세계의 클라우드 AI 시장 : 제공별, 기술 유형별, 호스팅 유형별, 조직 규모별, 비즈니스 기능별, 산업별, 지역별 - 예측(-2029년)

The cloud AI market will grow from USD 80.30 billion in 2024 to USD 327.15 billion by 2029 at a compounded annual growth rate (CAGR) of 32.4% during the forecast period. Cloud AI transforms technology use across industries, including manufacturing, healthcare, finance, and retail. For instance, hospitals employ cloud AI to forecast health trends and quickly evaluate medical data, assisting physicians in making better decisions for their patients.

More businesses are using cloud AI since it offers powerful computing and data analysis without needing hardware investments. This allows companies to use AI for real-time insights, predictions, and automation, helping them work more efficiently, save money, and focus on core operations.

By offering, the AI as a service segment holds the highest CAGR during the forecast period.

AI-as-a-Service (AIaaS) is expected to grow the highest in the cloud AI market. It gives businesses access to advanced AI tools without investing in expensive infrastructure or specialized knowledge. A key advantage of AIaaS is its scalability and flexibility. Businesses can quickly change their AI capabilities as required, which works well for companies of all sizes. It also makes AI accessible to smaller businesses that don't have the resources to create and manage their systems. AIaaS providers often have simple interfaces and tools that make it easier to connect with existing systems, so businesses don't need extensive technical skills.

AIaaS is becoming more popular as businesses try to improve customer experiences and run their operations more efficiently. The growth of AutoML (automated machine learning) and pre-trained models in AIaaS is helping this trend by making it easier to develop and use AI applications. As businesses embrace data-driven decision-making, AIaaS will be crucial in navigating data complexities and driving innovation.

Based on vertical, the BFSI segment holds the largest market share during the forecast period.

Banks and financial services use cloud AI to improve security, customer service, and efficiency. Cloud AI helps them analyze data instantly, which is essential for detecting fraud, managing risks, and providing personalized services for each customer. AI models process large amounts of data in real-time to find unusual patterns and reduce the risks of financial crimes. Cloud AI improves customer service by offering personalized advice and chatbots, making banking faster and more effective in meeting the growing demand for digital services.

In insurance, cloud AI speeds up claims processing, predicts risks, and examines data, making work quicker and decisions more accurate. It also allows businesses to adjust resources without significant upfront investments in IT systems. This flexibility helps companies to adapt to changing market demands and follow new regulations. Overall, cloud AI improves security, lets companies offer personalized services, and helps them operate more efficiently to meet customer needs and keep up with a fast-changing digital world.

Based on the business function, the operations & supply segment holds the highest CAGR during the forecast period.

Cloud AI transforms how companies manage logistics, inventories, and efficiency in operations and supply chains. Companies could use AI systems to obtain real-time data about their supply chain, improve inventory management, and better predict demand. This reduces expenses, increases flexibility, and helps to satisfy customers' needs better. AI also helps businesses find potential problems and improve delivery routes, making the supply chain faster and more responsive.

Recent trends in the cloud AI market for operations and supply chain chains include the integration of Internet of Things (IoT) devices for real-time data collection, which enhances visibility across the supply chain. More businesses are using AI to automate tasks such as order processing and inventory management so their employees can focus on making key decisions. AI also helps with predictive maintenance, keeping equipment running smoothly and reducing expensive downtime. As companies work to be more eco-friendly, AI helps them cut waste and use resources more efficiently.

Breakdown of primaries

We interviewed Chief Executive Officers (CEOs), directors of innovation and technology, system integrators, and executives from several significant cloud AI market companies.

• By Company: Tier I: 40%, Tier II: 25%, and Tier III: 35%
• By Designation: C-Level Executives: 25%, Director Level: 37%, and Others: 38%
• By Region: North America: 42%, Europe: 24%, Asia Pacific: 18%, Rest of World: 16%

Some of the significant cloud AI market vendors are Google (US), IBM (US), AWS (US), Microsoft (US), Oracle (US), Nvidia (US), Salesforce (US), SAP (Germany), Alibaba Cloud (China), HPE (US), and Intel (US).

Research coverage:

In the market report, we covered the cloud AI market across segments. We estimated the market size and growth potential for many segments based on offering, technology type, hosting type, organization size, business function, verticals, and region. It contains a thorough competition analysis of the major market participants, information about their businesses, essential observations about their product and service offerings, current trends, and critical market strategies.

