Causal AI Market by Offering, Application - Global Forecast to 2030
상품코드:1617391
리서치사:MarketsandMarkets
발행일:2024년 12월
페이지 정보:영문 332 Pages
라이선스 & 가격 (부가세 별도)
ㅁ Add-on 가능: 고객의 요청에 따라 일정한 범위 내에서 Customization이 가능합니다. 자세한 사항은 문의해 주시기 바랍니다.
한글목차
세계의 코절 AI 시장 규모는 2024년 5,620만 달러에서 2030년까지 4억 5,680만 달러에 달할 것으로 예측되며, 예측 기간에 CAGR로 41.8%강력한 성장이 전망됩니다.
의료, 금융, 자율주행차 등 기존 AI 접근 방식으로는 예측의 인과관계를 파악하는 데 어려움을 겪고 있는 산업에서 고급 의사결정 툴에 대한 수요가 증가하면서 이러한 성장세를 견인하고 있습니다. 또한 인과관계 파악에서 인과관계에 기반한 계획 실행으로 초점이 이동함에 따라 특히 신속한 분석과 맞춤형 서비스에서 다양한 산업에서 코즈알 AI를 도입하는 것이 중요해지고 있습니다. 하지만 인과관계 추론 모델을 구축하고 실행에 옮기기까지의 복잡한 프로세스가 시장에 큰 걸림돌로 작용하고 있습니다. 이를 위해서는 방대한 지식과 컴퓨팅 리소스가 필요하므로 중소기업의 도입이 제한될 수 있습니다. 또한 데이터 프라이버시 및 규제 준수에 대한 우려는 데이터 가용성과 활용을 방해하고 있으며, 이는 혁신과 윤리적 문제 사이의 균형을 맞추는 것이 얼마나 어려운 일인지 잘 보여줍니다.
조사 범위
조사 대상년
2019-2030년
기준년
2023년
예측 기간
2024-2030년
단위
100만 달러
부문
제공, 용도, 업계, 지역
대상 지역
북미, 유럽, 아시아태평양, 중동 및 아프리카, 라틴아메리카
"제공별로는 소프트웨어 부문이 예측 기간 중 가장 큰 시장 점유율을 차지할 것으로 예상됩니다."
예측 기간 중 소프트웨어 부문은 조직이 의사결정에 고급 인과관계 추론 기능을 활용할 수 있도록 지원함으로써 코절 AI 시장에서 가장 큰 시장 점유율을 차지할 것으로 예상됩니다. 코절 AI 기술은 기존의 예측 분석을 넘어 인과관계를 발견할 수 있는 툴와 플랫폼을 기업에 제공합니다. 이 능력은 복잡하고 끊임없이 변화하는 환경에서 정보에 입각한 의사결정을 내리려는 기업에게 점점 더 중요해지고 있습니다. 소프트웨어 솔루션은 의료, 금융, 소매, 제조 등의 분야에서 기존 시스템을 개선, 맞춤화, 통합하여 접근성과 유연성을 향상시킬 수 있습니다. 또한 AI 플랫폼의 빠른 발전, 클라우드 기반 배포 옵션, 사용하기 쉬운 인터페이스는 소프트웨어의 채택을 증가시키고 있습니다. 기업은 코절 AI 기술을 활용해 업무 개선, 고객 대응 강화, 데이터 분석을 통한 리스크 관리 강화, 실용적인 인사이트을 위한 데이터 분석에 활용하고 있습니다.
"산업별로는 의료 및 생명과학 분야가 예측 기간 중 가장 빠른 시장 성장률을 나타낼 것으로 예상됩니다."
