Causal AI Market by Offering (Causal AI Platforms, Causal Discovery, Causal Inference, Causal Modelling, Root Cause Analysis), Application (Financial Management, Sales & Customer Management, Operations & Supply Chain Management) - Global Forecast to 2030
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.
“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 casual 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.
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:
“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 casual 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%
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.
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 Kьnstlicher Intelligenz (AI Regulation Law - Germany)
5.11.2.2.3 Loi pour une Rйpublique numйrique (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
5.11.2.4.5 African Union (AU) AI Framework
5.11.2.4.6 Egyptian Artificial Intelligence Strategy
5.11.2.4.7 Kuwait National Development Plan (New Kuwait Vision 2035)
5.11.2.5 Latin America
5.11.2.5.1 Brazilian General Data Protection Law (LGPD)
5.11.2.5.2 Federal Law on Protection of Personal Data Held by Private Parties - Mexico
5.11.2.5.3 Argentina Personal Data Protection Law (PDPL) & AI Ethics Framework
5.11.2.5.4 Chilean Data Protection Law & National AI Policy
5.11.2.5.5 Colombian Data Protection Law (Law 1581) & AI Ethics Guidelines
5.11.2.5.6 Peruvian Personal Data Protection Law & National AI Strategy
5.12 PATENT ANALYSIS
5.12.1 METHODOLOGY
5.12.2 PATENTS FILED, BY DOCUMENT TYPE
5.12.3 INNOVATION AND PATENT APPLICATIONS
5.13 KEY CONFERENCES AND EVENTS (2024–2025)
5.14 PORTER’S FIVE FORCES ANALYSIS
5.14.1 THREAT OF NEW ENTRANTS
5.14.2 THREAT OF SUBSTITUTES
5.14.3 BARGAINING POWER OF SUPPLIERS
5.14.4 BARGAINING POWER OF BUYERS
5.14.5 INTENSITY OF COMPETITIVE RIVALRY
5.15 KEY STAKEHOLDERS & BUYING CRITERIA
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.15.2 BUYING CRITERIA
5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
6 CAUSAL AI MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: CAUSAL AI MARKET DRIVERS
6.2 SOFTWARE
6.2.1 RISING DEMAND FOR DATA-DRIVEN DECISIONS DRIVES GROWTH IN INDUSTRY-SPECIFIC CAUSAL AI SOFTWARE
6.2.2 CAUSAL AI PLATFORMS
6.2.3 CAUSAL AI SOLUTIONS
6.2.3.1 Causal discovery
6.2.3.2 Causal modeling
6.2.3.3 Decision intelligence
6.2.3.4 Root-cause analysis
6.2.3.5 Causal AI APIs
6.2.3.6 Software development kits
6.3 SERVICES
6.3.1 CAUSAL AI SERVICES ENABLE BUSINESSES TO PREDICT IMPACT OF POTENTIAL CHANGES AND MAKE PROACTIVE ADJUSTMENTS
6.3.1.1 Consulting services
6.3.1.2 Deployment & integration services
6.3.1.3 Training, support & maintenance services
7 CAUSAL AI MARKET, BY APPLICATION
7.1 INTRODUCTION
7.1.1 APPLICATION: CAUSAL AI MARKET DRIVERS
7.2 FINANCIAL MANAGEMENT
7.2.1 CAUSAL AI IMPROVES REGULATORY COMPLIANCE AND FOSTERS AGILE FINANCIAL MANAGEMENT IN ORGANIZATIONS
7.2.2 FACTOR INVESTING
7.2.3 PORTFOLIO SIMULATION
7.2.4 INVESTMENT ANALYSIS
7.2.5 OTHER FINANCIAL MANAGEMENT APPLICATIONS
7.3 SALES & CUSTOMER MANAGEMENT
7.3.1 CAUSAL AI HELPS ORGANIZATIONS IDENTIFY KEY DRIVERS OF CUSTOMER ACTIONS BY ANALYZING CAUSAL RELATIONSHIPS BETWEEN FACTORS
