Cloud AI Market by Cloud AI Infrastructure (Compute, Storage, Network), AI & ML Platforms (Auto ML), MLOps and Lifecycle Management (AI Workflow Orchestration), AIaaS, Technology (Generative AI and Other AI) - Global Forecast to 2029
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.
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).
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%
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).
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
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