Reasons to buy this report:

With information on the most accurate revenue estimates for the whole cloud AI industry and its subsegments, the research will benefit market leaders and recent newcomers. Stakeholders will benefit from this report's increased understanding of the competitive environment, which will help them better position their companies and develop go-to-market strategies. The research offers information on the main market drivers, constraints, opportunities, and challenges, as well as aids players in understanding the pulse of the industry.

The report provides insights on the following pointers:

Analysis of key drivers (provide the necessary infrastructure and scalability for gen AI applications, allowing organizations to harness massive datasets and computational power), restraints (many businesses are cautious about adopting cloud-based AI solutions due to concerns over data ownership, encryption, and the potential misuse of AI-powered insights), opportunities (as technologies like the IoT the need for AI-driven solutions that can manage, analyze, and optimize the vast amounts of data generated by these innovations is increasing), and challenges (complexity of AI integration is a significant challenge for the cloud AI market, particularly for businesses with limited technical expertise).

• Product Development/Innovation: Comprehensive analysis of emerging technologies, R&D initiatives, and new service and product introductions in the cloud AI industry.
• Market Development: In-depth details regarding profitable markets: the paper examines the global cloud AI industry.
• Market Diversification: Comprehensive details regarding recent advancements, investments, unexplored regions, new goods and services, and the cloud AI industry.
• Competitive Assessment: Thorough analysis of the market shares, expansion plans, and service portfolios of the top competitors in the cloud AI industry, such as Google (US), IBM (US), AWS (US), Microsoft (US), and Oracle (US).

TABLE OF CONTENTS

1 INTRODUCTION

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

2 RESEARCH METHODOLOGY

• 2.1 RESEARCH APPROACH
  • 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 MARKET BREAKUP AND DATA TRIANGULATION
• 2.3 MARKET SIZE ESTIMATION
  • 2.3.1 TOP-DOWN APPROACH
  • 2.3.2 BOTTOM-UP APPROACH
  • 2.3.3 MARKET SIZE ESTIMATION APPROACHES
• 2.4 MARKET FORECAST
• 2.5 RESEARCH ASSUMPTIONS
• 2.6 RESEARCH LIMITATIONS

3 EXECUTIVE SUMMARY

4 PREMIUM INSIGHTS

• 4.1 GROWTH OPPORTUNITIES FOR PLAYERS IN CLOUD AI MARKET
• 4.2 CLOUD AI MARKET, BY OFFERING
• 4.3 CLOUD AI MARKET, BY HOSTING TYPE
• 4.4 CLOUD AI MARKET, BY TECHNOLOGY TYPE
• 4.5 CLOUD AI MARKET, BY BUSINESS FUNCTION
• 4.6 CLOUD AI MARKET, BY ORGANIZATION SIZE
• 4.7 CLOUD AI MARKET, BY VERTICAL
• 4.8 CLOUD AI MARKET: REGIONAL SCENARIO