7.3.2 CHURN PREDICTION & PREVENTION
7.3.3 CUSTOMER SEGMENTATION
7.3.4 CUSTOMER LIFETIME VALUE (CLV) PREDICTION
7.3.5 CUSTOMER EXPERIENCE OPTIMIZATION
7.3.6 PERSONALIZED RECOMMENDATIONS
7.3.7 OTHER SALES & CUSTOMER MANAGEMENT APPLICATIONS
7.4 OPERATIONS & SUPPLY CHAIN MANAGEMENT
7.4.1 CAUSAL AI ENABLES BUSINESSES OPTIMIZE PROCESSES, PREDICT DISRUPTIONS, AND MAKE DATA-DRIVEN DECISIONS TO ENHANCE EFFICIENCY
7.4.2 BOTTLENECK REMEDIATION
7.4.3 PREDICTIVE MAINTENANCE
7.4.4 REAL-TIME FAILURE RESPONSE
7.4.5 INVENTORY MANAGEMENT
7.4.6 OTHER OPERATIONS & SUPPLY CHAIN MANAGEMENT APPLICATIONS
7.5 MARKETING & PRICING MANAGEMENT
7.5.1 CAUSAL AI HELPS BUSINESSES MAKE DATA-DRIVEN DECISIONS TO BOOST PROFITABILITY AND GAIN COMPETITIVE EDGE IN RAPIDLY CHANGING MARKET
7.5.2 MARKETING CHANNEL OPTIMIZATION
7.5.3 PRICE ELASTICITY MODELING
7.5.4 PROMOTIONAL IMPACT ANALYSIS
7.5.5 COMPETITIVE PRICING ANALYSIS
7.5.6 OTHER MARKETING & PRICING MANAGEMENT APPLICATIONS
7.6 OTHER APPLICATIONS
8 CAUSAL AI MARKET, BY VERTICAL
8.1 INTRODUCTION
8.1.1 VERTICAL: CAUSAL AI MARKET DRIVERS
8.2 BFSI
8.2.1 CAUSAL AI RESHAPE BFSI PRACTICES, SETTING NEW STANDARDS FOR CUSTOMER-CENTRIC SERVICE DELIVERY IN FINANCIAL ECOSYSTEMS
8.2.2 BFSI: USE CASES
8.3 HEALTHCARE & LIFE SCIENCES
8.3.1 CAUSAL AI GUIDES POLICIES OR PUBLIC HEALTH INTERVENTIONS, LEADING TO EFFECTIVE HEALTH PROGRAMS
8.3.2 HEALTHCARE & LIFE SCIENCES: USE CASES
8.4 RETAIL & E-COMMERCE
8.4.1 BUSINESSES USING CAUSAL AI FOR ANALYZING FINANCIAL IMPACT, PROVIDING DATA-BACKED INSIGHTS ON DECISIONS
8.4.2 RETAIL & E-COMMERCE: USE CASES
8.5 MANUFACTURING
8.5.1 CAUSAL AI ENABLES DEEPER INSIGHTS INTO CAUSE-AND-EFFECT RELATIONSHIPS IN PRODUCTION PROCESSES, REVOLUTIONIZING MANUFACTURING
8.5.2 MANUFACTURING: USE CASES
8.6 TRANSPORTATION & LOGISTICS
8.6.1 CAUSAL AI ENHANCING INVENTORY MANAGEMENT, ROUTE PLANNING, AND OVERALL OPERATIONAL EFFICIENCY, REDUCING DOWNTIME AND COSTS
8.6.2 TRANSPORTATION & LOGISTICS: USE CASES
8.7 MEDIA & ENTERTAINMENT
8.7.1 CAUSAL AI PROVIDES DEEPER INSIGHTS INTO CONTENT CREATION AND AUDIENCE ENGAGEMENT
8.7.2 MEDIA & ENTERTAINMENT: USE CASES
8.8 TELECOMMUNICATIONS
8.8.1 TELECOM COMPANIES UTILIZING CAUSAL AI TO IDENTIFY SPECIFIC FACTORS CONTRIBUTING TO CUSTOMER DISSATISFACTION
8.8.2 TELECOMMUNICATIONS: USE CASES
8.9 ENERGY & UTILITIES
8.9.1 CAUSAL AI OPTIMIZES ENERGY PRODUCTION, ALLOWING MORE EFFICIENT SCHEDULING AND OPERATION OF PLANTS
8.9.2 ENERGY & UTILITIES: USE CASES
8.10 OTHER VERTICALS
9 CAUSAL AI MARKET, BY REGION
9.1 INTRODUCTION
9.2 NORTH AMERICA
9.2.1 NORTH AMERICA: CAUSAL AI MARKET DRIVERS
9.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
9.2.3 US
9.2.3.1 Need for advanced analytics that determine cause-and-effect relationships to drive market
9.2.4 CANADA
9.2.4.1 Use of causal AI to enhance everything from supply chain operations to personalized marketing strategies to drive market
9.3 EUROPE
9.3.1 EUROPE: CAUSAL AI MARKET DRIVERS
9.3.2 EUROPE: MACROECONOMIC OUTLOOK
9.3.3 UK
9.3.3.1 Advancements in machine learning, data analytics, and artificial intelligence technologies to drive market
