세계의 고객 서비스용 AI 시장 : 최종사용자별, 제품별, 기술별, 고객 인터랙션 채널별, 지역별 - 예측(-2030년)

The AI for customer service market is projected to grow from USD 12.06 billion in 2024 to USD 47.82 billion by 2030, at a compound annual growth rate (CAGR) of 25.8% during the forecast period. AI-powered chatbots and virtual assistants are transforming customer service by providing efficient, personalized support. These technologies enable businesses to engage customers 24/7, offering instant responses to inquiries, which significantly reduces wait times and enhances satisfaction. Chatbots can handle multiple conversations simultaneously, allowing for scalability during peak periods without compromising service quality. Additionally, they utilize advanced algorithms to analyze customer data, enabling tailored recommendations and contextual interactions that foster deeper connections. This personalization not only improves user experience but also drives customer loyalty. As companies increasingly adopt these AI solutions, chatbots are becoming essential tools in modern customer engagement strategies, streamlining operations and enhancing overall service quality.

"By end user, healthcare & life sciences segment will lead the market during the forecast period."

Healthcare and life sciences are increasingly leading the customer service market through innovative engagement strategies. Hybrid engagement models are emerging, combining personalized interactions with digital channels to enhance customer experiences. Companies are leveraging AI technologies for tailored communications, self-service analytics, and intelligent patient services, fostering a more responsive environment. The shift towards digital transformation has made telemedicine and virtual visits commonplace, allowing patients to interact conveniently with healthcare providers. Additionally, organizations are focusing on personalized insights and customized care journeys, ensuring that patient needs are met effectively. This evolution not only improves service delivery but also enhances overall patient satisfaction and loyalty in a rapidly changing landscape.

"By region, Asia Pacific to register the highest CAGR market during the forecast period." Asia Pacific is leading the AI-powered customer service market due to the region's rapid adoption of technology, large consumer bases, and increasing demand for enhanced customer experiences. The rise of e-commerce, mobile services, and digital transformation initiatives across various industries, particularly in retail, banking, and telecommunications, has driven the need for more efficient and personalized customer interactions. India and China are the top countries driving this trend. In India, the focus is on improving service delivery and reducing costs, while in China, AI is being integrated into smart customer service solutions, including voice assistants and chatbots, to serve millions of customers. These innovations enhance customer satisfaction, streamline operations, and meet the growing expectations of both consumers and businesses.

Breakdown of primaries

In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the AI for customer service market.

• By Company: Tier I: 45%, Tier II: 35%, and Tier III: 20%
• By Designation: C-Level: 40%, Director Level: 35%, and Others: 25%
• By Region: North America: 30%, Europe: 20%, Asia Pacific: 35%, Middle East & Africa: 10%, and Latin America: 5%.

Microsoft (US), IBM (US), Google (US), AWS (US), Salesforce (US), Atlassian (Australia), ServiceNow (US), SAP (Germany), Zendesk (US); are some of the key players in the AI for customer service market.

The study includes an in-depth competitive analysis of these key players in the AI for customer service market, including their company profiles, recent developments, and key market strategies.

Research Coverage

This research report categorizes the AI for customer service market by product type (chatbots and virtual assistants, AI-driven ticketing systems, sentiment and feedback analysis tools, recommendation systems, visual and diagnostic tools, workflow automation, content management, AI agents), by deployment mode (cloud and on-premises), by customer service delivery mode (self-service, agent augmented backend operations automation), by functional area (pre-sales and post-sales), by technology (generative AI and other AI), by customer interaction channel (text and email, voice, video/visual, and omnichannel), by end user (media & entertainment, telecommunications, government & public sector, healthcare & life sciences, manufacturing, retail & ecommerce, technology & software, travel & hospitality, transportation & logistics). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the AI for customer service market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions and services, key strategies, Contracts, partnerships, and agreements. new product & service launches, mergers and acquisitions, and recent developments associated with the AI for customer service market. Competitive analysis of upcoming startups in the AI for customer service market ecosystem is covered in this report.

Key Benefits of Buying the Report

The report will help the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall AI for customer service market and the subsegments. This report will help stakeholders understand the competitive landscape and gain more insights to position their businesses better and to plan suitable go-to-market strategies. The report also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.

