The AI in Customer Relationship Management Market Size is valued at USD 14.8 billion in 2023 and is predicted to reach USD 138.6 billion by the year 2031 at a 32.5% CAGR during the forecast period for 2024-2031.
AI in Customer Relationship Management (CRM) enhances how businesses manage customer interactions by automating tasks, personalizing experiences, and providing insights into customer behavior. AI-powered tools like chatbots, virtual assistants, and predictive analytics improve customer service, sales forecasting, and lead scoring. AI also helps with personalized marketing, churn prediction, and sentiment analysis, enabling companies to optimize customer engagement and satisfaction. By automating routine tasks & offering real-time insights, AI makes CRM more efficient, customer-focused, and data-driven
The ability of AI to perform predictive analytics is a key benefit for CRM. AI systems can predict consumer behaviour, which enables companies to take proactive measures to resolve problems and grab opportunities. This predictive power helps to lower churn, find possible high-value clients, and increase customer lifetime value.
The AI in customer relationship management is being driven by several factors including demand for personalized customer experience, rising investment in AI, growing volume of customer data, need for improved customer support and many others. However, the market growth of this market is restricted by some variables like a shortage of skilled workforce, data privacy and security concerns, technical limitations and others.
Furthermore, integration with other technologies such as IoT and big data analytics are some of the major potential opportunities for market growth during the projected period.
The AI in the customer relationship management market is segmented as type, application, end-use industry and deployment model. Based on type, the market is segmented into machine learning, natural language processing (NLP), image and speech recognition, predictive analytics, chatbots, and virtual assistants. By application, the market is further segmented into sales automation, customer service and support, marketing personalization, customer data analysis and lead scoring and qualification. Based on industry the industry is bifurcated into retail and e-commerce, banking and finance, healthcare, telecommunications, travel, and hospitality. Based on the deployment model, the global AI in customer relationship management market is divided into Cloud-based AI-CRM, On-premises AI-CRM and Hybrid AI-CRM.
The chatbots and virtual assistants category is expected to hold a major share of the global AI in the CRM market. Routine interactions with customers are automated by chatbots as well as virtual assistants, which eliminates the need for human agents. Businesses save an enormous amount of money as a result, freeing up resources for more difficult jobs. Providing 24/7 help improves client retention and happiness. Companies don't need to hire more workers to service clients in multiple time zones. Furthermore, through conversations, chatbots and virtual assistants collect useful customer data that can be examined to learn more about the preferences, behaviour, and problems of the user. This data-driven methodology aids in the improvement of sales and marketing plans. Thus, during the forecast period, this is anticipated to fuel market expansion.
The customer service and support segment is projected to grow at a rapid rate in the global AI in customer relationship management market. By automating interactions, delivering tailored experiences, and improving operational efficiency, artificial intelligence (AI) is completely changing customer service and support in the CRM market. Although there are certain difficulties with AI-driven customer care solutions, such as data protection, integration complexity, and client acceptability, they are greatly outweighed by the advantages. The use of AI in CRM will increase as technology develops, leading to notable gains in consumer satisfaction and organizational effectiveness.
The North American AI in customer relationship management market is expected to report the largest market share in the near future. AI & machine learning (ML) are among the cutting-edge technologies that North American businesses are quick to embrace. Enhancing customer service and engagement has expedited the incorporation of AI into CRM systems. Additionally, the area is home to top IT firms that are pioneering the development and application of AI-driven CRM solutions, such as Salesforce, Microsoft, Oracle, and Adobe. The creation of creative AI solutions in CRM is further supported by large R&D expenditures made by the public and commercial sectors, which promote ongoing advancement and the development of fresh abilities. In addition, Europe is likely to grow rapidly in the global AI in customer relationship management market due to rapid digital transformation and increasing government initiatives and investments.
| Report Attribute | Specifications |
| Market Size Value In 2023 | USD 14.8 Bn |
| Revenue Forecast In 2031 | USD 138.6 Bn |
| Growth Rate CAGR | CAGR of 32.5% from 2024 to 2031 |
| Quantitative Units | Representation of revenue in US$ Bn and CAGR from 2024 to 2031 |
| Historic Year | 2019 to 2023 |
| Forecast Year | 2024-2031 |
| Report Coverage | The forecast of revenue, the position of the company, the competitive market structure, growth prospects, and trends |
| Segments Covered | By Type, Application, End-Use Industry and Deployment Model |
| Regional Scope | North America; Europe; Asia Pacific; Latin America; Middle East & Africa |
| Country Scope | U.S.; Canada; U.K.; Germany; China; India; Japan; Brazil; Mexico; France; Italy; Spain; South East Asia; South Korea |
| Competitive Landscape | Salesforce Inc., Microsoft Corporation, Oracle Corporation, IBM Corporation, SAP SE, Zendesk Inc., Adobe Inc., C3.ai Inc., Zoho Corporation, Genesys, Freshworks Inc., Pegasystems Inc. and other market plyers. |
| Customization Scope | Free customization report with the procurement of the report and modifications to the regional and segment scope. Particular Geographic competitive landscape. |
| Pricing And Available Payment Methods | Explore pricing alternatives that are customized to your particular study requirements. |
AI in Customer Relationship Management Market By Type-
AI in Customer Relationship Management Market By Application-
AI in Customer Relationship Management Market By Industry-
AI in Customer Relationship Management Market By Deployment Model-
AI in Customer Relationship Management Market By Region-
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Asia-Pacific-
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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.