9.3.4 GERMANY
9.3.4.1 Investment in AI research through initiatives to drive market
9.3.5 FRANCE
9.3.5.1 French AI startups attracting significant investment to scale their AI-driven platforms to drive market
9.3.6 REST OF EUROPE
9.4 ASIA PACIFIC
9.4.1 ASIA PACIFIC: CAUSAL AI MARKET DRIVERS
9.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
9.4.3 CHINA
9.4.3.1 China’s strong commitment to becoming world leader in AI to drive market
9.4.4 INDIA
9.4.4.1 Advancements in causal AI by Indian tech firms and academia, supported by collaborations and government initiatives, to drive market
9.4.5 JAPAN
9.4.5.1 Industries leveraging causal AI to optimize operations and create more adaptive systems to drive market
9.4.6 SOUTH KOREA
9.4.6.1 Partnerships with global AI firms to create more advanced causal inference algorithms to drive market
9.4.7 ASEAN
9.4.7.1 Integration of causal AI into diverse sectors to drive market
9.4.8 REST OF ASIA PACIFIC
9.5 MIDDLE EAST & AFRICA
9.5.1 MIDDLE EAST & AFRICA: CAUSAL AI MARKET DRIVERS
9.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
9.5.3 SAUDI ARABIA
9.5.3.1 Leveraging causal models to enhance predictive capabilities, optimize resource allocation, and improve operational efficiencies to drive market
9.5.4 UAE
9.5.4.1 Prioritization of AI across development strategies to drive market
9.5.5 SOUTH AFRICA
9.5.5.1 Startups using causal AI to improve financial inclusion to drive market
9.5.6 REST OF MIDDLE EAST
9.6 LATIN AMERICA
9.6.1 LATIN AMERICA: CAUSAL AI MARKET DRIVERS
9.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
9.6.3 BRAZIL
9.6.3.1 Growing demand for advanced analytics and decision-making tools across sectors to drive market
9.6.4 MEXICO
9.6.4.1 Causal AI to play pivotal role in reshaping technological landscape and business strategies
9.6.5 REST OF LATIN AMERICA
10 COMPETITIVE LANDSCAPE
10.1 OVERVIEW
10.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
10.3 REVENUE ANALYSIS
10.4 MARKET SHARE ANALYSIS
10.4.1 MARKET SHARE OF KEY PLAYERS OFFERING CAUSAL AI
10.4.1.1 Market Ranking Analysis
10.5 PRODUCT COMPARATIVE ANALYSIS
10.5.1 DECISIONOS PLATFORM (CAUSALENS)
10.5.2 CAUSAL REASONING PLATFORM (CAUSELY)
10.5.3 LIFESIGHT PLATFORM (LIFESIGHT)
10.5.4 CAUSALITY ENGINE, COGNIZANT CAUSALITY SERVICE (COGNIZANT)
10.5.5 DYNATRACE PLATFORM (DYNATRACE)
10.6 COMPANY VALUATION AND FINANCIAL METRICS
10.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
10.7.1 STARS
10.7.2 EMERGING LEADERS
10.7.3 PERVASIVE PLAYERS
10.7.4 PARTICIPANTS
10.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
10.7.5.1 Company footprint
10.7.5.2 Regional footprint
10.7.5.3 Offering footprint
10.7.5.4 Application footprint
10.7.5.5 Vertical footprint
10.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
10.8.1 PROGRESSIVE COMPANIES
10.8.2 RESPONSIVE COMPANIES
10.8.3 DYNAMIC COMPANIES
10.8.4 STARTING BLOCKS
10.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
10.8.5.1 Detailed list of key startups/SMEs
10.8.5.2 Competitive benchmarking of key startups/SMEs
10.9 COMPETITIVE SCENARIO AND TRENDS
10.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