5 MARKET OVERVIEW AND INDUSTRY TRENDS

• 5.1 INTRODUCTION
• 5.2 MARKET DYNAMICS
  • 5.2.1 DRIVERS
    • 5.2.1.1 Increasing advancements in generative AI and intelligent automation
    • 5.2.1.2 Rising adoption of cloud-based services and applications
    • 5.2.1.3 Growing importance of data-driven decision-making
  • 5.2.2 RESTRAINTS
    • 5.2.2.1 Data privacy and security concerns
    • 5.2.2.2 Limited internet connectivity
  • 5.2.3 OPPORTUNITIES
    • 5.2.3.1 Expansion into SMEs
    • 5.2.3.2 Integration with emerging technologies
  • 5.2.4 CHALLENGES
    • 5.2.4.1 Complexity of AI integration
    • 5.2.4.2 High costs of AI implementation
• 5.3 CASE STUDY ANALYSIS
  • 5.3.1 CASE STUDY 1: SIEMENS CONNECTED FRONTLINE WORKERS AND ENGINEERS FOR REAL-TIME PROBLEM-SOLVING USING AZURE AI
  • 5.3.2 CASE STUDY 2: ACCELERATED COLLECTION AND ANALYSIS OF INVESTMENT INFORMATION FOR EDGAR FINANCE WITH HELP OF IBM
  • 5.3.3 CASE STUDY 3: AUTOMATING SUPPORT REQUEST TRIAGE WITH SALESFORCE AI
• 5.4 ECOSYSTEM ANALYSIS
• 5.5 SUPPLY CHAIN ANALYSIS
• 5.6 PRICING ANALYSIS
  • 5.6.1 INDICATIVE PRICING ANALYSIS: CLOUD AI MARKET, BY OFFERING, 2024
  • 5.6.2 AVERAGE SELLING PRICE TRENDS
  • 5.6.3 AVERAGE SELLING PRICE TREND OF KEY PLAYERS, BY TECHNOLOGY, 2024
• 5.7 PATENT ANALYSIS
• 5.8 TECHNOLOGY ANALYSIS
  • 5.8.1 KEY TECHNOLOGIES
    • 5.8.1.1 Automated machine learning
    • 5.8.1.2 Cloud computing
  • 5.8.2 COMPLEMENTARY TECHNOLOGIES
    • 5.8.2.1 Edge computing
    • 5.8.2.2 Data lakes
    • 5.8.2.3 AI development frameworks
  • 5.8.3 ADJACENT TECHNOLOGIES
    • 5.8.3.1 Blockchain
    • 5.8.3.2 Internet of Things
• 5.9 REGULATORY LANDSCAPE
  • 5.9.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
  • 5.9.2 REGULATIONS, BY REGION
    • 5.9.2.1 North America
    • 5.9.2.2 Europe
    • 5.9.2.3 Asia Pacific
    • 5.9.2.4 Middle East & South Africa
    • 5.9.2.5 Latin America
  • 5.9.3 REGULATORY IMPLICATIONS AND INDUSTRY STANDARDS
    • 5.9.3.1 General Data Protection Regulation (GDPR)
    • 5.9.3.2 SEC Rule 17a-4
    • 5.9.3.3 ISO/IEC 27001
    • 5.9.3.4 System and Organization Controls 2 Type II Compliance
    • 5.9.3.5 Financial Industry Regulatory Authority (FINRA)
    • 5.9.3.6 Freedom of Information Act (FOIA)
    • 5.9.3.7 Health Insurance Portability and Accountability Act (HIPAA)
• 5.10 PORTER'S FIVE FORCES ANALYSIS
  • 5.10.1 THREAT OF NEW ENTRANTS
  • 5.10.2 THREAT OF SUBSTITUTES
  • 5.10.3 BARGAINING POWER OF BUYERS
  • 5.10.4 BARGAINING POWER OF SUPPLIERS
  • 5.10.5 INTENSITY OF COMPETITIVE RIVALRY
• 5.11 KEY STAKEHOLDERS AND BUYING CRITERIA
  • 5.11.1 KEY STAKEHOLDERS IN BUYING PROCESS
  • 5.11.2 BUYING CRITERIA
• 5.12 KEY CONFERENCES AND EVENTS, 2024-2025
• 5.13 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
• 5.14 BUSINESS MODEL ANALYSIS
  • 5.14.1 SUBSCRIPTION-BASED MODEL
  • 5.14.2 PAY-PER-USE MODEL
  • 5.14.3 FREEMIUM MODEL
  • 5.14.4 ENTERPRISE LICENSING MODEL
  • 5.14.5 EMERGING BUSINESS MODELS
    • 5.14.5.1 Marketplace model
    • 5.14.5.2 Data monetization model
    • 5.14.5.3 Collaborative development model
    • 5.14.5.4 Outcome-based pricing model
    • 5.14.5.5 Vertical-specific solutions model
• 5.15 INVESTMENT AND FUNDING SCENARIO
• 5.16 IMPACT OF AI/GEN AI ON CLOUD AI MARKET
  • 5.16.1 CASE STUDY: JOHNSON & JOHNSON PARTNERED WITH MICROSOFT AZURE TO DEPLOY GENERATIVE AI FOR AUTOMATION AND IMPROVING DECISION-MAKING IN HEALTHCARE
  • 5.16.2 TOP VENDORS ADAPTING TO GEN AI
    • 5.16.2.1 Microsoft
    • 5.16.2.2 Google Cloud
    • 5.16.2.3 IBM Watson
    • 5.16.2.4 Amazon Web Services (AWS)
    • 5.16.2.5 Anthropic
• 5.17 FUTURE OF AI IN CLOUD
• 5.18 USE CASES OF AI CLOUD
  • 5.18.1 INTELLIGENT CHATBOTS AND VIRTUAL AGENTS
  • 5.18.2 AI-DRIVEN RECOMMENDATION ENGINES
  • 5.18.3 AI FOR FINANCIAL RISK MODELLING
  • 5.18.4 COMPUTER VISION APPLICATIONS