의료 및 생명과학 산업은 맞춤형 의료 및 의약품 개발을 혁신하고 환자 치료를 강화할 것으로 예상됨에 따라 코즈얼 AI 시장이 급성장할 것으로 예상됩니다. 코절 AI는 의료 프로바이더와 연구자들이 인과관계를 밝혀내어 질병 발생, 치료 효과 및 전반적인 건강 결과에 대한 이해를 향상시킬 수 있도록 돕습니다. 이러한 능력은 임상적 의사결정을 개선하고, 치료의 시행착오를 최소화하며, 건강 상태에 영향을 미치는 영향력 있는 요인을 인식함으로써 의약품 개발 과정을 가속화할 수 있습니다. 또한 의학 연구에서 코자르 AI는 대규모 데이터세트를 분석할 때 교란 요인을 고려하면서 인과관계를 이해하고, 단순한 상관관계가 아닌 인과관계를 파악하는 것이 매우 중요합니다. 의료 기관은 비용 관리와 환자 결과 및 업무 효율성 향상을 위해 예측 및 처방 분석에 대한 요구가 증가함에 따라 코절 AI의 활용을 늘리고 있습니다. 전자의무기록, 웨어러블 의료기기 등 의료 데이터의 디지털화도 이 분야의 성장을 가속하고 코절 AI를 활용할 수 있는 기회를 창출하고 있습니다.
세계의 코절 AI 시장에 대해 조사분석했으며, 주요 촉진요인과 억제요인, 경쟁 구도, 향후 동향등의 정보를 제공하고 있습니다.
It is anticipated that the Causal AI market will experience substantial growth, increasing from USD 56.2 million in 2024 to USD 456.8 million by 2030, with a strong CAGR of 41.8% throughout the forecast period. The rise is fueled by growing demand for advanced decision-making tools in industries such as healthcare, finance, and autonomous vehicles, where traditional AI approaches struggle to clarify the causal relationships behind predictions. Moreover, the increasing significance of employing Causal AI across different industries is evident, particularly in swift analysis and tailored services, as the focus shifts from identifying relationships to executing plans rooted in causality. However, significant obstacles are being faced by the market due to the complex process of constructing and putting into effect causal inference models. This requires extensive knowledge and computational resources, possibly restricting smaller companies from adopting them. Moreover, worries about data privacy and adhering to regulations still hinder the availability and use of data, highlighting the difficulty of balancing innovation with ethical concerns.
Scope of the Report
Years Considered for the Study
2019-2030
Base Year
2023
Forecast Period
2024-2030
Units Considered
USD (Million)
Segments
Offering, Application, Vertical and Region
Regions covered
North America, Europe, Asia Pacific, Middle East & Africa, and Latin America
"By offering, software segment is expected to have the largest market share during the forecast period"
During the forecast period, the software segment is expected to have largest market share in the causal AI market by enabling organizations to leverage advanced causal inference capabilities for decision-making. Causal AI technology provides businesses with tools and platforms to discover cause and effect connections, going beyond traditional predictive analytics. This ability is increasingly crucial for companies looking to make well-informed decisions in complex, constantly changing environments. Software solutions can improve, customize, and integrate with existing systems to increase accessibility and flexibility in sectors such as healthcare, finance, retail, and manufacturing. Moreover, the quick advancement of AI platforms, cloud-based deployment choices, and easy-to-use interfaces has also increased the adoption of software. Businesses are using causal AI technology to improve operations, enhance customer interactions, and enhance risk management through analyzing data for actionable insights.
"By vertical, Healthcare & Life sciences is expected to register the fastest market growth rate during the forecast period."
The healthcare and life sciences industry is forecasted to experience fast growth in the causal AI market as it holds promise for transforming personalized medicine, drug development, and enhancing patient care. Causal AI enables healthcare providers and researchers to uncover causal connections, resulting in improved comprehension of disease development, treatment efficacy, and overall health outcomes. This capacity improves clinical decision-making, minimizes trial-and-error in treatments, and speeds up drug development processes by recognizing influential factors affecting health conditions. Furthermore, in medical research, it is crucial for causal AI to analyze large datasets while considering confounding variables in order to understand causality instead of just correlation. Healthcare organizations are increasingly using causal AI to meet the growing need for predictive and prescriptive analytics in order to control costs, boost patient outcomes, and improve operational efficiency. Advancements in digitizing medical data, including electronic health records and wearable health devices, are also driving growth in the sector, creating opportunities for causal AI applications.
"By Region, North America to have the largest market share in 2024, and Asia Pacific is slated to grow at the fastest rate during the forecast period."