10.9.2 DEALS
11 COMPANY PROFILES
11.1 INTRODUCTION
11.2 KEY PLAYERS
11.2.1 GOOGLE
11.2.1.1 Business overview
11.2.1.2 Products/Solutions/Services offered
11.2.1.3 Recent developments
11.2.1.3.1 Product launches & enhancements
11.2.1.3.2 Deals
11.2.1.4 MnM view
11.2.1.4.1 Key strengths
11.2.1.4.2 Strategic choices
11.2.1.4.3 Weaknesses and competitive threats
11.2.2 IBM
11.2.2.1 Business overview
11.2.2.2 Products/Solutions/Services offered
11.2.2.3 Recent developments
11.2.2.3.1 Product launches & enhancements
11.2.2.4 MnM view
11.2.2.4.1 Key strengths
11.2.2.4.2 Strategic choices
11.2.2.4.3 Weaknesses and competitive threats
11.2.3 MICROSOFT
11.2.3.1 Business overview
11.2.3.2 Products/Solutions/Services offered
11.2.3.3 Recent developments
11.2.3.3.1 Product launches & enhancements
11.2.3.4 MnM view
11.2.3.4.1 Key strengths
11.2.3.4.2 Strategic choices
11.2.3.4.3 Weaknesses and competitive threats
11.2.4 DYNATRACE
11.2.4.1 Business overview
11.2.4.2 Products/Solutions/Services offered
11.2.4.3 Recent developments
11.2.4.3.1 Product launches & enhancements
11.2.4.3.2 Deals
11.2.4.4 MnM view
11.2.4.4.1 Key strengths
11.2.4.4.2 Strategic choices
11.2.4.4.3 Weaknesses and competitive threats
11.2.5 COGNIZANT
11.2.5.1 Business overview
11.2.5.2 Products/Solutions/Services offered
11.2.5.3 Recent developments
11.2.5.3.1 Product launches & enhancements
11.2.5.4 MnM View
11.2.5.4.1 Key strengths
11.2.5.4.2 Strategic choices
11.2.5.4.3 Weaknesses and competitive threats
11.2.6 LOGILITY
11.2.6.1 Business overview
11.2.6.2 Products/Solutions/Services offered
11.2.6.3 Recent developments
11.2.6.3.1 Deals
11.2.7 DATAROBOT
11.2.7.1 Business overview
11.2.7.2 Products/Solutions/Services offered
11.2.8 CAUSALENS
11.2.8.1 Business overview
11.2.8.2 Products/Solutions/Services offered
11.2.8.3 Recent developments
11.2.8.3.1 Product launches & enhancements
11.2.8.3.2 Deals
11.2.9 DATA POEM
11.2.10 LIFESIGHT
11.2.11 AITIA
11.2.12 CAUSALY
11.3 STARTUPS/SMES
11.3.1 CAUSALITY LINK
11.3.1.1 Business overview
11.3.1.2 Products/Solutions/Services offered
11.3.1.3 Recent developments
11.3.1.3.1 Product launches & enhancements
11.3.1.3.2 Deals
11.3.2 TASKADE
11.3.2.1 Business overview
11.3.2.2 Products/Solutions/Services offered
11.3.2.3 Recent developments
11.3.2.3.1 Product launches & enhancements
11.3.3 CAUSELY
11.3.4 XPLAIN DATA
11.3.5 PARABOLE.AI
11.3.6 DATMA
11.3.7 INCRMNTAL
11.3.8 SCALNYX
11.3.9 GEMINOS
11.3.10 CAUSAI
11.3.11 CAUSA
11.3.12 ACTABLE AI
11.3.13 BIOTX.AI
11.3.14 HOWSO
11.3.15 VELDT
11.3.16 CML INSIGHT
12 ADJACENT AND RELATED MARKETS
12.1 INTRODUCTION
12.2 ARTIFICIAL INTELLIGENCE (AI) MARKET – GLOBAL FORECAST TO 2030
12.2.1 MARKET DEFINITION
12.2.2 MARKET OVERVIEW
12.2.2.1 Artificial intelligence market, by offering
12.2.2.2 Artificial intelligence market, by business function
12.2.2.3 Artificial intelligence market, by technology
12.2.2.4 Artificial intelligence market, by vertical
12.2.2.5 Artificial intelligence market, by region
12.3 AI GOVERNANCE MARKET– GLOBAL FORECAST TO 2030
12.3.1 MARKET DEFINITION
12.3.2 MARKET OVERVIEW
12.3.2.1 AI governance market, by product type