6 CLOUD AI MARKET, BY OFFERING

• 6.1 INTRODUCTION
  • 6.1.1 OFFERING: CLOUD AI MARKET DRIVERS
• 6.2 INFRASTRUCTURE
  • 6.2.1 GROWING NEED FOR HIGH-PERFORMANCE COMPUTING AND SCALABLE RESOURCES IN AI WORKLOADS TO PROPEL MARKET
  • 6.2.2 CLOUD AI INFRASTRUCTURE
    • 6.2.2.1 Compute
    • 6.2.2.2 Storage
    • 6.2.2.3 Networking
  • 6.2.3 AI AND ML PLATFORMS
    • 6.2.3.1 ML platforms
    • 6.2.3.2 Automated machine learning (AutoML)
    • 6.2.3.3 Data preparation and management
  • 6.2.4 MLOPS AND LIFECYCLE MANAGEMENT
    • 6.2.4.1 Model monitoring and version control
    • 6.2.4.2 AI workflow orchestration
• 6.3 AI-AS-A-SERVICE (AIAAS)
  • 6.3.1 INCREASING DEMAND FOR SCALABLE, FLEXIBLE, AND COST-EFFECTIVE AI SOLUTIONS TO FUEL MARKET GROWTH

7 CLOUD AI MARKET, BY TECHNOLOGY TYPE

• 7.1 INTRODUCTION
  • 7.1.1 TECHNOLOGY TYPE: CLOUD AI MARKET DRIVERS
• 7.2 GENERATIVE AI
  • 7.2.1 DEMAND FOR GENERATIVE AI MODELS TO DYNAMICALLY SCALE RESOURCES AND ENHANCE COST EFFICIENCY
• 7.3 OTHER AI
  • 7.3.1 NEED FOR HIGH PROCESSING POWER AND SCALABLE RESOURCES

8 CLOUD AI MARKET, BY HOSTING TYPE

• 8.1 INTRODUCTION
  • 8.1.1 HOSTING TYPE: CLOUD AI MARKET DRIVERS
• 8.2 MANAGED HOSTING
  • 8.2.1 FAULT-TOLERANT DATA CENTERS TO BOOST DEMAND FOR MANAGED HOSTING
• 8.3 SELF-HOSTING
  • 8.3.1 DEMAND FOR INCREASED CONTROL OVER AI INFRASTRUCTURE

9 CLOUD AI MARKET, BY ORGANIZATION SIZE

• 9.1 INTRODUCTION
  • 9.1.1 ORGANIZATION SIZE: CLOUD AI MARKET DRIVERS
• 9.2 LARGE ENTERPRISES
  • 9.2.1 DEMAND FOR SCALABLE AND SECURE CLOUD AI SOLUTIONS IN COMPLEX ENTERPRISE ENVIRONMENTS
• 9.3 SMES
  • 9.3.1 DEMAND FOR COST-EFFECTIVE AND SCALABLE CLOUD AI SOLUTIONS IN SMALL-SIZED ENTERPRISES

10 CLOUD AI MARKET, BY BUSINESS FUNCTION

• 10.1 INTRODUCTION
  • 10.1.1 BUSINESS FUNCTION: CLOUD AI MARKET DRIVERS
• 10.2 MARKETING
  • 10.2.1 GROWING DEMAND FOR DATA-DRIVEN INSIGHTS AND PERSONALIZATION TO DRIVE MARKET
  • 10.2.2 MARKETING: USE CASES
    • 10.2.2.1 Customer journey optimization
    • 10.2.2.2 Predictive lead scoring
    • 10.2.2.3 Market trends and competitive analysis
    • 10.2.2.4 Email marketing optimization
• 10.3 SALES
  • 10.3.1 CLOUD AI PLATFORMS PROVIDE INSIGHTS INTO CUSTOMER JOURNEY AND BUYING INTENT
  • 10.3.2 SALES: USE CASES
    • 10.3.2.1 Sales forecasting
    • 10.3.2.2 Personalized customer engagement
    • 10.3.2.3 Customer sentiment analysis
    • 10.3.2.4 Dynamic pricing and discounting
• 10.4 HUMAN RESOURCES
  • 10.4.1 NEED FOR CLOUD AI SOLUTIONS FOR DATA-DRIVEN TALENT ACQUISITION IN HR
  • 10.4.2 HUMAN RESOURCES: USE CASES
    • 10.4.2.1 Candidate screening
    • 10.4.2.2 Employee retention analysis
    • 10.4.2.3 Performance management
    • 10.4.2.4 Workforce planning and forecasting
• 10.5 FINANCE & ACCOUNTING
  • 10.5.1 AI HELPS STREAMLINE PROCESSES, ENHANCES ACCURACY, AND PROVIDES VALUABLE INSIGHTS FOR DECISION-MAKING
  • 10.5.2 FINANCE & ACCOUNTING: USE CASES
    • 10.5.2.1 Fraud detection
    • 10.5.2.2 Financial forecasting
    • 10.5.2.3 Expense management
    • 10.5.2.4 Invoice processing
• 10.6 OPERATIONS & SUPPLY CHAIN
  • 10.6.1 NEED FOR CLOUD AI FOR REAL-TIME DATA ANALYSIS AND INVENTORY OPTIMIZATION
  • 10.6.2 OPERATIONS & SUPPLY CHAINS: USE CASES
    • 10.6.2.1 Predictive maintenance
    • 10.6.2.2 Supply chain optimization
    • 10.6.2.3 AIOps
    • 10.6.2.4 IT service management

