Summary

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.
 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.

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Table of Contents

1 INTRODUCTION 32
1.1 STUDY OBJECTIVES 32
1.2 MARKET DEFINITION 32
1.2.1 INCLUSIONS AND EXCLUSIONS 33
1.3 MARKET SCOPE 33
1.3.1 MARKET SEGMENTATION 34
1.3.2 YEARS CONSIDERED 36
1.4 CURRENCY CONSIDERED 37
1.5 STAKEHOLDERS 37
1.6 SUMMARY OF CHANGES 37
2 RESEARCH METHODOLOGY 39
2.1 RESEARCH DATA 39
2.1.1 SECONDARY DATA 40
2.1.2 PRIMARY DATA 40
2.1.2.1 Breakup of primary profiles 41
2.1.2.2 Key industry insights 41
2.2 MARKET BREAKUP AND DATA TRIANGULATION 42
2.3 MARKET SIZE ESTIMATION 43
2.3.1 TOP-DOWN APPROACH 43
2.3.2 BOTTOM-UP APPROACH 44
2.4 MARKET FORECAST 47
2.5 RESEARCH ASSUMPTIONS 48
2.6 RESEARCH LIMITATIONS 50
3 EXECUTIVE SUMMARY 51
4 PREMIUM INSIGHTS 58
4.1 ATTRACTIVE OPPORTUNITIES FOR PLAYERS IN CAUSAL AI MARKET 58
4.2 CAUSAL AI MARKET: TOP THREE APPLICATIONS 59
4.3 NORTH AMERICA: CAUSAL AI MARKET, BY APPLICATION AND VERTICAL 59
4.4 CAUSAL AI MARKET, BY REGION 60
5 MARKET OVERVIEW AND INDUSTRY TRENDS 61
5.1 INTRODUCTION 61
5.2 MARKET DYNAMICS 61
5.2.1 DRIVERS 62
5.2.1.1 Increasing demand for explainable AI in regulated industries 62
5.2.1.2 Growing demand for robust counterfactual analysis 63
5.2.1.3 Surge in demand for predictive maintenance and root cause analysis 63
5.2.1.4 Shift from predictive to causal AI-based prescriptive analytics 63
5.2.2 RESTRAINTS 64
5.2.2.1 Lack of standardized tools and frameworks for causal inference 64
5.2.2.2 High computational costs for causal modeling 64
5.2.3 OPPORTUNITIES 64
5.2.3.1 Causal AI in precision healthcare and drug discovery 64
5.2.3.2 Scalable causal inference APIs for real-time applications 65
5.2.3.3 Integrating causal AI with IoT for real-time decision making 65
5.2.4 CHALLENGES 65
5.2.4.1 Complexity of causal model development and interpretability 65
5.2.4.2 Data quality and availability for causal inference 66
5.3 EVOLUTION OF CAUSAL AI 66
5.4 SUPPLY CHAIN ANALYSIS 68
5.5 ECOSYSTEM ANALYSIS 70
5.5.1 CAUSAL AI PLATFORM PROVIDERS 72
5.5.2 CAUSAL AI TOOL PROVIDERS 72
5.5.3 CAUSAL AI TOOLKITS AND APIS PROVIDERS 72
5.5.4 CAUSAL AI SERVICE PROVIDERS 72
5.6 INVESTMENT LANDSCAPE AND FUNDING SCENARIO 72
5.7 IMPACT OF GENERATIVE AI IN CAUSAL AI MARKET 74
5.7.1 ENHANCED DATA AVAILABILITY FOR CAUSAL ANALYSIS 75
5.7.2 STRESS TESTING OF CAUSAL MODELS 75
5.7.3 SUPPORT FOR COMPLEX MULTIVARIABLE ANALYSIS 75
5.7.4 ACCELERATED MODEL DEVELOPMENT 75
5.7.5 BIAS REDUCTION FOR FAIRER OUTCOMES 75
5.7.6 DYNAMIC SIMULATIONS FOR CAUSAL TESTING 76
5.8 PRICING ANALYSIS 76
5.8.1 PRICING DATA, BY OFFERING 76
5.8.2 PRICING DATA, BY APPLICATION 77
