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市場調查報告書
商品編碼
1766105
2032 年因果人工智慧市場預測:按組件、部署模式、技術、組織規模、應用、最終用戶和地區進行的全球分析Causal AI Market Forecasts to 2032 - Global Analysis By Component (Software and Services), Deployment Mode, Technology, Organization Size, Application, End User and By Geography |
根據 Stratistics MRC 的數據,全球因果人工智慧市場規模預計將在 2025 年達到 8,081 萬美元,到 2032 年達到 10.2756 億美元,複合年成長率為 43.8%。因果人工智慧是一種先進的人工智慧形式,它專注於理解因果關係,而不僅僅是識別相關性。透過建模變數之間的相互影響,它使系統能夠模擬結果、做出更明智的決策並提供更深入的洞察。與通常充當黑盒子的傳統人工智慧不同,因果人工智慧具有更高的透明度並支持反事實推理,這使得它在醫療保健、金融和政策決策等高風險領域尤其有價值。
據麥肯錫全球研究院稱,人工智慧方法,特別是因果推理技術,每年可以在 19 個行業的 9 種商業活動中釋放 3.5 兆至 5.8 兆美元的價值。
對反事實推理的需求日益成長
對可解釋人工智慧日益成長的需求,推動了各行各業對因果推理人工智慧的採用。各組織正從傳統的黑箱模型轉向能夠模擬「假設」情境的系統。這種轉變透過識別因果關係而非單純的相關性,從而實現更明智的決策。在醫療保健和金融等領域,反事實虛擬支持風險評估和治療最佳化。監管機構也越來越重視透明度,這進一步提升了他們對因果推理的興趣。因此,因果推理人工智慧正成為下一代分析的基礎工具。
技術複雜度高
建構準確的因果模型需要深厚的領域知識和先進的統計專業知識。許多組織缺乏內部人才來實施和維護此類系統。此外,因果框架難以與現有的AI流程整合。缺乏標準化方法進一步增加了應用的複雜性。這些因素減緩了因果AI解決方案的廣泛應用。
醫療保健和藥物研發領域人工智慧應用的成長
因果人工智慧為醫療保健和藥物研究帶來了變革性機會。它使研究人員能夠識別治療和患者預後的因果關係,從而改善臨床決策。在藥物研發中,因果模型有助於分離影響療效和副作用的變數。這加速了標靶治療和個人化醫療的發展。健康數據和運算能力的日益普及也推動了這一趨勢。因此,醫療保健正逐漸成為因果人工智慧創新的關鍵領域。
認知和理解有限
許多習慣於傳統預測性AI的組織難以理解相關性和因果性之間的根本區別,從而誤解了因果AI的獨特價值提案——即不僅要解釋發生了什麼,還要解釋為什麼會發生。因此,他們可能不願意投資複雜的因果模型,也無法充分理解因果AI所帶來的決策增強、可解釋性和減少偏差的能力。儘管該技術潛力巨大,但這種知識差距加上對專業知識的需求,阻礙了其應用,並減緩了市場成長。
COVID-19的影響
新冠疫情顯著加速了因果人工智慧市場的成長。隨著企業面臨前所未有的衝擊,對強大且可解釋的決策工具的需求變得至關重要。因果人工智慧憑藉其識別因果因素的能力,比傳統人工智慧提供了更深入的洞察,並協助危機管理、供應鏈調整和醫療回應。各行各業對因果人工智慧的需求激增,紛紛尋求更具彈性、數據主導的策略。這促使對因果人工智慧技術的投資和研究不斷增加,使其成為後疫情時代數位轉型的關鍵參與企業。
預計在預測期內軟體部分將成為最大的部分。
由於對可解釋且透明的人工智慧解決方案的需求不斷成長,人工智慧在複雜決策中的應用日益廣泛,以及各行各業對精準預測分析的需求,預計軟體領域將在預測期內佔據最大的市場佔有率。企業正在尋找不僅能預測結果,還能理解根本原因的軟體。機器學習的發展、數據的可用性以及監管機構對負責任的人工智慧的關注,將進一步推動因果人工智慧軟體的開發和應用。
預計教育領域在預測期內將實現最高的複合年成長率。
由於對能夠開發和實施可解釋人工智慧模型的熟練專業人員的需求日益成長,預計教育領域將在預測期內實現最高成長率。隨著各行各業採用因果人工智慧,教育機構和培訓計畫也不斷擴展以滿足需求。人們對人工智慧倫理、法規合規性以及傳統機器學習局限性的認知不斷提高,也激發了人們對因果推理的興趣,促使教育機構將因果人工智慧納入資料科學和人工智慧課程。
預計亞太地區將在預測期內佔據最大的市場佔有率,這得益於快速的數位轉型、人工智慧研究投入的增加以及對可解釋和可信的人工智慧解決方案日益成長的需求。各國政府和企業將人工智慧作為經濟成長和政策制定的優先事項,提升了人們對因果推理的興趣。在中國、印度和日本等國家,日益豐富的數據、強大的技術基礎設施以及政府的支持舉措,進一步加速了因果推理人工智慧技術的普及。
在預測期內,北美地區預計將呈現最高的複合年成長率,這得益於強勁的技術創新、高級分析技術的廣泛應用,以及醫療保健和金融等受監管行業對可解釋人工智慧的需求日益成長。領先的科技公司和學術機構正在大力投資因果關係研究。此外,對數據主導決策和遵守人工智慧倫理標準的需求日益成長,正推動該地區各個領域快速採用和開發因果關係人工智慧解決方案。
According to Stratistics MRC, the Global Causal AI Market is accounted for $80.81 million in 2025 and is expected to reach $1027.56 million by 2032 growing at a CAGR of 43.8% during the forecast period. Causal AI is an advanced form of artificial intelligence that focuses on understanding cause-and-effect relationships rather than just identifying correlations. By modeling how variables influence one another, it enables systems to simulate outcomes, make better decisions, and provide deeper insights. Unlike traditional AI, which often functions as a black box, causal AI offers greater transparency, supports counterfactual reasoning, and is especially valuable in high-stakes domains like healthcare, finance, and policy-making.