11 CLOUD AI MARKET, BY VERTICAL

• 11.1 INTRODUCTION
  • 11.1.1 VERTICAL: CLOUD AI MARKET DRIVERS
• 11.2 BFSI
  • 11.2.1 DEMAND FOR ENHANCED SECURITY AND FRAUD DETECTION TO DRIVE MARKET
  • 11.2.2 BFSI: USE CASES
    • 11.2.2.1 Fraud detection & prevention
    • 11.2.2.2 Risk assessment & management
    • 11.2.2.3 Credit scoring & underwriting
    • 11.2.2.4 Customer service automation
• 11.3 RETAIL & E-COMMERCE
  • 11.3.1 GROWING FOCUS ON PERSONALIZED MARKETING TO DRIVE MARKET
  • 11.3.2 RETAIL & E-COMMERCE: USE CASES
    • 11.3.2.1 Personalized product recommendation
    • 11.3.2.2 Customer relationship management
    • 11.3.2.3 Visual search
    • 11.3.2.4 Virtual customer assistant
• 11.4 MANUFACTURING
  • 11.4.1 NEED FOR QUALITY CONTROL AND PREDICTIVE MAINTENANCE TO MINIMIZE DOWNTIME AND WASTE TO DRIVE MARKET
  • 11.4.2 MANUFACTURING: USE CASES
    • 11.4.2.1 Predictive maintenance & machinery inspection
    • 11.4.2.2 Material movement management
    • 11.4.2.3 Production planning
    • 11.4.2.4 Quality control
• 11.5 GOVERNMENT & DEFENSE
  • 11.5.1 DEMAND FOR AI-DRIVEN INSIGHTS FOR PRECISION POLICY DECISIONS TO DRIVE MARKET
  • 11.5.2 GOVERNMENT & DEFENSE: USE CASES
    • 11.5.2.1 Surveillance & situational awareness
    • 11.5.2.2 Law enforcement
    • 11.5.2.3 Intelligence analysis and data processing
    • 11.5.2.4 Simulation & training
• 11.6 HEALTHCARE & LIFE SCIENCES
  • 11.6.1 GROWING EMPHASIS ON EARLY DISEASE DETECTION AND PERSONALIZED TREATMENT TO DRIVE MARKET
  • 11.6.2 HEALTHCARE & LIFE SCIENCES: USE CASES
    • 11.6.2.1 Patient data and risk analysis
    • 11.6.2.2 Lifestyle management & monitoring and wearables
    • 11.6.2.3 In-patient care & hospital management
    • 11.6.2.4 Medical imaging and diagnostics
• 11.7 TECHNOLOGY & SOFTWARE PROVIDERS
  • 11.7.1 DEMAND FOR CUSTOMIZED CLOUD AI PLATFORMS ACROSS VERTICALS TO DRIVE MARKET
  • 11.7.2 TECHNOLOGY & SOFTWARE PROVIDER: USE CASES
    • 11.7.2.1 Big data analytics
    • 11.7.2.2 AI-driven software development
    • 11.7.2.3 Cybersecurity and fraud detection
    • 11.7.2.4 Robotics and automation
• 11.8 IT & TELECOM
  • 11.8.1 NEED FOR OPTIMIZED NETWORK MANAGEMENT AND ENHANCED CUSTOMER INTERACTION TO DRIVE MARKET
  • 11.8.2 IT & TELECOM: USE CASES
    • 11.8.2.1 AI-driven network analytics
    • 11.8.2.2 Chatbot-enhanced customer support
    • 11.8.2.3 Cloud-based unified communications
    • 11.8.2.4 Fraud detection in mobile services
• 11.9 ENERGY & UTILITIES
  • 11.9.1 INCREASED EMPHASIS ON IMPROVED ENERGY DISTRIBUTION AND RESOURCE MANAGEMENT TO DRIVE MARKET
  • 11.9.2 ENERGY & UTILITIES: USE CASES
    • 11.9.2.1 Energy demand forecasting
    • 11.9.2.2 Grid optimization & management
    • 11.9.2.3 Smart metering & energy data management
    • 11.9.2.4 Energy storage optimization
• 11.10 MEDIA & ENTERTAINMENT
  • 11.10.1 DEMAND FOR ENHANCED CONTENT CREATION, DISTRIBUTION, AND AUDIENCE ENGAGEMENT TO DRIVE MARKET
  • 11.10.2 MEDIA & ENTERTAINMENT: USE CASES
    • 11.10.2.1 Content recommendation engines
    • 11.10.2.2 Automated video editing
    • 11.10.2.3 Sentiment analysis for audience feedback
    • 11.10.2.4 Virtual and augmented reality experiences
• 11.11 AUTOMOTIVE, TRANSPORTATION, & LOGISTICS
  • 11.11.1 DEMAND FOR EFFICIENT ROUTING AND SCHEDULING TO DRIVE MARKET
  • 11.11.2 AUTOMOTIVE, TRANSPORTATION, & LOGISTICS: USE CASES
    • 11.11.2.1 Supply chain visibility and tracking
    • 11.11.2.2 Route optimization
    • 11.11.2.3 Driver assistance systems
    • 11.11.2.4 Smart logistics & warehousing
• 11.12 OTHER VERTICALS