The report provides insights on the following pointers:

• Analysis of key drivers (Improved customer engagement with omni-channel self-service options, and enhancing efficiency and satisfaction with intelligent routing), restraints (Mitigating deepfake threats in customer service), opportunities (augmenting customer service efficiency with Gen AI solutions, empowering proactive customer service with ai solutions), and challenges (threat of job displacements in customer service)
• Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the AI for customer service market
• Market Development: Comprehensive information about lucrative markets - the report analyses the AI for customer service market across varied regions.
• Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the AI for customer service market
• Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players Microsoft (US), IBM (US), Google (US), Oracle (US), AWS (US), Salesforce (US), Atlassian (Australia), ServiceNow (US), SAP (Germany), Zendesk (US), Sprinklr (US), OpenAI (US), Aisera (US), UiPath (US), HubSpot (US), NICE (Israel), Intercom (US), Qualtrics (US) among others in AI for customer service market.

TABLE OF CONTENTS

1 INTRODUCTION

• 1.1 STUDY OBJECTIVES
• 1.2 MARKET DEFINITION
  • 1.2.1 INCLUSIONS & EXCLUSIONS
• 1.3 MARKET SCOPE
  • 1.3.1 MARKET SEGMENTATION & REGIONS COVERED
  • 1.3.2 YEARS CONSIDERED
• 1.4 CURRENCY CONSIDERED
• 1.5 STAKEHOLDERS

2 RESEARCH METHODOLOGY

• 2.1 RESEARCH DATA
  • 2.1.1 SECONDARY DATA
  • 2.1.2 PRIMARY DATA
    • 2.1.2.1 Breakup of primary profiles
    • 2.1.2.2 Key industry insights
• 2.2 DATA TRIANGULATION
• 2.3 MARKET SIZE ESTIMATION
  • 2.3.1 TOP-DOWN APPROACH
  • 2.3.2 BOTTOM-UP APPROACH
• 2.4 MARKET FORECAST
• 2.5 RESEARCH ASSUMPTIONS
• 2.6 RISK ASSESSMENT
• 2.7 RESEARCH LIMITATIONS

3 EXECUTIVE SUMMARY

4 PREMIUM INSIGHTS

• 4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN AI FOR CUSTOMER SERVICE MARKET
• 4.2 AI FOR CUSTOMER SERVICE MARKET: TOP THREE CUSTOMER SERVICE DELIVERY MODES
• 4.3 NORTH AMERICA: AI FOR CUSTOMER SERVICE MARKET, BY DEPLOYMENT MODE AND FUNCTIONAL AREA
• 4.4 AI FOR CUSTOMER SERVICE MARKET: BY REGION