5.9 CASE STUDY ANALYSIS 78
5.9.1 CASE STUDY 1: DYNATRACE BOOSTS BMO'S DIGITAL EFFICIENCY WITH CAUSAL AI-POWERED INSIGHTS AND AUTOMATION 78
5.9.2 CASE STUDY 2: FINGERSOFT ACHIEVES DATA-DRIVEN MARKETING OPTIMIZATION WITH INCRMNTAL’S CAUSAL AI INSIGHTS 79
5.9.3 CASE STUDY 3: ACCELERATING FAULT DETECTION WITH CAUSAL AI FOR ENHANCED PRODUCT RELIABILITY IN MANUFACTURING 79
5.9.4 CASE STUDY 4: LEVERAGING CAUSAL AI FOR ENHANCED ROOT CAUSE ANALYSIS IN TRUMPF’S EQUIPMENT MAINTENANCE 80
5.9.5 CASE STUDY 5: CAUSA TECH ENHANCED OPERATIONAL EFFICIENCY FOR LEADING MANUFACTURING FIRM, STRENGTHENING SUPPLY CHAIN RESILIENCE 80
5.9.6 CASE STUDY 6: LIFESIGHT ADDRESSING KEY CHALLENGES IN MARKETING, ENHANCING EFFICIENCY AND SALES FOR DTC BEAUTY BRAND 81
5.10 TECHNOLOGY ANALYSIS 81
5.10.1 KEY TECHNOLOGIES 81
5.10.1.1 Causal inference algorithms 81
5.10.1.2 Explainable AI (XAI) 82
5.10.1.3 Structural equation modeling (SEM) 82
5.10.1.4 Bayesian networks 82
5.10.1.5 Causal graphs 83
5.10.2 COMPLEMENTARY TECHNOLOGIES 83
5.10.2.1 Machine learning 83
5.10.2.2 Reinforcement learning 83
5.10.2.3 Data engineering 84
5.10.2.4 Knowledge graphs 84
5.10.3 ADJACENT TECHNOLOGIES 84
5.10.3.1 Predictive analytics 84
5.10.3.2 Decision intelligence 85
5.10.3.3 Synthetic data generation 85
5.10.3.4 Natural language processing (NLP) 85
5.11 REGULATORY LANDSCAPE 86
5.11.1 REGULATORY BODIES, GOVERNMENT AGENCIES, AND OTHER ORGANIZATIONS 86
5.11.2 REGULATIONS: CAUSAL AI 90
5.11.2.1 North America 90
5.11.2.1.1 Blueprint for AI Bill of Rights (US) 90
5.11.2.1.2 Directive on Automated Decision-Making (Canada) 91
5.11.2.2 Europe 91
5.11.2.2.1 UK AI Regulation White Paper 91
5.11.2.2.2 Gesetz zur Regulierung Künstlicher Intelligenz (AI Regulation Law - Germany) 92
5.11.2.2.3 Loi pour une République numérique (Digital Republic Act - France) 92
5.11.2.2.4 Codice in materia di protezione dei dati personali (Data Protection Code - Italy) 92
5.11.2.2.5 Ley de Servicios Digitales (Digital Services Act - Spain) 93
5.11.2.2.6 Dutch Data Protection Authority (Autoriteit Persoonsgegevens) Guidelines 93
5.11.2.2.7 Swedish National Board of Trade AI Guidelines 93
5.11.2.2.8 Danish Data Protection Agency (Datatilsynet) AI Recommendations 94
5.11.2.2.9 Artificial Intelligence 4.0 (AI 4.0) Program - Finland 94
5.11.2.3 Asia Pacific 95
5.11.2.3.1 Personal Data Protection Bill (PDPB) & National Strategy on AI (NSAI) - India 95
5.11.2.3.2 Basic Act on Advancement of Utilizing Public and Private Sector Data & AI Guidelines - Japan 95
5.11.2.3.3 New Generation Artificial Intelligence Development Plan & AI Ethics Guidelines - China 95
5.11.2.3.4 Framework Act on Intelligent Informatization – South Korea 96