North America is projected to be at the forefront of the causal AI market by 2024, as a result of its advanced technology, significant investments in AI R&D, and the major presence of key companies like Google, IBM, and Microsoft. The area has developed a strong atmosphere that supports the application of causal AI across sectors like healthcare, finance, and manufacturing, giving an advantage in competition. Additionally, its significant impact in the field is reinforced by top educational establishments and a dedication to fostering innovation. However, the Asia Pacific (APAC) area is expected to experience the most rapid expansion in the estimated period because of rapid digital transformation and growing enthusiasm for AI-driven solutions in nations like China, Japan, and India. The rapid growth of the region is fueled by the increasing embrace of AI in industries such as e-commerce, automotive production, and finance, combined with significant backing and funding for AI research from the government. Moreover, an increasing number of technology proficient individuals and the flourishing startup culture in APAC are leading to a demand for informal AI programs, positioning it as a rapidly growing sector in the times ahead.
Breakdown of primaries
In-depth interviews were conducted with Chief Executive Officers (CEOs), innovation and technology directors, system integrators, and executives from various key organizations operating in the Causal AI market.
By Company: Tier I - 17%, Tier II - 26%, and Tier III - 57%
By Designation: D-Level Executives - 47%, C-Level Executives - 19%, and others - 34%
By Region: North America - 45%, Europe - 20%, Asia Pacific - 24%, Middle East & Africa - 7%, and Latin America - 4%
The report includes the study of key players offering Causal AI solutions. It profiles major vendors in the Causal AI market. The major players in the Causal AI market include IBM (US), Google (US), Microsoft (US), Dynatrace (US), Cognizant (US), Logility (US), Datarobot (US), CausaLens (UK), Aitia (US), Taskade (US), Causely (US), Causaly (UK), Causality Link (US), Xplain data (Germany), Parabole.AI (US), Datma (US), Incrmntl (Israel), Scalnyx (France), Geminos (US), Data Poem (US), CausaAI (Netherlands), Causa (UK), Lifesight (US), Actable AI (UK), biotx.ai (Germany), Howso (US), VELDT (Japan), and CML Insight (US).
Research coverage
This research report categorizes the Causal AI Market by offering (software and services), by application (financial management, sales & customer management, operations & supply chain management, marketing & pricing management, and other applications), by vertical (BFSI, healthcare & life sciences, retail & e-commerce, manufacturing, transportation & logistics, media & entertainment, telecommunications, energy & utilities, and other verticals) and by Region (North America, Europe, Asia Pacific, Middle East & Africa, and Latin America). The scope of the report covers detailed information regarding the major factors, such as drivers, restraints, challenges, and opportunities, influencing the growth of the Causal AI market. A detailed analysis of the key industry players has been done to provide insights into their business overview, solutions, and services; key strategies; contracts, partnerships, agreements, new product & service launches, mergers and acquisitions, and recent developments associated with the Causal AI market. Competitive analysis of upcoming startups in the Causal AI market ecosystem is covered in this report.
Key Benefits of Buying the Report
The report would provide the market leaders/new entrants in this market with information on the closest approximations of the revenue numbers for the overall Causal AI market and its subsegments. It would help stakeholders understand the competitive landscape and gain more insights better to position their business and plan suitable go-to-market strategies. It also helps stakeholders understand the pulse of the market and provides them with information on key market drivers, restraints, challenges, and opportunities.
The report provides insights on the following pointers:
Analysis of key drivers ( Increasing Demand for Explainable AI in Regulated Industries, Growing demand for Robust Counterfactual Analysis, Surge in Demand for Predictive Maintenance and Root Cause Analysis, Shift from Predictive to Causal AI based Prescriptive Analytics), restraints (Lack of Standardized Tools and Frameworks for Causal Inference, High Computational Costs for Causal Modeling), opportunities (Causal AI in Precision Healthcare and Drug Discovery, Scalable Causal Inference APIs for Real-Time Applications , Integrating Causal AI with IoT for Real-Time Decision Making), and challenges (Complexity of Causal Model Development and Interpretability, Data Quality and Availability for Causal Inference).
Product Development/Innovation: Detailed insights on upcoming technologies, research & development activities, and new product & service launches in the Causal AI market.
Market Development: Comprehensive information about lucrative markets - the report analyses the Causal AI market across varied regions.
Market Diversification: Exhaustive information about new products & services, untapped geographies, recent developments, and investments in the Causal AI market.