5 MARKET OVERVIEW AND INDUSTRY TRENDS

• 5.1 INTRODUCTION
• 5.2 MARKET DYNAMICS
  • 5.2.1 DRIVERS
    • 5.2.1.1 Improved customer engagement with omni-channel self-service options
    • 5.2.1.2 Maximizing agent efficiency through AI integration
    • 5.2.1.3 Enhancing efficiency and satisfaction with intelligent routing
  • 5.2.2 RESTRAINTS
    • 5.2.2.1 Mitigating deepfake threats in customer service
  • 5.2.3 OPPORTUNITIES
    • 5.2.3.1 Transforming customer service with generative AI innovations
    • 5.2.3.2 Empowering proactive customer service with AI solutions
  • 5.2.4 CHALLENGES
    • 5.2.4.1 Threats of job displacements in customer service
• 5.3 INDUSTRY TRENDS
  • 5.3.1 EVOLUTION OF AI FOR CUSTOMER SERVICE MARKET
  • 5.3.2 CASE STUDY ANALYSIS
    • 5.3.2.1 Smokeball enhanced efficiency and satisfaction with BrainFish AI help center
    • 5.3.2.2 Philip Morris enhances customer engagement with Tovie AI's Mark Chatbot
    • 5.3.2.3 Qapital achieves 24/7 service and automation with Ada's AI solution
    • 5.3.2.4 Gorgias helped Everyday Dose streamline customer support to manage high ticket volumes
    • 5.3.2.5 RingCentral unified Corteva's communication for global collaboration success
    • 5.3.2.6 Jardim Exotico enhances customer support with Tovie AI's chatbot solution
    • 5.3.2.7 Orange Spain streamlines operations with UiPath's RPA solution
  • 5.3.3 ECOSYSTEM ANALYSIS
    • 5.3.3.1 Chatbots and virtual assistant providers
      • 5.3.3.1.1 Rule-based chatbots
      • 5.3.3.1.2 Conversational bots
      • 5.3.3.1.3 Voice assistants
    • 5.3.3.2 AI-driven ticketing system providers
      • 5.3.3.2.1 Automated ticket routing
      • 5.3.3.2.2 Self-service portals
      • 5.3.3.2.3 Case resolution assistant
    • 5.3.3.3 Sentiment and feedback analysis tools
      • 5.3.3.3.1 Sentiment & emotion detection
      • 5.3.3.3.2 Customer feedback
      • 5.3.3.3.3 Social media monitoring
    • 5.3.3.4 Recommendation systems
      • 5.3.3.4.1 Dynamic FAQs
      • 5.3.3.4.2 Knowledge base platforms
    • 5.3.3.5 Visual and diagnostic tools
      • 5.3.3.5.1 Image recognition tools
      • 5.3.3.5.2 Voice-based assistance
    • 5.3.3.6 Workflow automation
      • 5.3.3.6.1 Robotic process automation
      • 5.3.3.6.2 Integrated CRM automation
    • 5.3.3.7 Content management
      • 5.3.3.7.1 Content distribution
      • 5.3.3.7.2 Content generation
      • 5.3.3.7.3 Content moderation
    • 5.3.3.8 AI agents
      • 5.3.3.8.1 Performance analytics
      • 5.3.3.8.2 Conversation intelligence
    • 5.3.3.9 Customer interaction channels
      • 5.3.3.9.1 Text and email
      • 5.3.3.9.2 Voice
      • 5.3.3.9.3 Video/Visual
      • 5.3.3.9.4 Omnichannel
    • 5.3.3.10 End users
  • 5.3.4 TECHNOLOGY ANALYSIS
    • 5.3.4.1 Key technologies
      • 5.3.4.1.1 NLP and deep learning
      • 5.3.4.1.2 Big data analytics
      • 5.3.4.1.3 Generative AI
        • 5.3.4.1.3.1 Rule-based models
        • 5.3.4.1.3.2 Statistical models
        • 5.3.4.1.3.3 Deep learning models
        • 5.3.4.1.3.4 Generative Adversarial Networks (GANs)
        • 5.3.4.1.3.5 Autoencoders
        • 5.3.4.1.3.6 Convolutional Neural Networks (CNNs)
        • 5.3.4.1.3.7 Transformer-based Large Language Models (LLMs)
      • 5.3.4.1.4 AI agent memory
        • 5.3.4.1.4.1 Short-term Memory (STM)
        • 5.3.4.1.4.2 Long-term Memory (LTM) Type 1
        • 5.3.4.1.4.3 Long-term Memory (LTM) Type 2
        • 5.3.4.1.4.4 Long-term Memory (LTM) Type 3
      • 5.3.4.1.5 Robotic Process Automation (RPA)
    • 5.3.4.2 Adjacent technologies
      • 5.3.4.2.1 Cloud computing
      • 5.3.4.2.2 Edge computing
      • 5.3.4.2.3 Internet of Things
      • 5.3.4.2.4 5G and advanced connectivity
    • 5.3.4.3 Complementary technologies
      • 5.3.4.3.1 Cybersecurity
      • 5.3.4.3.2 Augmented Reality (AR) and Virtual Reality (VR)
      • 5.3.4.3.3 Blockchain
  • 5.3.5 REGULATORY LANDSCAPE
    • 5.3.5.1 Regulatory bodies, government agencies, and other organizations
    • 5.3.5.2 Regulatory Framework
      • 5.3.5.2.1 North America
        • 5.3.5.2.1.1 US
        • 5.3.5.2.1.2 Canada
      • 5.3.5.2.2 Europe
        • 5.3.5.2.2.1 Germany
        • 5.3.5.2.2.2 UK
        • 5.3.5.2.2.3 France
      • 5.3.5.2.3 Asia Pacific
        • 5.3.5.2.3.1 Australia
        • 5.3.5.2.3.2 India
        • 5.3.5.2.3.3 China
      • 5.3.5.2.4 Middle East & Africa
        • 5.3.5.2.4.1 UAE
        • 5.3.5.2.4.2 Kenya
        • 5.3.5.2.4.3 Africa
      • 5.3.5.2.5 Latin America
        • 5.3.5.2.5.1 Brazil
        • 5.3.5.2.5.2 Mexico
        • 5.3.5.2.5.3 Argentina
  • 5.3.6 SUPPLY CHAIN ANALYSIS
  • 5.3.7 PORTER'S FIVE FORCES ANALYSIS
    • 5.3.7.1 Threat of new entrants
    • 5.3.7.2 Threat of substitutes
    • 5.3.7.3 Bargaining power of suppliers
    • 5.3.7.4 Bargaining power of buyers
    • 5.3.7.5 Intensity of competitive rivalry
  • 5.3.8 KEY CONFERENCES AND EVENTS (2025-2026)
  • 5.3.9 KEY STAKEHOLDERS AND BUYING CRITERIA
    • 5.3.9.1 Key Stakeholders in Buying Process
    • 5.3.9.2 Buying criteria
  • 5.3.10 PRICING ANALYSIS
    • 5.3.10.1 Indicative pricing analysis, by software type
    • 5.3.10.2 Indicative pricing analysis, by product type
  • 5.3.11 PATENT ANALYSIS
    • 5.3.11.1 Methodology
    • 5.3.11.2 Patents filed, by document type
    • 5.3.11.3 INNOVATIONS AND PATENT APPLICATIONS
  • 5.3.12 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
  • 5.3.13 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
  • 5.3.14 IMPACT OF GENERATIVE AI ON AI FOR CUSTOMER SERVICE MARKET
    • 5.3.14.1 Top use cases & market potential
    • 5.3.14.2 Key use cases
      • 5.3.14.2.1 Enhanced efficiency and productivity
      • 5.3.14.2.2 24/7 availability
      • 5.3.14.2.3 Personalized customer interactions
      • 5.3.14.2.4 Cost reduction
      • 5.3.14.2.5 Proactive customer engagement
      • 5.3.14.2.6 Scalability

