Global AI in Customer Experience Market Size is valued at USD 11.9 Bn in 2024 and is predicted to reach USD 117.8 Bn by the year 2034 at a 26.0% CAGR during the forecast period for 2025-2034.
Artificial intelligence in customer experience is the application of AI technology to improve and tailor interactions between customers and organizations. Technology in AI in customer experience has enabled more sophisticated machine learning algorithms, more accurate data analysis, and enhanced natural language processing, all of which have substantially expanded the growth in customer experience and are driving market expansion. Companies now provide their clients with services that are both responsive and personalized due to these improvements. With the help of AI customer experience solutions, companies can effortlessly manage a large number of client interactions.
This aids in the growth and expansion of the company and increases customer happiness and loyalty by guaranteeing consistent service quality, particularly during times of high demand. A lack of knowledge and expensive implementation, however, can impede the industry's growth. In addition, chatbots powered by AI are booming in popularity due to their competence in assisting clients around the clock and providing accurate answers to their concerns. Using AI in customer experience in marketing allows for more focused and individualized campaigns, which in turn can enhance market demand.
However, regulatory hurdles, high starting expenses, and worries about data privacy and security are all limiting factors in the market's expansion. AI in customer experiences can't grow due to these reasons. In addition, COVID-19 sped up the use of AI in customer service as companies tried to keep services running even when there were problems. As digital contacts and remote customer service have grown, so has the need for AI-driven solutions that improve efficiency, personalization, and engagement in a world that is changing quickly. Market growth is attributable to rising investments and technological developments in AI in the customer experience market.
The AI in the customer experience market is segmented based on type, application, end-user industry, deployment mode, and organization size. Based on the type, the market is segmented into natural language processing (NLP), machine learning, deep learning, computer vision, virtual assistants, and others. By application, the market is segmented into chatbots, voice assistants, personalized recommendations, sentiment analysis, customer segmentation, virtual customer support, predictive analytics, and customer behaviour analysis. In the end-user industry, the market is segmented into retail, e-commerce, banking and finance, healthcare, telecom, hospitality, automotive, and others. By deployment mode, the market is further categorized into cloud and on-premises. As per the organization size, the market is segmented into small &medium-sized enterprises (SMEs) and large enterprises.
Machine learning in the AI in customer experience market is likely to hold a major global market share because of its speed and accuracy in analyzing massive datasets. It lets companies learn what their customers want, anticipate their actions, and provide them with tailored experiences in real time. With the help of machine learning algorithms, recommendations and interactions are made more accurate. Businesses can now offer proactive and personalized customer service due to this capability, which increases happiness and loyalty and pushes machine learning technology to be widely used.
Large enterprises are growing rapidly because of their access to cutting-edge innovation, their ability to implement AI in customer experience solutions across several business functions, and the wealth of consumer data at their fingertips. Because of these skills, they are able to provide more tailored experiences, boost consumer happiness, and stay ahead of the competition, growing this segment.
The North American AI in customer experience market is expected to report the largest market share in revenue in the near future. This can be attributed to the better technology infrastructure, the widespread use of AI customer services, and a strong focus on making customers more interested. The market is also growing because of big tech companies in the area and big advances in AI research and development. In addition, the Europe is anticipated to grow rapidly in the global AI customer experience market due to an increasing number of factors, such as more digitalization, a growing middle class with changing customer needs, and big investments in AI technologies. The area has a growing e-commerce industry, and helpful government programs are expanding markets of the Europe region.