5.11.2.3.5 AI Ethics Framework (Australia) & AI Strategy (New Zealand) 96
5.11.2.3.6 Model AI Governance Framework - Singapore 97
5.11.2.3.7 National AI Framework - Malaysia 97
5.11.2.3.8 National AI Roadmap - Philippines 97
5.11.2.4 Middle East & Africa 98
5.11.2.4.1 Saudi Data & Artificial Intelligence Authority (SDAIA) Regulations 98
5.11.2.4.2 UAE National AI Strategy 2031 98
5.11.2.4.3 Qatar National AI Strategy 98
5.11.2.4.4 National Artificial Intelligence Strategy (2021–2025) - Turkey 99
5.11.2.4.5 African Union (AU) AI Framework 99
5.11.2.4.6 Egyptian Artificial Intelligence Strategy 100
5.11.2.4.7 Kuwait National Development Plan (New Kuwait Vision 2035) 100
5.11.2.5 Latin America 101
5.11.2.5.1 Brazilian General Data Protection Law (LGPD) 101
5.11.2.5.2 Federal Law on Protection of Personal Data Held by Private Parties - Mexico 101
5.11.2.5.3 Argentina Personal Data Protection Law (PDPL) & AI Ethics Framework 101
5.11.2.5.4 Chilean Data Protection Law & National AI Policy 102
5.11.2.5.5 Colombian Data Protection Law (Law 1581) & AI Ethics Guidelines 102
5.11.2.5.6 Peruvian Personal Data Protection Law & National AI Strategy 103
5.12 PATENT ANALYSIS 103
5.12.1 METHODOLOGY 103
5.12.2 PATENTS FILED, BY DOCUMENT TYPE 103
5.12.3 INNOVATION AND PATENT APPLICATIONS 104
5.13 KEY CONFERENCES AND EVENTS (2024–2025) 108
5.14 PORTER’S FIVE FORCES ANALYSIS 108
5.14.1 THREAT OF NEW ENTRANTS 110
5.14.2 THREAT OF SUBSTITUTES 110
5.14.3 BARGAINING POWER OF SUPPLIERS 110
5.14.4 BARGAINING POWER OF BUYERS 110
5.14.5 INTENSITY OF COMPETITIVE RIVALRY 110
5.15 KEY STAKEHOLDERS & BUYING CRITERIA 111
5.15.1 KEY STAKEHOLDERS IN BUYING PROCESS 111
5.15.2 BUYING CRITERIA 112
5.16 TRENDS/DISRUPTIONS IMPACTING CUSTOMER BUSINESS 113
6 CAUSAL AI MARKET, BY OFFERING 114
6.1 INTRODUCTION 115
6.1.1 OFFERING: CAUSAL AI MARKET DRIVERS 115
6.2 SOFTWARE 116
6.2.1 RISING DEMAND FOR DATA-DRIVEN DECISIONS DRIVES GROWTH IN INDUSTRY-SPECIFIC CAUSAL AI SOFTWARE 116
6.2.2 CAUSAL AI PLATFORMS 117
6.2.3 CAUSAL AI SOLUTIONS 118
6.2.3.1 Causal discovery 120
6.2.3.2 Causal modeling 121
6.2.3.3 Decision intelligence 122
6.2.3.4 Root-cause analysis 123
6.2.3.5 Causal AI APIs 124
6.2.3.6 Software development kits 125
6.3 SERVICES 126
6.3.1 CAUSAL AI SERVICES ENABLE BUSINESSES TO PREDICT IMPACT OF POTENTIAL CHANGES AND MAKE PROACTIVE ADJUSTMENTS 126
6.3.1.1 Consulting services 127
6.3.1.2 Deployment & integration services 128
6.3.1.3 Training, support & maintenance services 129
7 CAUSAL AI MARKET, BY APPLICATION 131
7.1 INTRODUCTION 132
7.1.1 APPLICATION: CAUSAL AI MARKET DRIVERS 132
7.2 FINANCIAL MANAGEMENT 134
7.2.1 CAUSAL AI IMPROVES REGULATORY COMPLIANCE AND FOSTERS AGILE FINANCIAL MANAGEMENT IN ORGANIZATIONS 134