6 AI FOR CUSTOMER SERVICE MARKET, BY END USER

• 6.1 INTRODUCTION
  • 6.1.1 END USER: AI FOR CUSTOMER SERVICE MARKET DRIVERS
• 6.2 BFSI
  • 6.2.1 ENHANCING BFSI CUSTOMER SERVICE WITH AI-DRIVEN EFFICIENCY AND SECURITY
• 6.3 MEDIA & ENTERTAINMENT
  • 6.3.1 PERSONALIZING AUDIENCE ENGAGEMENT WITH AI
• 6.4 TELECOMMUNICATIONS
  • 6.4.1 AUTOMATING CUSTOMER SUPPORT FOR FASTER RESOLUTIONS
• 6.5 GOVERNMENT & PUBLIC SECTOR
  • 6.5.1 ENHANCING CITIZEN SERVICES WITH AI-DRIVEN ASSISTANCE
• 6.6 HEALTHCARE & LIFE SCIENCES
  • 6.6.1 TRANSFORMING PATIENT INTERACTIONS WITH AI-POWERED SUPPORT
• 6.7 MANUFACTURING
  • 6.7.1 STREAMLINING TECHNICAL ASSISTANCE AND SUPPLY CHAIN INQUIRIES
• 6.8 RETAIL & E-COMMERCE
  • 6.8.1 ELEVATING SHOPPING EXPERIENCES WITH AI-DRIVEN CUSTOMER SERVICE
• 6.9 TECHNOLOGY & SOFTWARE
  • 6.9.1 OPTIMIZING USER SUPPORT WITH INTELLIGENT AI SOLUTIONS
• 6.10 TRAVEL & HOSPITALITY
  • 6.10.1 REVOLUTIONIZING GUEST SERVICES WITH AI-POWERED INTERACTIONS
• 6.11 TRANSPORTATION & LOGISTICS
  • 6.11.1 ENHANCING SHIPMENT TRACKING AND LOGISTICS SUPPORT WITH AI
• 6.12 OTHER END USERS