Competitive Assessment: In-depth assessment of market shares, growth strategies and service offerings of leading players like IBM (US), Google (US), Microsoft (US), Dynatrace (US), Cognizant (US), Logility (US), Datarobot (US), CausaLens (UK), Aitia (US), Taskade (US), Causely (US), Causaly (UK), Causality Link (US), Xplain data (Germany), Parabole.AI (US), Datma (US), Incrmntl (Israel), Scalnyx (France), Geminos (US), Data Poem (US), CausaAI (Netherlands), Causa (UK), Lifesight (US), Actable AI (UK), biotx.ai (Germany), Howso (US), VELDT (Japan), and CML Insight (US) among others in the Causal AI market. The report also helps stakeholders understand the pulse of the Causal AI market and provides them with information on key market drivers, restraints, challenges, and opportunities.
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
1.6 SUMMARY OF CHANGES
2 RESEARCH METHODOLOGY
2.1 RESEARCH DATA
2.1.1 SECONDARY DATA
2.1.2 PRIMARY DATA
2.1.2.1 Breakup of primary profiles
2.1.2.2 Key industry insights
2.2 MARKET BREAKUP AND DATA TRIANGULATION
2.3 MARKET SIZE ESTIMATION
2.3.1 TOP-DOWN APPROACH
2.3.2 BOTTOM-UP APPROACH
2.4 MARKET FORECAST
2.5 RESEARCH ASSUMPTIONS
2.6 RESEARCH LIMITATIONS
3 EXECUTIVE SUMMARY
4 PREMIUM INSIGHTS
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN CAUSAL AI MARKET
4.2 CAUSAL AI MARKET: TOP THREE APPLICATIONS
4.3 NORTH AMERICA: CAUSAL AI MARKET, BY APPLICATION AND VERTICAL
4.4 CAUSAL AI MARKET, BY REGION
5 MARKET OVERVIEW AND INDUSTRY TRENDS
5.1 INTRODUCTION
5.2 MARKET DYNAMICS
5.2.1 DRIVERS
5.2.1.1 Increasing demand for explainable AI in regulated industries
5.2.1.2 Growing demand for robust counterfactual analysis
5.2.1.3 Surge in demand for predictive maintenance and root cause analysis
5.2.1.4 Shift from predictive to causal AI-based prescriptive analytics
5.2.2 RESTRAINTS
5.2.2.1 Lack of standardized tools and frameworks for causal inference
5.2.2.2 High computational costs for causal modeling
5.2.3 OPPORTUNITIES
5.2.3.1 Causal AI in precision healthcare and drug discovery
5.2.3.2 Scalable causal inference APIs for real-time applications
5.2.3.3 Integrating causal AI with IoT for real-time decision making
5.2.4 CHALLENGES
5.2.4.1 Complexity of causal model development and interpretability
5.2.4.2 Data quality and availability for causal inference
5.3 EVOLUTION OF CAUSAL AI
5.4 SUPPLY CHAIN ANALYSIS
5.5 ECOSYSTEM ANALYSIS
5.5.1 CAUSAL AI PLATFORM PROVIDERS
5.5.2 CAUSAL AI TOOL PROVIDERS
5.5.3 CAUSAL AI TOOLKITS AND APIS PROVIDERS
5.5.4 CAUSAL AI SERVICE PROVIDERS
5.6 INVESTMENT LANDSCAPE AND FUNDING SCENARIO
5.7 IMPACT OF GENERATIVE AI IN CAUSAL AI MARKET
5.7.1 ENHANCED DATA AVAILABILITY FOR CAUSAL ANALYSIS
5.7.2 STRESS TESTING OF CAUSAL MODELS
5.7.3 SUPPORT FOR COMPLEX MULTIVARIABLE ANALYSIS
5.7.4 ACCELERATED MODEL DEVELOPMENT
5.7.5 BIAS REDUCTION FOR FAIRER OUTCOMES
5.7.6 DYNAMIC SIMULATIONS FOR CAUSAL TESTING
5.8 PRICING ANALYSIS
5.8.1 PRICING DATA, BY OFFERING
5.8.2 PRICING DATA, BY APPLICATION