12 CLOUD AI MARKET, BY REGION

• 12.1 INTRODUCTION
• 12.2 NORTH AMERICA
  • 12.2.1 NORTH AMERICA: MARKET DRIVERS
  • 12.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
  • 12.2.3 US
    • 12.2.3.1 Advancements in AI technologies, supportive ecosystem, and government initiatives to drive market
  • 12.2.4 CANADA
    • 12.2.4.1 Investments from tech companies to boost cloud environment
• 12.3 EUROPE
  • 12.3.1 EUROPE: MARKET DRIVERS
  • 12.3.2 EUROPE: MACROECONOMIC OUTLOOK
  • 12.3.3 UK
    • 12.3.3.1 AI-powered cloud solutions revolutionizing business landscape
  • 12.3.4 GERMANY
    • 12.3.4.1 Investments from tech giants to boost cloud AI demand
  • 12.3.5 FRANCE
    • 12.3.5.1 AI initiatives and investments in research and development to drive market
  • 12.3.6 ITALY
    • 12.3.6.1 Government investments in AI to support digital growth to drive market
  • 12.3.7 NORDIC
    • 12.3.7.1 Increasing adoption of cloud AI solutions in various sectors to drive market
  • 12.3.8 SPAIN
    • 12.3.8.1 Increasing AI adoption across industries seeking advanced analytics and automation to drive market
  • 12.3.9 REST OF EUROPE
• 12.4 ASIA PACIFIC
  • 12.4.1 ASIA PACIFIC: MARKET DRIVERS
  • 12.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
  • 12.4.3 CHINA
    • 12.4.3.1 Presence of strong local players to drive market
  • 12.4.4 JAPAN
    • 12.4.4.1 Increasing demand for AI-enabled solutions that enhance productivity and support digital transformation across sectors to drive market
  • 12.4.5 SOUTH KOREA
    • 12.4.5.1 Government-funded AI projects help local cloud vendors expand into global market
  • 12.4.6 AUSTRALIA & NEW ZEALAND
    • 12.4.6.1 Increasing application in healthcare, finance, and retail to drive demand for cloud AI
  • 12.4.7 INDIA
    • 12.4.7.1 Government initiatives to boost digital transformation to drive AI adoption
  • 12.4.8 REST OF ASIA PACIFIC
• 12.5 MIDDLE EAST & AFRICA
  • 12.5.1 MIDDLE EAST & AFRICA: MARKET DRIVERS
  • 12.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
  • 12.5.3 GULF COOPERATION COUNCIL (GCC)
    • 12.5.3.1 UAE
      • 12.5.3.1.1 Government initiatives and commitment to digital transformation to drive market
    • 12.5.3.2 Saudi Arabia
      • 12.5.3.2.1 Emphasis on AI development across various industry verticals to boost market
    • 12.5.3.3 Qatar
      • 12.5.3.3.1 Implementation of regulatory frameworks for AI adoption to drive market
    • 12.5.3.4 Rest of GCC countries
  • 12.5.4 SOUTH AFRICA
    • 12.5.4.1 Increasing investments in digital transformation and rising demand for AI-driven solutions across various sectors to drive market
  • 12.5.5 TURKEY
    • 12.5.5.1 National AI strategy and partnerships with major tech firms to drive market
  • 12.5.6 REST OF MIDDLE EAST & AFRICA
• 12.6 LATIN AMERICA
  • 12.6.1 LATIN AMERICA: MARKET DRIVERS
  • 12.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
  • 12.6.3 BRAZIL
    • 12.6.3.1 New AI initiatives by government and local vendors to boost market
  • 12.6.4 MEXICO
    • 12.6.4.1 Government-driven digital transformation to drive market
  • 12.6.5 ARGENTINA
    • 12.6.5.1 Increasing investments from global tech companies in AI partnerships and infrastructure to fuel market
  • 12.6.6 REST OF LATIN AMERICA