According to McKinsey Global Institute, AI approaches, particularly causal inference methods, have the potential to generate between USD 3.5 Trillion and USD 5.8 Trillion in value yearly across nine business activities in 19 industries.
Rise in counterfactual reasoning needs
The increasing demand for explainable AI is driving the adoption of causal AI across industries. Organizations are shifting from traditional black-box models to systems that can simulate "what-if" scenarios. This shift enables better decision-making by identifying cause-and-effect relationships rather than mere correlations. In sectors like healthcare and finance, counterfactual reasoning supports risk assessment and treatment optimization. Regulatory bodies are also emphasizing transparency, further boosting interest in causal inference. As a result, causal AI is becoming a foundational tool for next-generation analytics.
High technical complexity
Building accurate causal models requires deep domain knowledge and advanced statistical expertise. Many organizations lack the in-house talent to implement and maintain such systems. Additionally, integrating causal frameworks with existing AI pipelines can be challenging. The absence of standardized methodologies further complicates adoption. These factors collectively slow down the widespread deployment of causal AI solutions.
Growth of AI applications in healthcare and drug discovery
Causal AI presents transformative opportunities in healthcare and pharmaceutical research. It enables researchers to identify causal links between treatments and patient outcomes, improving clinical decision-making. In drug discovery, causal models help isolate variables that influence efficacy and side effects. This accelerates the development of targeted therapies and personalized medicine. The growing availability of health data and computational power supports this trend. As a result, healthcare is emerging as a key vertical for causal AI innovation.
Limited awareness and understanding
Many organizations, accustomed to traditional predictive AI, struggle to grasp the fundamental distinction between correlation and causation. This often leads to a misperception of Causal AI's unique value proposition - its ability to explain why things happen, rather than just what will happen. Consequently, there's a reluctance to invest in complex causal models, as businesses may not fully appreciate the enhanced decision-making, explainability, and bias reduction that Causal AI offers. This knowledge gap, coupled with the need for specialized expertise, hinders widespread adoption and slows market growth, despite the technology's immense potential.
Covid-19 Impact
The COVID-19 pandemic significantly accelerated the growth of the Causal AI market. As organizations faced unprecedented disruptions, the need for robust, explainable decision-making tools became critical. Causal AI, with its ability to identify cause-and-effect relationships, offered deeper insights than traditional AI, aiding in crisis management, supply chain adjustments, and healthcare responses. The demand surged across industries seeking more resilient, data-driven strategies. Consequently, investment and research in Causal AI technologies expanded, positioning it as a key player in post-pandemic digital transformation.
The software segment is expected to be the largest during the forecast period
The software segment is expected to account for the largest market share during the forecast period, due to the rising demand for explainable and transparent AI solutions, increasing adoption of AI for complex decision-making, and the need for accurate predictive analytics across industries. Businesses seek software that not only forecasts outcomes but also understands the underlying causes. Advancements in machine learning, data availability, and regulatory emphasis on responsible AI further boost the development and adoption of Causal AI software.
The education segment is expected to have the highest CAGR during the forecast period
Over the forecast period, the education segment is predicted to witness the highest growth rate, due to the growing need for skilled professionals who can develop and implement explainable AI models. As industries adopt Causal AI, academic institutions and training programs are expanding to meet demand. Increased awareness of AI ethics, regulatory compliance, and the limitations of traditional machine learning also fuel interest in causal reasoning, prompting educational institutions to integrate Causal AI into data science and AI curricula.
During the forecast period, the Asia Pacific region is expected to hold the largest market share driven by rapid digital transformation, growing investments in AI research, and increasing demand for explainable and trustworthy AI solutions. Governments and enterprises are prioritizing AI for economic growth and policy planning, boosting interest in causal inference. Expanding data availability, strong tech infrastructure, and supportive government initiatives in countries like China, India, and Japan further accelerate the adoption of Causal AI technologies.
Over the forecast period, the North America region is anticipated to exhibit the highest CAGR, owing to strong technological innovation, high adoption of advanced analytics, and a growing need for explainable AI in regulated industries like healthcare and finance. Leading tech companies and academic institutions are investing heavily in causal research. Additionally, increasing demand for data-driven decision-making and compliance with ethical AI standards fuels the region's rapid adoption and development of Causal AI solutions across various sectors.
Key players in the market
Some of the key players profiled in the Causal AI Market include Google LLC, Microsoft Corporation, IBM Corporation, causaLens, DataRobot, Inc., Causality Link LLC, Aitia, Causaly, Dynatrace Inc., Cognizant, Logility Inc., Parabole.ai, Geminos Software, Scalnyx, Data Poem, Lifesight, Incrmntal, and Senser.
In January 2025, IBM and The All England Lawn Tennis Club announced new and enhanced AI-powered digital experiences coming to The Championships, Wimbledon 2025. Making its debut is 'Match Chat', an interactive AI assistant that can answer fans' questions during live singles matches. The 'Likelihood to Win' tool is also being enhanced, offering fans a projected win percentage that can change throughout each game.
In September 2024, causaLens launched its groundbreaking AI agent platform for decision-making at the Causal AI Conference. causaLens Launches Revolutionary AI Agents Platform for Decision-making at the Causal AI Conference in London.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.