5.9 CASE STUDY ANALYSIS
5.9.1 CASE STUDY 1: DYNATRACE BOOSTS BMO'S DIGITAL EFFICIENCY WITH CAUSAL AI-POWERED INSIGHTS AND AUTOMATION
5.9.2 CASE STUDY 2: FINGERSOFT ACHIEVES DATA-DRIVEN MARKETING OPTIMIZATION WITH INCRMNTAL'S CAUSAL AI INSIGHTS
5.9.3 CASE STUDY 3: ACCELERATING FAULT DETECTION WITH CAUSAL AI FOR ENHANCED PRODUCT RELIABILITY IN MANUFACTURING
5.9.4 CASE STUDY 4: LEVERAGING CAUSAL AI FOR ENHANCED ROOT CAUSE ANALYSIS IN TRUMPF'S EQUIPMENT MAINTENANCE
5.9.5 CASE STUDY 5: CAUSA TECH ENHANCED OPERATIONAL EFFICIENCY FOR LEADING MANUFACTURING FIRM, STRENGTHENING SUPPLY CHAIN RESILIENCE
5.9.6 CASE STUDY 6: LIFESIGHT ADDRESSING KEY CHALLENGES IN MARKETING, ENHANCING EFFICIENCY AND SALES FOR DTC BEAUTY BRAND
5.10 TECHNOLOGY ANALYSIS
5.10.1 KEY TECHNOLOGIES
5.10.1.1 Causal inference algorithms
5.10.1.2 Explainable AI (XAI)
5.10.1.3 Structural equation modeling (SEM)
5.10.1.4 Bayesian networks
5.10.1.5 Causal graphs
5.10.2 COMPLEMENTARY TECHNOLOGIES
5.10.2.1 Machine learning
5.10.2.2 Reinforcement learning
5.10.2.3 Data engineering
5.10.2.4 Knowledge graphs
5.10.3 ADJACENT TECHNOLOGIES
5.10.3.1 Predictive analytics
5.10.3.2 Decision intelligence
5.10.3.3 Synthetic data generation
5.10.3.4 Natural language processing (NLP)
5.11 REGULATORY LANDSCAPE
5.11.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS
5.11.2 REGULATIONS: CAUSAL AI
5.11.2.1 North America
5.11.2.1.1 Blueprint for AI Bill of Rights (US)
5.11.2.1.2 Directive on Automated Decision-Making (Canada)
5.11.2.2 Europe
5.11.2.2.1 UK AI Regulation White Paper
5.11.2.2.2 Gesetz zur Regulierung Kunstlicher Intelligenz (AI Regulation Law - Germany)
5.11.2.2.3 Loi pour une Republique numerique (Digital Republic Act - France)
5.11.2.2.4 Codice in materia di protezione dei dati personali (Data Protection Code - Italy)
5.11.2.2.5 Ley de Servicios Digitales (Digital Services Act - Spain)
5.11.2.2.6 Dutch Data Protection Authority (Autoriteit Persoonsgegevens) Guidelines
5.11.2.2.7 Swedish National Board of Trade AI Guidelines
5.11.2.2.8 Danish Data Protection Agency (Datatilsynet) AI Recommendations
5.11.2.2.9 Artificial Intelligence 4.0 (AI 4.0) Program - Finland
5.11.2.3 Asia Pacific
5.11.2.3.1 Personal Data Protection Bill (PDPB) & National Strategy on AI (NSAI) - India
5.11.2.3.2 Basic Act on Advancement of Utilizing Public and Private Sector Data & AI Guidelines - Japan
5.11.2.3.3 New Generation Artificial Intelligence Development Plan & AI Ethics Guidelines - China
5.11.2.3.4 Framework Act on Intelligent Informatization - South Korea
5.11.2.3.5 AI Ethics Framework (Australia) & AI Strategy (New Zealand)
5.11.2.3.6 Model AI Governance Framework - Singapore
5.11.2.3.7 National AI Framework - Malaysia
5.11.2.3.8 National AI Roadmap - Philippines
5.11.2.4 Middle East & Africa
5.11.2.4.1 Saudi Data & Artificial Intelligence Authority (SDAIA) Regulations
5.11.2.4.2 UAE National AI Strategy 2031
5.11.2.4.3 Qatar National AI Strategy
5.11.2.4.4 National Artificial Intelligence Strategy (2021-2025) - Turkey
