세계의 클라우드 AI 시장 : 제공별, 기술 유형별, 호스팅 유형별, 조직 규모별, 비즈니스 기능별, 산업별, 지역별 - 예측(-2029년)
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
상품코드:1608639
리서치사:MarketsandMarkets
발행일:2024년 12월
페이지 정보:영문 387 Pages
라이선스 & 가격 (부가세 별도)
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한글목차
클라우드 AI 세계 시장은 2024년 803억 달러에서 2029년 3,271억 5,000만 달러로 성장해 예측 기간 동안 32.4%의 CAGR을 기록할 것으로 예상됩니다.
클라우드 AI는 제조, 의료, 금융, 소매 등 산업 전반의 기술 활용을 변화시키고 있습니다. 예를 들어, 병원은 클라우드 AI를 사용하여 건강 추세를 예측하고 의료 데이터를 신속하게 평가하여 의사가 환자에게 더 나은 결정을 내릴 수 있도록 돕고 있습니다.
조사 범위
조사 대상 연도
2019-2029년
기준 연도
2024년
예측 기간
2024-2029년
검토 단위
달러(10억 달러)
부문
제품별, 기술 유형별, 호스팅 유형별, 조직 규모별, 비즈니스 기능별, 산업별, 지역별
대상 지역
북미, 유럽, 아시아태평양, 중동 및 아프리카, 라틴아메리카
클라우드 AI는 하드웨어 투자 없이도 강력한 컴퓨팅과 데이터 분석을 제공하기 때문에 점점 더 많은 기업들이 클라우드 AI를 활용하고 있습니다. 이를 통해 기업은 실시간 인사이트, 예측, 자동화에 AI를 활용하여 업무 효율성을 높이고, 비용을 절감하며, 핵심 업무에 집중할 수 있습니다.
AIaaS(AI-as-a-Service)는 클라우드 AI 시장에서 가장 높은 성장세를 보일 것으로 예상되며, AIaaS는 기업이 고가의 인프라나 전문 지식에 투자하지 않고도 고급 AI 도구에 접근할 수 있는 기회를 제공하며, AIaaS의 가장 큰 장점은 확장성과 유연성입니다. 기업은 필요에 따라 AI 기능을 신속하게 변경할 수 있기 때문에 모든 규모의 기업에 유용합니다. 또한, AIaaS 제공 업체는 종종 기존 시스템과의 연결을 용이하게 하는 간단한 인터페이스와 도구를 갖추고 있기 때문에 기업이 풍부한 기술 기술을 필요로 하지 않기 때문에 시스템을 구축 및 관리할 리소스가 없는 중소기업도 AI를 쉽게 사용할 수 있습니다.
AIaaS는 기업이 고객 경험을 개선하고 운영 효율을 높이려는 노력의 일환으로 인기가 높아지고 있으며, AIaaS의 AutoML(자동 머신러닝)과 사전 학습된 모델 개발은 AI 애플리케이션의 개발 및 활용을 용이하게함으로써 이러한 추세에 힘을 실어주고 있습니다. 기업들이 데이터 기반 의사결정을 도입함에 따라 AIaaS는 데이터의 복잡성을 극복하고 혁신을 추진하는 데 있어 매우 중요한 역할을 할 것으로 예상됩니다.
이 보고서는 세계 클라우드 AI 시장을 조사하여 제공별, 기술 유형별, 호스팅 유형별, 조직 규모별, 비즈니스 기능별, 산업별, 지역별 동향, 시장 진입 기업 개요 등을 정리한 보고서입니다.
목차
제1장 소개
제2장 조사 방법
제3장 주요 요약
제4장 주요 인사이트
제5장 시장 개요와 업계 동향
소개
시장 역학
사례 연구 분석
생태계 분석
공급망 분석
가격 분석
특허 분석
기술 분석
규제 상황
Porter's Five Forces 분석
주요 이해관계자와 구입 기준
2024-2025년의 주요 회의와 이벤트
고객 비즈니스에 영향을 미치는 동향/혼란
비즈니스 모델 분석
투자와 자금 조달 시나리오
AI/생성형 AI가 클라우드 AI 시장에 미치는 영향
클라우드의 AI의 미래
AI 클라우드 이용 사례
제6장 클라우드 AI 시장, 제공별
소개
인프라
AIaaS
제7장 클라우드 AI 시장, 기술 유형별
소개
생성형 AI
기타
제8장 클라우드 AI 시장, 호스팅 유형별
소개
매니지드 호스팅
셀프 호스팅
제9장 클라우드 AI 시장, 조직 규모별
소개
대기업
중소기업
제10장 클라우드 AI 시장, 비즈니스 기능별
소개
마케팅
판매
인사
재무·회계
오퍼레이션과 공급망
제11장 클라우드 AI 시장, 업계별
소개
BFSI
소매·E-Commerce
제조
정부·방위
헬스케어와 생명과학
기술·소프트웨어 프로바이더
IT·통신
에너지·유틸리티
미디어·엔터테인먼트
자동차, 운송, 물류
기타
제12장 클라우드 AI 시장, 지역별
소개
북미
북미 : 시장 성장 촉진요인
북미 : 거시경제 전망
미국
캐나다
유럽
유럽 : 시장 성장 촉진요인
유럽 : 거시경제 전망
영국
독일
프랑스
이탈리아
노르딕
스페인
기타
아시아태평양
아시아태평양 : 시장 성장 촉진요인
아시아태평양 : 거시경제 전망
중국
일본
한국
호주와 뉴질랜드
인도
기타
중동 및 아프리카
중동 및 아프리카 : 시장 성장 촉진요인
중동 및 아프리카 : 거시경제 전망
걸프협력회의(GCC)
남아프리카공화국
터키
기타
라틴아메리카
라틴아메리카 : 시장 성장 촉진요인
라틴아메리카 : 거시경제 전망
브라질
멕시코
아르헨티나
기타
제13장 경쟁 구도
소개
주요 진출 기업 전략/강점, 2021-2024년
시장 점유율 분석, 2023년
브랜드/제품 비교
매출 분석, 2019-2023
기업 평가 매트릭스 : 주요 진출 기업, 2023년
기업 평가 매트릭스 : 스타트업/중소기업, 2023년
기업 가치 평가와 재무 지표
경쟁 시나리오
제14장 기업 개요
소개
주요 진출 기업
GOOGLE
IBM
AWS
MICROSOFT
ORACLE
NVIDIA
SALESFORCE
SAP
ALIBABA CLOUD
HPE
INTEL
기타 기업
TENCENT CLOUD
OPENAI
BAIDU
HUAWEI
C3 AI
CLOUDERA
ALTAIR
INFRACLOUD TECHNOLOGIES
CLOUDMINDS
STARTUPS/SMES
DATAROBOT
COHERE
GLEAN
H2O.AI
SCALE AI
INFLECTION AI
ANYSCALE
FRAME.AI
DATAIKU
YELLOW.AI
VISO.AI
제15장 인접/관련 시장
제16장 부록
ksm
영문 목차
영문목차
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.
Scope of the Report
Years Considered for the Study
2019-2029
Base Year
2024
Forecast Period
2024-2029
Units Considered
USD (Billion)
Segments
Offering, Technology Type, Hosting Type, Organization Size, Business Function, Verticals
Regions covered
North America, Europe, Asia Pacific, Middle East Africa, and Latin America
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