7.2.2 FACTOR INVESTING 135
7.2.3 PORTFOLIO SIMULATION 136
7.2.4 INVESTMENT ANALYSIS 137
7.2.5 OTHER FINANCIAL MANAGEMENT APPLICATIONS 138
7.3 SALES & CUSTOMER MANAGEMENT 139
7.3.1 CAUSAL AI HELPS ORGANIZATIONS IDENTIFY KEY DRIVERS OF CUSTOMER ACTIONS BY ANALYZING CAUSAL RELATIONSHIPS BETWEEN FACTORS 139
7.3.2 CHURN PREDICTION & PREVENTION 140
7.3.3 CUSTOMER SEGMENTATION 141
7.3.4 CUSTOMER LIFETIME VALUE (CLV) PREDICTION 142
7.3.5 CUSTOMER EXPERIENCE OPTIMIZATION 143
7.3.6 PERSONALIZED RECOMMENDATIONS 144
7.3.7 OTHER SALES & CUSTOMER MANAGEMENT APPLICATIONS 145

7.4 OPERATIONS & SUPPLY CHAIN MANAGEMENT 147
7.4.1 CAUSAL AI ENABLES BUSINESSES OPTIMIZE PROCESSES, PREDICT DISRUPTIONS, AND MAKE DATA-DRIVEN DECISIONS TO ENHANCE EFFICIENCY 147
7.4.2 BOTTLENECK REMEDIATION 148
7.4.3 PREDICTIVE MAINTENANCE 149
7.4.4 REAL-TIME FAILURE RESPONSE 150
7.4.5 INVENTORY MANAGEMENT 151
7.4.6 OTHER OPERATIONS & SUPPLY CHAIN MANAGEMENT APPLICATIONS 152
7.5 MARKETING & PRICING MANAGEMENT 153
7.5.1 CAUSAL AI HELPS BUSINESSES MAKE DATA-DRIVEN DECISIONS TO BOOST PROFITABILITY AND GAIN COMPETITIVE EDGE IN RAPIDLY CHANGING MARKET 153
7.5.2 MARKETING CHANNEL OPTIMIZATION 154
7.5.3 PRICE ELASTICITY MODELING 155
7.5.4 PROMOTIONAL IMPACT ANALYSIS 156
7.5.5 COMPETITIVE PRICING ANALYSIS 157
7.5.6 OTHER MARKETING & PRICING MANAGEMENT APPLICATIONS 158
7.6 OTHER APPLICATIONS 159
8 CAUSAL AI MARKET, BY VERTICAL 161
8.1 INTRODUCTION 162
8.1.1 VERTICAL: CAUSAL AI MARKET DRIVERS 162
8.2 BFSI 164
8.2.1 CAUSAL AI RESHAPE BFSI PRACTICES, SETTING NEW STANDARDS FOR CUSTOMER-CENTRIC SERVICE DELIVERY IN FINANCIAL ECOSYSTEMS 164
8.2.2 BFSI: USE CASES 165
8.3 HEALTHCARE & LIFE SCIENCES 166
8.3.1 CAUSAL AI GUIDES POLICIES OR PUBLIC HEALTH INTERVENTIONS, LEADING TO EFFECTIVE HEALTH PROGRAMS 166
8.3.2 HEALTHCARE & LIFE SCIENCES: USE CASES 166
8.4 RETAIL & E-COMMERCE 167
8.4.1 BUSINESSES USING CAUSAL AI FOR ANALYZING FINANCIAL IMPACT, PROVIDING DATA-BACKED INSIGHTS ON DECISIONS 167
8.4.2 RETAIL & E-COMMERCE: USE CASES 168
8.5 MANUFACTURING 169
8.5.1 CAUSAL AI ENABLES DEEPER INSIGHTS INTO CAUSE-AND-EFFECT RELATIONSHIPS IN PRODUCTION PROCESSES, REVOLUTIONIZING MANUFACTURING 169
8.5.2 MANUFACTURING: USE CASES 170
8.6 TRANSPORTATION & LOGISTICS 171
8.6.1 CAUSAL AI ENHANCING INVENTORY MANAGEMENT, ROUTE PLANNING, AND OVERALL OPERATIONAL EFFICIENCY, REDUCING DOWNTIME AND COSTS 171
8.6.2 TRANSPORTATION & LOGISTICS: USE CASES 171

8.7 MEDIA & ENTERTAINMENT 172
8.7.1 CAUSAL AI PROVIDES DEEPER INSIGHTS INTO CONTENT CREATION AND AUDIENCE ENGAGEMENT 172
8.7.2 MEDIA & ENTERTAINMENT: USE CASES 173
8.8 TELECOMMUNICATIONS 174
8.8.1 TELECOM COMPANIES UTILIZING CAUSAL AI TO IDENTIFY SPECIFIC FACTORS CONTRIBUTING TO CUSTOMER DISSATISFACTION 174