7 AI FOR CUSTOMER SERVICE MARKET, BY PRODUCT

• 7.1 INTRODUCTION
  • 7.1.1 PRODUCT: AI FOR CUSTOMER SERVICE MARKET DRIVERS
• 7.2 TYPE
  • 7.2.1 CHATBOTS AND VIRTUAL ASSISTANTS
    • 7.2.1.1 Rule-based chatbots
    • 7.2.1.2 AI-powered conversational bots
    • 7.2.1.3 Voice assistants & speech analytics
    • 7.2.1.4 Other chatbots & virtual assistants
  • 7.2.2 AI-DRIVEN TICKETING SYSTEMS
    • 7.2.2.1 Automated ticket routing
    • 7.2.2.2 Self-service portals
    • 7.2.2.3 Case resolution assistance
    • 7.2.2.4 Other AI-driven ticketing systems
  • 7.2.3 SENTIMENT AND FEEDBACK ANALYSIS TOOLS
    • 7.2.3.1 Sentiment & emotion detection
    • 7.2.3.2 Customer feedback
    • 7.2.3.3 Social media monitoring
    • 7.2.3.4 Other sentiment and feedback analysis tools
  • 7.2.4 RECOMMENDATION SYSTEMS
    • 7.2.4.1 Product recommendation systems
    • 7.2.4.2 Dynamic FAQs
    • 7.2.4.3 Knowledge base platforms
    • 7.2.4.4 Other recommendation systems
  • 7.2.5 VISUAL AND DIAGNOSTIC TOOLS
    • 7.2.5.1 Image recognition tools
    • 7.2.5.2 Video-based assistance
    • 7.2.5.3 Other visual and diagnostic tools
  • 7.2.6 WORKFLOW AUTOMATION
    • 7.2.6.1 Robotic Process Automation (RPA)
    • 7.2.6.2 Integrated CRM automation
    • 7.2.6.3 Other workflow automation tools
  • 7.2.7 CONTENT MANAGEMENT
    • 7.2.7.1 Content distribution
    • 7.2.7.2 Content generation
    • 7.2.7.3 Content moderation and filtration
    • 7.2.7.4 Other content management
  • 7.2.8 AI AGENTS
    • 7.2.8.1 Performance analytics
    • 7.2.8.2 Conversation intelligence
    • 7.2.8.3 Behavior analytics & engagement
    • 7.2.8.4 Other AI agents
  • 7.2.9 OTHER PRODUCT TYPES
• 7.3 BY DEPLOYMENT MODE
  • 7.3.1 CLOUD
    • 7.3.1.1 Flexibility, cost-effectiveness, and rapid deployment to drive market
  • 7.3.2 ON-PREMISES
    • 7.3.2.1 Secure and customized on-premises AI to drive market
• 7.4 BY CUSTOMER SERVICE DELIVERY MODE
  • 7.4.1 SELF-SERVICE
    • 7.4.1.1 Reduced wait times and operational costs to drive market
  • 7.4.2 AGENT AUGMENTED
    • 7.4.2.1 Elevating customer service with AI-powered augmented agents
  • 7.4.3 BACKEND OPERATIONS AUTOMATION
    • 7.4.3.1 Streamlined and optimized service operations to drive market
• 7.5 BY FUNCTIONAL AREA
  • 7.5.1 PRE-SALES
    • 7.5.1.1 Tailored solutions for improved customer experiences to drive market
  • 7.5.2 POST-SALES
    • 7.5.2.1 Increased customer satisfaction and support with AI solutions to drive market

8 AI FOR CUSTOMER SERVICE MARKET, BY TECHNOLOGY

• 8.1 INTRODUCTION
  • 8.1.1 GENERATIVE AI
    • 8.1.1.1 Empower dynamic and context-aware interactions with generative AI
  • 8.1.2 OTHER AI
    • 8.1.2.1 Enhancing customer service: Power of AI technologies

9 AI FOR CUSTOMER SERVICE MARKET, BY CUSTOMER INTERACTION CHANNEL

• 9.1 INTRODUCTION
• 9.2 TEXT AND EMAIL
  • 9.2.1 MAXIMIZED ENGAGEMENT WITH ASYNCHRONOUS COMMUNICATION TO DRIVE MARKET
• 9.3 VOICE
  • 9.3.1 INCREASED INTEGRATION OF VOICE WITH DIGITAL TOOLS TO DRIVE MARKET
• 9.4 VIDEO/VISUAL
  • 9.4.1 ENHANCED CUSTOMER ENGAGEMENT THROUGH VIDEO INTERACTIONS TO DRIVE MARKET
• 9.5 OMNICHANNEL
  • 9.5.1 INTEGRATION OF DATA ACROSS CHANNELS FOR ENHANCED PERSONALIZATION TO DRIVE MARKET