12.3.2.2 AI governance market, by functionality
12.3.2.3 AI governance market, by end user
12.3.2.4 AI governance market, by region
13 APPENDIX
13.1 DISCUSSION GUIDE
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
13.3 CUSTOMIZATION OPTIONS
13.4 RELATED REPORTS
13.5 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
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 Kьnstlicher Intelligenz (AI Regulation Law - Germany)
5.11.2.2.3 Loi pour une Rйpublique numйrique (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
5.11.2.4.5 African Union (AU) AI Framework
5.11.2.4.6 Egyptian Artificial Intelligence Strategy
5.11.2.4.7 Kuwait National Development Plan (New Kuwait Vision 2035)
5.11.2.5 Latin America
5.11.2.5.1 Brazilian General Data Protection Law (LGPD)
5.11.2.5.2 Federal Law on Protection of Personal Data Held by Private Parties - Mexico
5.11.2.5.3 Argentina Personal Data Protection Law (PDPL) & AI Ethics Framework
5.11.2.5.4 Chilean Data Protection Law & National AI Policy
5.11.2.5.5 Colombian Data Protection Law (Law 1581) & AI Ethics Guidelines
5.11.2.5.6 Peruvian Personal Data Protection Law & National AI Strategy
5.12 PATENT ANALYSIS
5.12.1 METHODOLOGY
5.12.2 PATENTS FILED, BY DOCUMENT TYPE
5.12.3 INNOVATION AND PATENT APPLICATIONS
5.13 KEY CONFERENCES AND EVENTS (2024–2025)
5.14 PORTER’S FIVE FORCES ANALYSIS
5.14.1 THREAT OF NEW ENTRANTS
5.14.2 THREAT OF SUBSTITUTES
5.14.3 BARGAINING POWER OF SUPPLIERS
5.14.4 BARGAINING POWER OF BUYERS
5.14.5 INTENSITY OF COMPETITIVE RIVALRY
5.15 KEY STAKEHOLDERS & BUYING CRITERIA
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS
5.15.2 BUYING CRITERIA
5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS
6 CAUSAL AI MARKET, BY OFFERING
6.1 INTRODUCTION
6.1.1 OFFERING: CAUSAL AI MARKET DRIVERS
6.2 SOFTWARE
6.2.1 RISING DEMAND FOR DATA-DRIVEN DECISIONS DRIVES GROWTH IN INDUSTRY-SPECIFIC CAUSAL AI SOFTWARE
6.2.2 CAUSAL AI PLATFORMS
6.2.3 CAUSAL AI SOLUTIONS
6.2.3.1 Causal discovery
6.2.3.2 Causal modeling
6.2.3.3 Decision intelligence
6.2.3.4 Root-cause analysis
6.2.3.5 Causal AI APIs
6.2.3.6 Software development kits
6.3 SERVICES
6.3.1 CAUSAL AI SERVICES ENABLE BUSINESSES TO PREDICT IMPACT OF POTENTIAL CHANGES AND MAKE PROACTIVE ADJUSTMENTS
6.3.1.1 Consulting services
6.3.1.2 Deployment & integration services
6.3.1.3 Training, support & maintenance services
7 CAUSAL AI MARKET, BY APPLICATION
7.1 INTRODUCTION
7.1.1 APPLICATION: CAUSAL AI MARKET DRIVERS
7.2 FINANCIAL MANAGEMENT
7.2.1 CAUSAL AI IMPROVES REGULATORY COMPLIANCE AND FOSTERS AGILE FINANCIAL MANAGEMENT IN ORGANIZATIONS
7.2.2 FACTOR INVESTING
7.2.3 PORTFOLIO SIMULATION
7.2.4 INVESTMENT ANALYSIS
7.2.5 OTHER FINANCIAL MANAGEMENT APPLICATIONS
7.3 SALES & CUSTOMER MANAGEMENT
7.3.1 CAUSAL AI HELPS ORGANIZATIONS IDENTIFY KEY DRIVERS OF CUSTOMER ACTIONS BY ANALYZING CAUSAL RELATIONSHIPS BETWEEN FACTORS
7.3.2 CHURN PREDICTION & PREVENTION
7.3.3 CUSTOMER SEGMENTATION
7.3.4 CUSTOMER LIFETIME VALUE (CLV) PREDICTION
7.3.5 CUSTOMER EXPERIENCE OPTIMIZATION
7.3.6 PERSONALIZED RECOMMENDATIONS
7.3.7 OTHER SALES & CUSTOMER MANAGEMENT APPLICATIONS