13 COMPETITIVE LANDSCAPE

• 13.1 INTRODUCTION
• 13.2 KEY PLAYER STRATEGIES/RIGHT TO WIN, 2021-2024
• 13.3 MARKET SHARE ANALYSIS, 2023
• 13.4 BRAND/PRODUCT COMPARISON
  • 13.4.1 IBM - IBM WATSON STUDIO
  • 13.4.2 GOOGLE - VERTEX AI
  • 13.4.3 MICROSOFT - AZURE AI
  • 13.4.4 AWS - AWS SAGEMAKER
  • 13.4.5 ORACLE - GENERATIVE AI SERVICES
• 13.5 REVENUE ANALYSIS, 2019-2023
• 13.6 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
  • 13.6.1 STARS
  • 13.6.2 EMERGING LEADERS
  • 13.6.3 PERVASIVE PLAYERS
  • 13.6.4 PARTICIPANTS
  • 13.6.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
    • 13.6.5.1 Company footprint
    • 13.6.5.2 Region footprint
    • 13.6.5.3 Offering footprint
    • 13.6.5.4 Technology type footprint
    • 13.6.5.5 Vertical footprint
• 13.7 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
  • 13.7.1 PROGRESSIVE COMPANIES
  • 13.7.2 RESPONSIVE COMPANIES
  • 13.7.3 DYNAMIC COMPANIES
  • 13.7.4 STARTING BLOCKS
  • 13.7.5 COMPETITIVE BENCHMARKING: STARTUP/SMES, 2023
    • 13.7.5.1 Detailed list of key startups/SMEs
    • 13.7.5.2 Competitive benchmarking of startups/SMEs
• 13.8 COMPANY VALUATION AND FINANCIAL METRICS
• 13.9 COMPETITIVE SCENARIO
  • 13.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
  • 13.9.2 DEALS