10 AI FOR CUSTOMER SERVICE MARKET, BY REGION

• 10.1 INTRODUCTION
• 10.2 NORTH AMERICA
  • 10.2.1 NORTH AMERICA: AI FOR CUSTOMER SERVICE MARKET DRIVERS
  • 10.2.2 NORTH AMERICA: MACROECONOMIC IMPACT
  • 10.2.3 US
    • 10.2.3.1 Rise in need to enhance customer experience using AI-powered chatbots and virtual assistants to drive market
  • 10.2.4 CANADA
    • 10.2.4.1 Innovations in ethical AI to enhance AI-enabled customer support and drive market
• 10.3 EUROPE
  • 10.3.1 EUROPE: AI FOR CUSTOMER SERVICE MARKET DRIVERS
  • 10.3.2 EUROPE: MACROECONOMIC IMPACT
  • 10.3.3 UK
    • 10.3.3.1 Enhancing customer engagement with ethical AI to drive market
  • 10.3.4 GERMANY
    • 10.3.4.1 Advancing AI-powered customer service to drive market
  • 10.3.5 FRANCE
    • 10.3.5.1 Advancing ethical AI solutions for customer service to drive market
  • 10.3.6 ITALY
    • 10.3.6.1 Empowering SMEs and strengthening data privacy to drive market
  • 10.3.7 SPAIN
    • 10.3.7.1 Oracle's USD 1 billion cloud investment to drive AI growth
  • 10.3.8 REST OF EUROPE
• 10.4 ASIA PACIFIC
  • 10.4.1 ASIA PACIFIC: AI FOR CUSTOMER SERVICE MARKET DRIVERS
  • 10.4.2 ASIA PACIFIC: MACROECONOMIC IMPACT
  • 10.4.3 CHINA
    • 10.4.3.1 Implementation of regulatory approval for generative AI applications to drive market
  • 10.4.4 JAPAN
    • 10.4.4.1 Regulatory efforts and partnerships to drive AI for customer service
  • 10.4.5 INDIA
    • 10.4.5.1 Adoption of AI-driven solutions for personalized customer service to drive market
  • 10.4.6 SOUTH KOREA
    • 10.4.6.1 Increased AI integration for personalized customer support to drive market
  • 10.4.7 AUSTRALIA & NEW ZEALAND
    • 10.4.7.1 AI revolution in Australia & New Zealand to drive market
  • 10.4.8 ASEAN COUNTRIES
    • 10.4.8.1 Governments strengthening digital infrastructure for AI innovation to drive market
  • 10.4.9 REST OF ASIA PACIFIC
• 10.5 MIDDLE EAST & AFRICA
  • 10.5.1 MIDDLE EAST & AFRICA: AI FOR CUSTOMER SERVICE MARKET DRIVERS
  • 10.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC IMPACT
  • 10.5.3 MIDDLE EAST
    • 10.5.3.1 KSA
      • 10.5.3.1.1 Saudi Arabia's Vision 2030 for enhancing AI-driven customer engagement to drive market
    • 10.5.3.2 UAE
      • 10.5.3.2.1 UAE's digital transformation fuels AI-driven customer service innovation
    • 10.5.3.3 Bahrain
      • 10.5.3.3.1 Bahrain's regulatory sandbox drives AI innovation in customer service
    • 10.5.3.4 Kuwait
      • 10.5.3.4.1 SAP empowering Kuwaiti organizations by embedding AI into business applications for better operational efficiency
    • 10.5.3.5 Rest of Middle East
  • 10.5.4 AFRICA
• 10.6 LATIN AMERICA
  • 10.6.1 LATIN AMERICA: AI FOR CUSTOMER SERVICE MARKET DRIVERS
  • 10.6.2 LATIN AMERICA: MACROECONOMIC IMPACT
  • 10.6.3 BRAZIL
    • 10.6.3.1 Increased customer service innovation with AI-powered chatbots to drive market
  • 10.6.4 MEXICO
    • 10.6.4.1 Mexico leverages AI for customer service through key partnerships and innovations
  • 10.6.5 ARGENTINA
    • 10.6.5.1 Strategic partnerships and investment incentives to drive AI growth
  • 10.6.6 REST OF LATIN AMERICA

