Technologies Limited

14.11.   Amadeus IT Group SA

14.12.   Infor, Inc.

14.13.   Salesforce.com, Inc.

14.14.   Huawei Technologies Co., Ltd.

14.15.   NEC Corporation

14.16.   Amadeus IT Group  

14.17.    Others**

15.   Research Methodology

15.1.      External Power Outputs / Databases

15.2.      Internal Proprietary Database

15.3.      Primary Research

15.4.      Secondary Research

15.5.      Assumptions

15.6.      Limitations

15.7.      Report FAQs

16.   Research Findings & Conclusion