8.8.2 TELECOMMUNICATIONS: USE CASES 174
8.9 ENERGY & UTILITIES 175
8.9.1 CAUSAL AI OPTIMIZES ENERGY PRODUCTION, ALLOWING MORE EFFICIENT SCHEDULING AND OPERATION OF PLANTS 175
8.9.2 ENERGY & UTILITIES: USE CASES 176
8.10 OTHER VERTICALS 177
9 CAUSAL AI MARKET, BY REGION 179
9.1 INTRODUCTION 180
9.2 NORTH AMERICA 182
9.2.1 NORTH AMERICA: CAUSAL AI MARKET DRIVERS 182
9.2.2 NORTH AMERICA: MACROECONOMIC OUTLOOK 183
9.2.3 US 192
9.2.3.1 Need for advanced analytics that determine cause-and-effect relationships to drive market 192
9.2.4 CANADA 193
9.2.4.1 Use of causal AI to enhance everything from supply chain operations to personalized marketing strategies to drive market 193
9.3 EUROPE 194
9.3.1 EUROPE: CAUSAL AI MARKET DRIVERS 194
9.3.2 EUROPE: MACROECONOMIC OUTLOOK 195
9.3.3 UK 203
9.3.3.1 Advancements in machine learning, data analytics, and artificial intelligence technologies to drive market 203
9.3.4 GERMANY 204
9.3.4.1 Investment in AI research through initiatives to drive market 204
9.3.5 FRANCE 205
9.3.5.1 French AI startups attracting significant investment to scale their AI-driven platforms to drive market 205
9.3.6 REST OF EUROPE 206
9.4 ASIA PACIFIC 207
9.4.1 ASIA PACIFIC: CAUSAL AI MARKET DRIVERS 208
9.4.2 ASIA PACIFIC: MACROECONOMIC OUTLOOK 208
9.4.3 CHINA 218
9.4.3.1 China’s strong commitment to becoming world leader in AI to drive market 218

9.4.4 INDIA 219
9.4.4.1 Advancements in causal AI by Indian tech firms and academia, supported by collaborations and government initiatives, to drive market 219
9.4.5 JAPAN 220
9.4.5.1 Industries leveraging causal AI to optimize operations and create more adaptive systems to drive market 220
9.4.6 SOUTH KOREA 221
9.4.6.1 Partnerships with global AI firms to create more advanced causal inference algorithms to drive market 221
9.4.7 ASEAN 222
9.4.7.1 Integration of causal AI into diverse sectors to drive market 222
9.4.8 REST OF ASIA PACIFIC 223
9.5 MIDDLE EAST & AFRICA 224
9.5.1 MIDDLE EAST & AFRICA: CAUSAL AI MARKET DRIVERS 224
9.5.2 MIDDLE EAST & AFRICA: MACROECONOMIC OUTLOOK 225
9.5.3 SAUDI ARABIA 233
9.5.3.1 Leveraging causal models to enhance predictive capabilities, optimize resource allocation, and improve operational efficiencies to drive market 233
9.5.4 UAE 234
9.5.4.1 Prioritization of AI across development strategies to drive market 234
9.5.5 SOUTH AFRICA 235
9.5.5.1 Startups using causal AI to improve financial inclusion to drive market 235
9.5.6 REST OF MIDDLE EAST 236
9.6 LATIN AMERICA 237
9.6.1 LATIN AMERICA: CAUSAL AI MARKET DRIVERS 238
9.6.2 LATIN AMERICA: MACROECONOMIC OUTLOOK 238
9.6.3 BRAZIL 247
9.6.3.1 Growing demand for advanced analytics and decision-making tools across sectors to drive market 247