7.4 OPERATIONS & SUPPLY CHAIN MANAGEMENT
7.4.1 CAUSAL AI ENABLES BUSINESSES OPTIMIZE PROCESSES, PREDICT DISRUPTIONS, AND MAKE DATA-DRIVEN DECISIONS TO ENHANCE EFFICIENCY
7.4.2 BOTTLENECK REMEDIATION
7.4.3 PREDICTIVE MAINTENANCE
7.4.4 REAL-TIME FAILURE RESPONSE
7.4.5 INVENTORY MANAGEMENT
7.4.6 OTHER OPERATIONS & SUPPLY CHAIN MANAGEMENT APPLICATIONS
7.5 MARKETING & PRICING MANAGEMENT
7.5.1 CAUSAL AI HELPS BUSINESSES MAKE DATA-DRIVEN DECISIONS TO BOOST PROFITABILITY AND GAIN COMPETITIVE EDGE IN RAPIDLY CHANGING MARKET
7.5.2 MARKETING CHANNEL OPTIMIZATION
7.5.3 PRICE ELASTICITY MODELING
7.5.4 PROMOTIONAL IMPACT ANALYSIS
7.5.5 COMPETITIVE PRICING ANALYSIS
7.5.6 OTHER MARKETING & PRICING MANAGEMENT APPLICATIONS
7.6 OTHER APPLICATIONS
8 CAUSAL AI MARKET, BY VERTICAL
8.1 INTRODUCTION
8.1.1 VERTICAL: CAUSAL AI MARKET DRIVERS
8.2 BFSI
8.2.1 CAUSAL AI RESHAPE BFSI PRACTICES, SETTING NEW STANDARDS FOR CUSTOMER-CENTRIC SERVICE DELIVERY IN FINANCIAL ECOSYSTEMS
8.2.2 BFSI: USE CASES
8.3 HEALTHCARE & LIFE SCIENCES
8.3.1 CAUSAL AI GUIDES POLICIES OR PUBLIC HEALTH INTERVENTIONS, LEADING TO EFFECTIVE HEALTH PROGRAMS
8.3.2 HEALTHCARE & LIFE SCIENCES: USE CASES
8.4 RETAIL & E-COMMERCE
8.4.1 BUSINESSES USING CAUSAL AI FOR ANALYZING FINANCIAL IMPACT, PROVIDING DATA-BACKED INSIGHTS ON DECISIONS
8.4.2 RETAIL & E-COMMERCE: USE CASES
8.5 MANUFACTURING
8.5.1 CAUSAL AI ENABLES DEEPER INSIGHTS INTO CAUSE-AND-EFFECT RELATIONSHIPS IN PRODUCTION PROCESSES, REVOLUTIONIZING MANUFACTURING
8.5.2 MANUFACTURING: USE CASES
8.6 TRANSPORTATION & LOGISTICS
8.6.1 CAUSAL AI ENHANCING INVENTORY MANAGEMENT, ROUTE PLANNING, AND OVERALL OPERATIONAL EFFICIENCY, REDUCING DOWNTIME AND COSTS
8.6.2 TRANSPORTATION & LOGISTICS: USE CASES
8.7 MEDIA & ENTERTAINMENT
8.7.1 CAUSAL AI PROVIDES DEEPER INSIGHTS INTO CONTENT CREATION AND AUDIENCE ENGAGEMENT
8.7.2 MEDIA & ENTERTAINMENT: USE CASES
8.8 TELECOMMUNICATIONS
8.8.1 TELECOM COMPANIES UTILIZING CAUSAL AI TO IDENTIFY SPECIFIC FACTORS CONTRIBUTING TO CUSTOMER DISSATISFACTION
8.8.2 TELECOMMUNICATIONS: USE CASES
8.9 ENERGY & UTILITIES
8.9.1 CAUSAL AI OPTIMIZES ENERGY PRODUCTION, ALLOWING MORE EFFICIENT SCHEDULING AND OPERATION OF PLANTS
8.9.2 ENERGY & UTILITIES: USE CASES
8.10 OTHER VERTICALS
9 CAUSAL AI MARKET, BY REGION
9.1 INTRODUCTION
9.2 NORTH AMERICA
9.2.1 NORTH AMERICA: CAUSAL AI MARKET DRIVERS
9.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK
9.2.3 US
9.2.3.1 Need for advanced analytics that determine cause-and-effect relationships to drive market
9.2.4 CANADA
9.2.4.1 Use of causal AI to enhance everything from supply chain operations to personalized marketing strategies to drive market
9.3 EUROPE
9.3.1 EUROPE: CAUSAL AI MARKET DRIVERS
9.3.2 EUROPE: MACROECONOMIC OUTLOOK
9.3.3 UK
9.3.3.1 Advancements in machine learning, data analytics, and artificial intelligence technologies to drive market
9.3.4 GERMANY
9.3.4.1 Investment in AI research through initiatives to drive market
9.3.5 FRANCE
9.3.5.1 French AI startups attracting significant investment to scale their AI-driven platforms to drive market
9.3.6 REST OF EUROPE
9.4 ASIA PACIFIC
9.4.1 ASIA PACIFIC: CAUSAL AI MARKET DRIVERS