14 COMPANY PROFILES

• 14.1 INTRODUCTION
• 14.2 MAJOR PLAYERS
  • 14.2.1 GOOGLE
    • 14.2.1.1 Business overview
    • 14.2.1.2 Products/Solutions/Services offered
    • 14.2.1.3 Recent developments
      • 14.2.1.3.1 Product launches and enhancements
      • 14.2.1.3.2 Deals
      • 14.2.1.3.3 Expansions
    • 14.2.1.4 MnM view
      • 14.2.1.4.1 Right to win
      • 14.2.1.4.2 Strategic choices
      • 14.2.1.4.3 Weaknesses and competitive threats
  • 14.2.2 IBM
    • 14.2.2.1 Business overview
    • 14.2.2.2 Products/Solutions/Services offered
    • 14.2.2.3 Recent developments
      • 14.2.2.3.1 Product launches and enhancements
      • 14.2.2.3.2 Deals
    • 14.2.2.4 MnM view
      • 14.2.2.4.1 Right to win
      • 14.2.2.4.2 Strategic choices
      • 14.2.2.4.3 Weaknesses and competitive threats
  • 14.2.3 AWS
    • 14.2.3.1 Business overview
    • 14.2.3.2 Products/Solutions/Services offered
    • 14.2.3.3 Recent developments
      • 14.2.3.3.1 Product launches and enhancements
      • 14.2.3.3.2 Deals
      • 14.2.3.3.3 Others
    • 14.2.3.4 MnM view
      • 14.2.3.4.1 Right to win
      • 14.2.3.4.2 Strategic choices
      • 14.2.3.4.3 Weaknesses and competitive threats
  • 14.2.4 MICROSOFT
    • 14.2.4.1 Business overview
    • 14.2.4.2 Products/Solutions/Services offered
    • 14.2.4.3 Recent developments
      • 14.2.4.3.1 Product launches and enhancements
      • 14.2.4.3.2 Deals
    • 14.2.4.4 MnM view
      • 14.2.4.4.1 Right to win
      • 14.2.4.4.2 Strategic choices
      • 14.2.4.4.3 Weaknesses and competitive threats
  • 14.2.5 ORACLE
    • 14.2.5.1 Business overview
    • 14.2.5.2 Products/Solutions/Services offered
    • 14.2.5.3 Recent developments
      • 14.2.5.3.1 Product launches and enhancements
      • 14.2.5.3.2 Deals
    • 14.2.5.4 MnM view
      • 14.2.5.4.1 Right to win
      • 14.2.5.4.2 Strategic choices
      • 14.2.5.4.3 Weaknesses and competitive threats
  • 14.2.6 NVIDIA
    • 14.2.6.1 Business overview
    • 14.2.6.2 Products/Solutions/Services offered
    • 14.2.6.3 Recent developments
      • 14.2.6.3.1 Product launches and enhancements
      • 14.2.6.3.2 Deals
  • 14.2.7 SALESFORCE
    • 14.2.7.1 Business overview
    • 14.2.7.2 Products/Solutions/Services offered
    • 14.2.7.3 Recent developments
      • 14.2.7.3.1 Product launches and enhancements
      • 14.2.7.3.2 Deals
  • 14.2.8 SAP
    • 14.2.8.1 Business overview
    • 14.2.8.2 Products/Solutions/Services offered
    • 14.2.8.3 Recent developments
      • 14.2.8.3.1 Product launches and enhancements
      • 14.2.8.3.2 Deals
  • 14.2.9 ALIBABA CLOUD
    • 14.2.9.1 Business overview
    • 14.2.9.2 Products/Solutions/Services offered
    • 14.2.9.3 Recent developments
      • 14.2.9.3.1 Product launches and enhancements
      • 14.2.9.3.2 Deals
  • 14.2.10 HPE
    • 14.2.10.1 Business overview
    • 14.2.10.2 Products/Solutions/Services offered
    • 14.2.10.3 Recent developments
      • 14.2.10.3.1 Product launches and enhancements
      • 14.2.10.3.2 Deals
  • 14.2.11 INTEL
    • 14.2.11.1 Business overview
    • 14.2.11.2 Products/Solutions/Services offered
    • 14.2.11.3 Recent developments
      • 14.2.11.3.1 Product launches and enhancements
      • 14.2.11.3.2 Deals
• 14.3 OTHER PLAYERS
  • 14.3.1 TENCENT CLOUD
  • 14.3.2 OPENAI
  • 14.3.3 BAIDU
  • 14.3.4 HUAWEI
  • 14.3.5 C3 AI
  • 14.3.6 CLOUDERA
  • 14.3.7 ALTAIR
  • 14.3.8 INFRACLOUD TECHNOLOGIES
  • 14.3.9 CLOUDMINDS
• 14.4 STARTUPS/SMES
  • 14.4.1 DATAROBOT
  • 14.4.2 COHERE
  • 14.4.3 GLEAN
  • 14.4.4 H2O.AI
  • 14.4.5 SCALE AI
  • 14.4.6 INFLECTION AI
  • 14.4.7 ANYSCALE
  • 14.4.8 FRAME.AI
  • 14.4.9 DATAIKU
  • 14.4.10 YELLOW.AI
  • 14.4.11 VISO.AI

15 ADJACENT/RELATED MARKETS

• 15.1 INTRODUCTION
• 15.2 RELATED MARKETS
• 15.3 LIMITATIONS
• 15.4 ARTIFICIAL INTELLIGENCE (AI) MARKET
• 15.5 AI INFRASTRUCTURE MARKET

16 APPENDIX

• 16.1 DISCUSSION GUIDE
• 16.2 KNOWLEDGESTORE: MARKETSANDMARKETS' SUBSCRIPTION PORTAL
• 16.3 CUSTOMIZATION OPTIONS
• 16.4 RELATED REPORTS
• 16.5 AUTHOR DETAILS