11 COMPETITIVE LANDSCAPE

• 11.1 OVERVIEW
• 11.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2020-2024
• 11.3 REVENUE ANALYSIS, 2019-2023
• 11.4 MARKET SHARE ANALYSIS, 2023
  • 11.4.1 MARKET SHARE ANALYSIS OF KEY PLAYERS
  • 11.4.2 MARKET RANKING ANALYSIS
• 11.5 PRODUCT COMPARATIVE ANALYSIS, BY PRODUCT TYPE
  • 11.5.1 PRODUCT COMPARATIVE ANALYSIS, BY CHATBOTS AND VIRTUAL ASSISTANTS
    • 11.5.1.1 Google Dialogflow
    • 11.5.1.2 IBM Watson Assistant
    • 11.5.1.3 XO Automation (Kore.ai)
  • 11.5.2 PRODUCT COMPARATIVE ANALYSIS, BY AI-DRIVEN TICKETING SYSTEMS
    • 11.5.2.1 Freedy AI (Freshdesk)
    • 11.5.2.2 AI bot (Zendesk)
    • 11.5.2.3 Zia AI (Zoho)
  • 11.5.3 PRODUCT COMPARATIVE ANALYSIS, BY RECOMMENDATION SYSTEMS
    • 11.5.3.1 Amazon Personalize (AWS)
    • 11.5.3.2 Product Recommendation Engines (Salesforce)
    • 11.5.3.3 Dynamic Yield
• 11.6 COMPANY VALUATION AND FINANCIAL METRICS OF KEY VENDORS
• 11.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
  • 11.7.1 STARS
  • 11.7.2 EMERGING LEADERS
  • 11.7.3 PERVASIVE PLAYERS
  • 11.7.4 PARTICIPANTS
  • 11.7.5 COMPANY FOOTPRINT: KEY PLAYERS
    • 11.7.5.1 Company footprint
    • 11.7.5.2 Region footprint
    • 11.7.5.3 Product type footprint
    • 11.7.5.4 Customer interaction channel footprint
    • 11.7.5.5 End user footprint
• 11.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
  • 11.8.1 PROGRESSIVE COMPANIES
  • 11.8.2 RESPONSIVE COMPANIES
  • 11.8.3 DYNAMIC COMPANIES
  • 11.8.4 STARTING BLOCKS
  • 11.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
    • 11.8.5.1 Detailed list of key startups/SMEs
    • 11.8.5.2 Competitive benchmarking of key startups/SMEs
• 11.9 COMPETITIVE SCENARIO
  • 11.9.1 PRODUCT LAUNCHES & ENHANCEMENTS
  • 11.9.2 DEALS