9.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK
9.4.3 CHINA
9.4.3.1 China’s strong commitment to becoming world leader in AI to drive market
9.4.4 INDIA
9.4.4.1 Advancements in causal AI by Indian tech firms and academia, supported by collaborations and government initiatives, to drive market
9.4.5 JAPAN
9.4.5.1 Industries leveraging causal AI to optimize operations and create more adaptive systems to drive market
9.4.6 SOUTH KOREA
9.4.6.1 Partnerships with global AI firms to create more advanced causal inference algorithms to drive market
9.4.7 ASEAN
9.4.7.1 Integration of causal AI into diverse sectors to drive market
9.4.8 REST OF ASIA PACIFIC
9.5 MIDDLE EAST & AFRICA
9.5.1 MIDDLE EAST & AFRICA: CAUSAL AI MARKET DRIVERS
9.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK
9.5.3 SAUDI ARABIA
9.5.3.1 Leveraging causal models to enhance predictive capabilities, optimize resource allocation, and improve operational efficiencies to drive market
9.5.4 UAE
9.5.4.1 Prioritization of AI across development strategies to drive market
9.5.5 SOUTH AFRICA
9.5.5.1 Startups using causal AI to improve financial inclusion to drive market
9.5.6 REST OF MIDDLE EAST
9.6 LATIN AMERICA
9.6.1 LATIN AMERICA: CAUSAL AI MARKET DRIVERS
9.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK
9.6.3 BRAZIL
9.6.3.1 Growing demand for advanced analytics and decision-making tools across sectors to drive market
9.6.4 MEXICO
9.6.4.1 Causal AI to play pivotal role in reshaping technological landscape and business strategies
9.6.5 REST OF LATIN AMERICA
10 COMPETITIVE LANDSCAPE
10.1 OVERVIEW
10.2 KEY PLAYER STRATEGIES/RIGHT TO WIN
10.3 REVENUE ANALYSIS
10.4 MARKET SHARE ANALYSIS
10.4.1 MARKET SHARE OF KEY PLAYERS OFFERING CAUSAL AI
10.4.1.1 Market Ranking Analysis
10.5 PRODUCT COMPARATIVE ANALYSIS
10.5.1 DECISIONOS PLATFORM (CAUSALENS)
10.5.2 CAUSAL REASONING PLATFORM (CAUSELY)
10.5.3 LIFESIGHT PLATFORM (LIFESIGHT)
10.5.4 CAUSALITY ENGINE, COGNIZANT CAUSALITY SERVICE (COGNIZANT)
10.5.5 DYNATRACE PLATFORM (DYNATRACE)
10.6 COMPANY VALUATION AND FINANCIAL METRICS
10.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023
10.7.1 STARS
10.7.2 EMERGING LEADERS
10.7.3 PERVASIVE PLAYERS
10.7.4 PARTICIPANTS
10.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023
10.7.5.1 Company footprint
10.7.5.2 Regional footprint
10.7.5.3 Offering footprint
10.7.5.4 Application footprint
10.7.5.5 Vertical footprint
10.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023
10.8.1 PROGRESSIVE COMPANIES
10.8.2 RESPONSIVE COMPANIES
10.8.3 DYNAMIC COMPANIES
10.8.4 STARTING BLOCKS
10.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023
10.8.5.1 Detailed list of key startups/SMEs
10.8.5.2 Competitive benchmarking of key startups/SMEs
10.9 COMPETITIVE SCENARIO AND TRENDS
10.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS
10.9.2 DEALS
11 COMPANY PROFILES
11.1 INTRODUCTION
11.2 KEY PLAYERS
11.2.1 GOOGLE
11.2.1.1 Business overview
11.2.1.2 Products/Solutions/Services offered
11.2.1.3 Recent developments
11.2.1.3.1 Product launches & enhancements
11.2.1.3.2 Deals
11.2.1.4 MnM view
11.2.1.4.1 Key strengths