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Report Attribute |
Specifications |
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Market Size Value In 2024 |
USD 11.9 Bn |
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Revenue Forecast In 2034 |
USD 117.8 Bn |
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Growth Rate CAGR |
CAGR of 26.0% from 2025 to 2034 |
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Quantitative Units |
Representation of revenue in US$ Bn and CAGR from 2025 to 2034 |
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Historic Year |
2021 to 2024 |
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Forecast Year |
2025-2034 |
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Report Coverage |
The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
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Segments Covered |
By Type, Application, End-User Industry , Deployment Mode, Organization Size |
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Regional Scope |
North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
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Country Scope |
U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
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Competitive Landscape |
IBM Corporation, Salesforce, Microsoft Corporation, Oracle Corporation, SAP SE, Adobe Inc., Google LLC, Amazon Web Services (AWS), Genesys, Zendesk, Nuance Communications, Pegasystems Inc., Verint Systems, LivePerson Inc., Freshworks Inc., SAS Institute Inc., Avaya Inc., Acquire.io, Intercom Inc., Bold360 (LogMeIn), Ada Support Inc., Drift.com Inc., Clarabridge Inc., Aptean, Khoros, LLC |
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Customization Scope |
Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
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Pricing And Available Payment Methods |
Explore pricing alternatives that are customized to your particular study requirements. |
Chapter 1. Methodology and Scope
1.1. Research Methodology
1.2. Research Scope & Assumptions
Chapter 2. Executive Summary
Chapter 3. Global AI in Customer Experience Market Snapshot
Chapter 4. Global AI in Customer Experience 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)
5.2.2. Machine Learning
5.2.3. Deep Learning
5.2.4. Computer Vision
5.2.5. Virtual Assistants
5.2.6. Others
Chapter 6. Market Segmentation 2: by Application Estimates & Trend Analysis
6.1. by Application & Market Share, 2024 & 2034
6.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Application:
6.2.1. Chatbots
6.2.2. Voice Assistants
6.2.3. Personalized Recommendations
6.2.4. Sentiment Analysis
6.2.5. Customer Segmentation
6.2.6. Virtual Customer Support
6.2.7. Predictive Analytics
6.2.8. Customer Behavior Analysis
Chapter 7. Market Segmentation 3: by End-User Industry Estimates & Trend Analysis
7.1. by End-User Industry & Market Share, 2024 & 2034
7.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by End-User Industry:
7.2.1. Retail
7.2.2. E-commerce
7.2.3. Banking and Finance
7.2.4. Healthcare
7.2.5. Telecom
7.2.6. Hospitality
7.2.7. Automotive
7.2.8. Others
Chapter 8. Market Segmentation 4: by Deployment Mode Estimates & Trend Analysis
8.1. by Deployment Mode & Market Share, 2024 & 2034
8.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Deployment Mode:
8.2.1. Cloud
8.2.2. On-Premises
Chapter 9. Market Segmentation 4: by Organization Size Estimates & Trend Analysis
9.1. by Organization Size & Market Share, 2024 & 2034
9.2. Market Size (Value (US$ Mn)) & Forecasts and Trend Analyses, 2021 to 2034 for the following by Organization Size:
9.2.1. Small and Medium-sized Enterprises (SMEs)
9.2.2. Large Enterprises
Chapter 10. AI in Customer Experience Market Segmentation 5: Regional Estimates & Trend Analysis
10.1. North America
10.1.1. North America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
10.1.2. North America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
10.1.3. North America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
10.1.4. North America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
10.1.5. North America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Organization Size, 2021-2034
10.1.6. North America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
10.2. Europe
10.2.1. Europe AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
10.2.2. Europe AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
10.2.3. Europe AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
10.2.4. Europe AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
10.2.5. Europe AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Organization Size, 2021-2034
10.2.6. Europe AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
10.3. Asia Pacific
10.3.1. Asia Pacific AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
10.3.2. Asia Pacific AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
10.3.3. Asia-Pacific AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
10.3.4. Asia-Pacific AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
10.3.5. Asia-Pacific AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Organization Size, 2021-2034
10.3.6. Asia Pacific AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
10.4. Latin America
10.4.1. Latin America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
10.4.2. Latin America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
10.4.3. Latin America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
10.4.4. Latin America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
10.4.5. Latin America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Organization Size, 2021-2034
10.4.6. Latin America AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
10.5. Middle East & Africa
10.5.1. Middle East & Africa AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Type, 2021-2034
10.5.2. Middle East & Africa AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Application, 2021-2034
10.5.3. Middle East & Africa AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by End-User Industry, 2021-2034
10.5.4. Middle East & Africa AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Deployment Mode, 2021-2034
10.5.5. Middle East & Africa AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by Organization Size, 2021-2034
10.5.6. Middle East & Africa AI in Customer Experience Market Revenue (US$ Million) Estimates and Forecasts by country, 2021-2034
Chapter 11. Competitive Landscape
11.1. Major Mergers and Acquisitions/Strategic Alliances
11.2. Company Profiles
11.2.1. IBM Corporation
11.2.2. Salesforce
11.2.3. Microsoft Corporation
11.2.4. Oracle Corporation
11.2.5. SAP SE
11.2.6. Adobe Inc.
11.2.7. Google LLC
11.2.8. Amazon Web Services (AWS)
11.2.9. Genesys
11.2.10. Zendesk
11.2.11. Nuance Communications
11.2.12. Pegasystems Inc.
11.2.13. Verint Systems
11.2.14. LivePerson Inc.
11.2.15. Freshworks Inc.
11.2.16. SAS Institute Inc.
11.2.17. Avaya Inc.
11.2.18. Acquire.io
11.2.19. Intercom Inc.
11.2.20. Bold360 (LogMeIn)
11.2.21. Ada Support Inc.
11.2.22. Drift.com Inc.
11.2.23. Clarabridge Inc.
11.2.24. Aptean
11.2.25. Khoros, LLC
11.2.26. Other Prominent Players
AI in the Customer Experience Market By Type-
AI in the Customer Experience Market By Application-
AI in the Customer Experience Market By End-User Industry-
AI in the Customer Experience Market By Deployment Mode-
AI in the Customer Experience Market By Organization Size-
AI in the Customer Experience Market By Region-
North America-
Europe-
Asia-Pacific-
Latin America-
Middle East & 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:
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:
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.