9.6.4 MEXICO 248
9.6.4.1 Causal AI to play pivotal role in reshaping technological landscape and business strategies 248
9.6.5 REST OF LATIN AMERICA 249
10 COMPETITIVE LANDSCAPE 250
10.1 OVERVIEW 250
10.2 KEY PLAYER STRATEGIES/RIGHT TO WIN 250
10.3 REVENUE ANALYSIS 252
10.4 MARKET SHARE ANALYSIS 253
10.4.1 MARKET SHARE OF KEY PLAYERS OFFERING CAUSAL AI 253
10.4.1.1 Market Ranking Analysis 254

10.5 PRODUCT COMPARATIVE ANALYSIS 256
10.5.1 DECISIONOS PLATFORM (CAUSALENS) 256
10.5.2 CAUSAL REASONING PLATFORM (CAUSELY) 256
10.5.3 LIFESIGHT PLATFORM (LIFESIGHT) 257
10.5.4 CAUSALITY ENGINE, COGNIZANT CAUSALITY SERVICE (COGNIZANT) 257
10.5.5 DYNATRACE PLATFORM (DYNATRACE) 257
10.6 COMPANY VALUATION AND FINANCIAL METRICS 257
10.7 COMPANY EVALUATION MATRIX: KEY PLAYERS, 2023 258
10.7.1 STARS 258
10.7.2 EMERGING LEADERS 258
10.7.3 PERVASIVE PLAYERS 259
10.7.4 PARTICIPANTS 259
10.7.5 COMPANY FOOTPRINT: KEY PLAYERS, 2023 260
10.7.5.1 Company footprint 260
10.7.5.2 Regional footprint 260
10.7.5.3 Offering footprint 261
10.7.5.4 Application footprint 261
10.7.5.5 Vertical footprint 262
10.8 COMPANY EVALUATION MATRIX: STARTUPS/SMES, 2023 263
10.8.1 PROGRESSIVE COMPANIES 263
10.8.2 RESPONSIVE COMPANIES 263
10.8.3 DYNAMIC COMPANIES 263
10.8.4 STARTING BLOCKS 263
10.8.5 COMPETITIVE BENCHMARKING: STARTUPS/SMES, 2023 265
10.8.5.1 Detailed list of key startups/SMEs 265
10.8.5.2 Competitive benchmarking of key startups/SMEs 266
10.9 COMPETITIVE SCENARIO AND TRENDS 267
10.9.1 PRODUCT LAUNCHES AND ENHANCEMENTS 267
10.9.2 DEALS 268
11 COMPANY PROFILES 270
11.1 INTRODUCTION 270
11.2 KEY PLAYERS 270
11.2.1 GOOGLE 270
11.2.1.1 Business overview 270
11.2.1.2 Products/Solutions/Services offered 271
11.2.1.3 Recent developments 272
11.2.1.3.1 Product launches & enhancements 272
11.2.1.3.2 Deals 272

11.2.1.4 MnM view 272
11.2.1.4.1 Key strengths 272
11.2.1.4.2 Strategic choices 273
11.2.1.4.3 Weaknesses and competitive threats 273
11.2.2 IBM 274
11.2.2.1 Business overview 274
11.2.2.2 Products/Solutions/Services offered 275
11.2.2.3 Recent developments 276
11.2.2.3.1 Product launches & enhancements 276
11.2.2.4 MnM view 276
11.2.2.4.1 Key strengths 276
11.2.2.4.2 Strategic choices 276
11.2.2.4.3 Weaknesses and competitive threats 276
11.2.3 MICROSOFT 277
11.2.3.1 Business overview 277
11.2.3.2 Products/Solutions/Services offered 278
11.2.3.3 Recent developments 279
11.2.3.3.1 Product launches & enhancements 279
11.2.3.4 MnM view 279
11.2.3.4.1 Key strengths 279
11.2.3.4.2 Strategic choices 279
11.2.3.4.3 Weaknesses and competitive threats 279
11.2.4 DYNATRACE 280
11.2.4.1 Business overview 280
11.2.4.2 Products/Solutions/Services offered 281
11.2.4.3 Recent developments 282