11.2.1.4.2 Strategic choices
11.2.1.4.3 Weaknesses and competitive threats
11.2.2 IBM
11.2.2.1 Business overview
11.2.2.2 Products/Solutions/Services offered
11.2.2.3 Recent developments
11.2.2.3.1 Product launches & enhancements
11.2.2.4 MnM view
11.2.2.4.1 Key strengths
11.2.2.4.2 Strategic choices
11.2.2.4.3 Weaknesses and competitive threats
11.2.3 MICROSOFT
11.2.3.1 Business overview
11.2.3.2 Products/Solutions/Services offered
11.2.3.3 Recent developments
11.2.3.3.1 Product launches & enhancements
11.2.3.4 MnM view
11.2.3.4.1 Key strengths
11.2.3.4.2 Strategic choices
11.2.3.4.3 Weaknesses and competitive threats
11.2.4 DYNATRACE
11.2.4.1 Business overview
11.2.4.2 Products/Solutions/Services offered
11.2.4.3 Recent developments
11.2.4.3.1 Product launches & enhancements
11.2.4.3.2 Deals
11.2.4.4 MnM view
11.2.4.4.1 Key strengths
11.2.4.4.2 Strategic choices
11.2.4.4.3 Weaknesses and competitive threats
11.2.5 COGNIZANT
11.2.5.1 Business overview
11.2.5.2 Products/Solutions/Services offered
11.2.5.3 Recent developments
11.2.5.3.1 Product launches & enhancements
11.2.5.4 MnM View
11.2.5.4.1 Key strengths
11.2.5.4.2 Strategic choices
11.2.5.4.3 Weaknesses and competitive threats
11.2.6 LOGILITY
11.2.6.1 Business overview
11.2.6.2 Products/Solutions/Services offered
11.2.6.3 Recent developments
11.2.6.3.1 Deals
11.2.7 DATAROBOT
11.2.7.1 Business overview
11.2.7.2 Products/Solutions/Services offered
11.2.8 CAUSALENS
11.2.8.1 Business overview
11.2.8.2 Products/Solutions/Services offered
11.2.8.3 Recent developments
11.2.8.3.1 Product launches & enhancements
11.2.8.3.2 Deals
11.2.9 DATA POEM
11.2.10 LIFESIGHT
11.2.11 AITIA
11.2.12 CAUSALY
11.3 STARTUPS/SMES
11.3.1 CAUSALITY LINK
11.3.1.1 Business overview
11.3.1.2 Products/Solutions/Services offered
11.3.1.3 Recent developments
11.3.1.3.1 Product launches & enhancements
11.3.1.3.2 Deals
11.3.2 TASKADE
11.3.2.1 Business overview
11.3.2.2 Products/Solutions/Services offered
11.3.2.3 Recent developments
11.3.2.3.1 Product launches & enhancements
11.3.3 CAUSELY
11.3.4 XPLAIN DATA
11.3.5 PARABOLE.AI
11.3.6 DATMA
11.3.7 INCRMNTAL
11.3.8 SCALNYX
11.3.9 GEMINOS
11.3.10 CAUSAI
11.3.11 CAUSA
11.3.12 ACTABLE AI
11.3.13 BIOTX.AI
11.3.14 HOWSO
11.3.15 VELDT
11.3.16 CML INSIGHT
12 ADJACENT AND RELATED MARKETS
12.1 INTRODUCTION
12.2 ARTIFICIAL INTELLIGENCE (AI) MARKET – GLOBAL FORECAST TO 2030
12.2.1 MARKET DEFINITION
12.2.2 MARKET OVERVIEW
12.2.2.1 Artificial intelligence market, by offering
12.2.2.2 Artificial intelligence market, by business function
12.2.2.3 Artificial intelligence market, by technology
12.2.2.4 Artificial intelligence market, by vertical
12.2.2.5 Artificial intelligence market, by region
12.3 AI GOVERNANCE MARKET– GLOBAL FORECAST TO 2030
12.3.1 MARKET DEFINITION
12.3.2 MARKET OVERVIEW
12.3.2.1 AI governance market, by product type
12.3.2.2 AI governance market, by functionality
12.3.2.3 AI governance market, by end user
12.3.2.4 AI governance market, by region
13 APPENDIX
13.1 DISCUSSION GUIDE
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL
13.3 CUSTOMIZATION OPTIONS
13.4 RELATED REPORTS
13.5 AUTHOR DETAILS