12 COMPANY PROFILES

• 12.1 INTRODUCTION
• 12.2 KEY PLAYERS
  • 12.2.1 MICROSOFT
    • 12.2.1.1 Business overview
    • 12.2.1.2 Products/Solutions/Services offered
    • 12.2.1.3 Recent developments
      • 12.2.1.3.1 Product launches and enhancements
      • 12.2.1.3.2 Deals
    • 12.2.1.4 MnM view
      • 12.2.1.4.1 Key strengths
      • 12.2.1.4.2 Strategic choices
      • 12.2.1.4.3 Weaknesses and competitive threats
  • 12.2.2 IBM
    • 12.2.2.1 Business overview
    • 12.2.2.2 Products/Solutions/Services offered
    • 12.2.2.3 Recent developments
      • 12.2.2.3.1 Product launches and enhancements
      • 12.2.2.3.2 Deals
    • 12.2.2.4 MnM view
      • 12.2.2.4.1 Key strengths
      • 12.2.2.4.2 Strategic choices
      • 12.2.2.4.3 Weaknesses and competitive threats
  • 12.2.3 GOOGLE
    • 12.2.3.1 Business overview
    • 12.2.3.2 Products/Solutions/Services offered
    • 12.2.3.3 Recent developments
      • 12.2.3.3.1 Product launches and enhancements
      • 12.2.3.3.2 Deals
    • 12.2.3.4 MnM view
      • 12.2.3.4.1 Key strengths
      • 12.2.3.4.2 Strategic choices
      • 12.2.3.4.3 Weaknesses and competitive threats
  • 12.2.4 AWS
    • 12.2.4.1 Business overview
    • 12.2.4.2 Products/Solutions/Services offered
    • 12.2.4.3 Recent developments
      • 12.2.4.3.1 Product launches and enhancements
      • 12.2.4.3.2 Deals
    • 12.2.4.4 MnM view
      • 12.2.4.4.1 Key strengths
      • 12.2.4.4.2 Strategic choices
      • 12.2.4.4.3 Weaknesses and competitive threats
  • 12.2.5 SALESFORCE
    • 12.2.5.1 Business overview
    • 12.2.5.2 Products/Solutions/Services offered
    • 12.2.5.3 Recent developments
      • 12.2.5.3.1 Product launches and enhancements
    • 12.2.5.4 MnM view
      • 12.2.5.4.1 Key strengths
      • 12.2.5.4.2 Strategic choices
      • 12.2.5.4.3 Weaknesses and competitive threats
  • 12.2.6 ATLASSIAN
    • 12.2.6.1 Business overview
    • 12.2.6.2 Products/Solutions/Services offered
    • 12.2.6.3 Recent developments
      • 12.2.6.3.1 Product launches and enhancements
  • 12.2.7 SERVICENOW
    • 12.2.7.1 Business overview
    • 12.2.7.2 Products/Solutions/Services offered
    • 12.2.7.3 Recent developments
      • 12.2.7.3.1 Product launches and enhancements
  • 12.2.8 ZENDESK
    • 12.2.8.1 Business overview
    • 12.2.8.2 Products/Solutions/Services offered
    • 12.2.8.3 Recent developments
      • 12.2.8.3.1 Product launches and enhancements
      • 12.2.8.3.2 Deals
  • 12.2.9 SAP
    • 12.2.9.1 Business overview
    • 12.2.9.2 Products/Solutions/Services offered
    • 12.2.9.3 Recent developments
      • 12.2.9.3.1 Deals
  • 12.2.10 SPRINKLR
    • 12.2.10.1 Business overview
    • 12.2.10.2 Products/Solutions/Services offered
    • 12.2.10.3 Recent developments
      • 12.2.10.3.1 Deals
  • 12.2.11 OPENAI
    • 12.2.11.1 Business overview
    • 12.2.11.2 Products/Solutions/Services offered
    • 12.2.11.3 Recent developments
      • 12.2.11.3.1 Product Launches and Enhancements
      • 12.2.11.3.2 Deals
  • 12.2.12 AISERA
  • 12.2.13 UIPATH
  • 12.2.14 HUBSPOT
  • 12.2.15 NICE
  • 12.2.16 INTERCOM
  • 12.2.17 QUALTRICS
  • 12.2.18 FRESHWORKS
  • 12.2.19 LIVEPERSON
  • 12.2.20 HELPSHIFT
  • 12.2.21 YELLOW.AI
  • 12.2.22 COGITO
  • 12.2.23 SMARTACTION
  • 12.2.24 TALKDESK
  • 12.2.25 FIVE9
  • 12.2.26 RINGCENTRAL
  • 12.2.27 NEXTIVA
  • 12.2.28 KORE.AI
  • 12.2.29 DYNAMIC YIELD
  • 12.2.30 JIOHAPTIK
  • 12.2.31 ORACLE
  • 12.2.32 AFINITI
• 12.3 STARTUPS/SMES
  • 12.3.1 KOMMUNICATE
  • 12.3.2 HELP SCOUT
  • 12.3.3 GORGIAS
  • 12.3.4 ATERA
  • 12.3.5 ADA
  • 12.3.6 KUSTOMER
  • 12.3.7 LEVITY
  • 12.3.8 COGNIGY
  • 12.3.9 ENGAGEWARE
  • 12.3.10 NETOMI
  • 12.3.11 LEVELAI
  • 12.3.12 SYBILL AI
  • 12.3.13 ONE AI
  • 12.3.14 BRAINFISH
  • 12.3.15 SENTISUM
  • 12.3.16 BALTO
  • 12.3.17 TOVIE AI
  • 12.3.18 GURU
  • 12.3.19 TIDIO
  • 12.3.20 QUIQ
  • 12.3.21 AIRCALL
  • 12.3.22 ONEREACH.AI
  • 12.3.23 CRESTA
  • 12.3.24 DEEPDESK
  • 12.3.25 FRONT
  • 12.3.26 FULLVIEW
  • 12.3.27 CRESCENDO AI
  • 12.3.28 GRIDSPACE

13 ADJACENT AND RELATED MARKETS

• 13.1 INTRODUCTION
• 13.2 CONVERSATIONAL AI MARKET - GLOBAL FORECAST TO 2030
  • 13.2.1 MARKET DEFINITION
  • 13.2.2 MARKET OVERVIEW
    • 13.2.2.1 Conversational AI market, by offering
    • 13.2.2.2 Conversational AI market, by service
    • 13.2.2.3 Conversational AI market, by business function
    • 13.2.2.4 Conversational AI market, by conversational agent type
    • 13.2.2.5 Conversational AI market, by integration mode
    • 13.2.2.6 Conversational AI market, by vertical
    • 13.2.2.7 Conversational AI market, by region
• 13.3 AI AGENTS MARKET
  • 13.3.1 MARKET DEFINITION
  • 13.3.2 MARKET OVERVIEW
    • 13.3.2.1 AI agents market, by agent system
    • 13.3.2.2 AI agents market, by product type
    • 13.3.2.3 AI agents market, by agent role
    • 13.3.2.4 AI agents market, by end user
    • 13.3.2.5 AI agents market, by region

14 APPENDIX

• 14.1 DISCUSSION GUIDE
• 14.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 14.3 CUSTOMIZATION OPTIONS
• 14.4 RELATED REPORTS
• 14.5 AUTHOR DETAILS