11.2.4.3.1 Product launches & enhancements 282
11.2.4.3.2 Deals 282
11.2.4.4 MnM view 283
11.2.4.4.1 Key strengths 283
11.2.4.4.2 Strategic choices 283
11.2.4.4.3 Weaknesses and competitive threats 283
11.2.5 COGNIZANT 284
11.2.5.1 Business overview 284
11.2.5.2 Products/Solutions/Services offered 285
11.2.5.3 Recent developments 286
11.2.5.3.1 Product launches & enhancements 286
11.2.5.4 MnM View 286
11.2.5.4.1 Key strengths 286
11.2.5.4.2 Strategic choices 286
11.2.5.4.3 Weaknesses and competitive threats 286

11.2.6 LOGILITY 287
11.2.6.1 Business overview 287
11.2.6.2 Products/Solutions/Services offered 288
11.2.6.3 Recent developments 289
11.2.6.3.1 Deals 289
11.2.7 DATAROBOT 290
11.2.7.1 Business overview 290
11.2.7.2 Products/Solutions/Services offered 290
11.2.8 CAUSALENS 291
11.2.8.1 Business overview 291
11.2.8.2 Products/Solutions/Services offered 291
11.2.8.3 Recent developments 292
11.2.8.3.1 Product launches & enhancements 292
11.2.8.3.2 Deals 292
11.2.9 DATA POEM 293
11.2.10 LIFESIGHT 293
11.2.11 AITIA 294
11.2.12 CAUSALY 294
11.3 STARTUPS/SMES 295
11.3.1 CAUSALITY LINK 295
11.3.1.1 Business overview 295
11.3.1.2 Products/Solutions/Services offered 295
11.3.1.3 Recent developments 296
11.3.1.3.1 Product launches & enhancements 296
11.3.1.3.2 Deals 296
11.3.2 TASKADE 297
11.3.2.1 Business overview 297
11.3.2.2 Products/Solutions/Services offered 297
11.3.2.3 Recent developments 298
11.3.2.3.1 Product launches & enhancements 298
11.3.3 CAUSELY 299
11.3.4 XPLAIN DATA 299
11.3.5 PARABOLE.AI 300
11.3.6 DATMA 301
11.3.7 INCRMNTAL 302
11.3.8 SCALNYX 303
11.3.9 GEMINOS 304
11.3.10 CAUSAI 305
11.3.11 CAUSA 305
11.3.12 ACTABLE AI 306
11.3.13 BIOTX.AI 306
11.3.14 HOWSO 307
11.3.15 VELDT 307
11.3.16 CML INSIGHT 308
12 ADJACENT AND RELATED MARKETS 309
12.1 INTRODUCTION 309
12.2 ARTIFICIAL INTELLIGENCE (AI) MARKET – GLOBAL FORECAST TO 2030 309
12.2.1 MARKET DEFINITION 309
12.2.2 MARKET OVERVIEW 309
12.2.2.1 Artificial intelligence market, by offering 310
12.2.2.2 Artificial intelligence market, by business function 311
12.2.2.3 Artificial intelligence market, by technology 312
12.2.2.4 Artificial intelligence market, by vertical 313
12.2.2.5 Artificial intelligence market, by region 315
12.3 AI GOVERNANCE MARKET– GLOBAL FORECAST TO 2030 316
12.3.1 MARKET DEFINITION 316
12.3.2 MARKET OVERVIEW 316
12.3.2.1 AI governance market, by product type 317
12.3.2.2 AI governance market, by functionality 318
12.3.2.3 AI governance market, by end user 319
12.3.2.4 AI governance market, by region 320
13 APPENDIX 322
13.1 DISCUSSION GUIDE 322
13.2 KNOWLEDGESTORE: MARKETSANDMARKETS’ SUBSCRIPTION PORTAL 328
13.3 CUSTOMIZATION OPTIONS 330
13.4 RELATED REPORTS 330
13.5 AUTHOR DETAILS 331