市場調查報告書
商品編碼
1470670
因果人工智慧市場:按產品、配置和產業分類 - 2024-2030 年全球預測Causal AI Market by Offering (Platform, Services), Deployment (Cloud, On-Premise), Vertical - Global Forecast 2024-2030 |
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預計2023年因果AI市場規模為5.0153億美元,預計2024年將達6.5038億美元,2030年將達34.8866億美元,複合年成長率為31.92%。
因果人工智慧涉及先進的人工智慧技術,使機器能夠理解複雜系統內的因果關係。這項最尖端科技旨在透過基於因果推理提供更準確的預測和見解來改進決策流程。各行業對更好的預測分析的需求不斷成長,以便在競爭格局中做出資料主導的決策,並且景觀模型的使用正在增加,以做出更明智的決策。大型資料集的可用性和運算能力的增強使研究人員和開發人員能夠創建更複雜的機器學習演算法來處理複雜的因果關係。隨著技術的進步和變得更加容易獲得,因果人工智慧解決方案的採用率正在迅速增加。開發精確模型的複雜性阻礙了市場的成長,這些模型可以僅從大量資料之間的相關性中識別出真正的因果關係。新興市場在開發因果人工智慧模型方面取得的技術開拓,可以從大量資料中識別因果關係,預計將為市場成長創造機會。
主要市場統計 | |
---|---|
基準年[2023] | 50153萬美元 |
預測年份 [2024] | 65038萬美元 |
預測年份 [2030] | 3,488.66 百萬美元 |
複合年成長率(%) | 31.92% |
更好地控制交付模型開發並擴大平台使用
該平台提供了一套全面的工具和功能,使用戶能夠開發、部署和管理複雜的因果人工智慧模型。這些平台提供多種功能,包括資料預處理、模型開發、視覺化工具以及與現有系統的整合。服務由專門從事因果人工智慧的顧問公司和供應商提供,根據客戶的特定需求提供客製化解決方案。這些服務範圍從因果建模策略的建議到端到端因果人工智慧解決方案的全面實施。
按行業擴大因果人工智慧在醫療保健和生命科學行業診斷和藥物開發中的使用
銀行、金融服務和保險業擴大採用因果人工智慧來偵測詐欺、管理風險和改善客戶服務。因果人工智慧透過個人化治療計劃、藥物發現和早期疾病診斷正在徹底改變醫療保健和生命科學領域。在製造領域,Causal AI 正在幫助最佳化供應鏈流程,透過預測性維護提高生產效率,並實現智慧工廠。零售和電子商務平台正在利用因果人工智慧,透過提供個人化建議、最佳化定價策略、管理庫存等來改善客戶體驗。在運輸和物流行業,因果人工智慧正在幫助最佳化路線規劃、預測車輛維護需求並改善倉庫運作。因果人工智慧在各行業的採用正在透過提高效率、降低成本和增強決策能力來改變業務。
部署:雲端基礎的因果人工智慧具有成本效益,並且可以快速部署。
雲端基礎的因果人工智慧解決方案因其擴充性、易於訪問性和較低的初始成本而越來越受歡迎。這些解決方案非常適合尋求靈活資源管理和快速部署的企業。本地因果 AI 解決方案適合優先考慮資料安全、控制和客製化的企業。此類解決方案通常受到金融和醫療保健等受監管行業的公司的青睞,這些行業嚴格的資料隱私法規要求敏感資訊在內部儲存和處理。與本地選項相比,雲端部署提供擴充性、可存取性、成本效益和快速實施。然而,它可能不適合有嚴格資料隱私要求的公司或尋求對其人工智慧基礎設施進行廣泛客製化的公司。另一方面,本地部署可以在遵守法令遵循的同時提供對資料安全和系統客製化的更多控制,但需要更高的前期投資和更長的實施時間。
區域洞察
在美洲,隨著人工智慧創新以矽谷為中心,對因果人工智慧解決方案的需求持續強勁。該地區的特點是企業和研究機構引進技術的強烈願望。此外,北美各國政府正在積極支持人工智慧研究項目,提供大量資金和獎勵,進一步增加了對因果人工智慧技術的需求。由於其先進的數位基礎設施和對研發計劃的持續投資,歐洲正在成為全球因果人工智慧領域的另一個重要地區。歐盟委員會對人工智慧計劃的巨額投資顯示了政府對將歐洲打造成人工智慧強國的支持。
非洲和中東地區對在經濟上利用巨量資料分析和機器學習能力的興趣激增,但必須克服技能有限和資源不足的挑戰。亞太地區的因果人工智慧市場具有指數成長的潛力。在中國政府成為人工智慧超級大國的雄心勃勃的計劃的推動下,中國已將該地區定位為世界人工智慧研究的領跑者之一。日本和新加坡等已開發國家也在人工智慧實施方面投入巨資,專注於機器人、自動駕駛汽車和醫療保健等領域。另一方面,印度、東南亞等新興市場人口眾多、技術進步快,為引進人工智慧提供了獨特的機會。
FPNV定位矩陣
FPNV 定位矩陣對於評估因果 AI 市場至關重要。我們檢視與業務策略和產品滿意度相關的關鍵指標,以對供應商進行全面評估。這種深入的分析使用戶能夠根據自己的要求做出明智的決策。根據評估,供應商被分為四個成功程度不同的像限:前沿(F)、探路者(P)、利基(N)和重要(V)。
市場佔有率分析
市場佔有率分析是一個綜合工具,可以對因果人工智慧市場中供應商的現狀進行深入而詳細的研究。全面比較和分析供應商在整體收益、基本客群和其他關鍵指標方面的貢獻,以便更好地了解公司的績效及其在爭奪市場佔有率時面臨的挑戰。此外,該分析還提供了對該行業競爭特徵的寶貴見解,包括在研究基準年觀察到的累積、分散主導地位和合併特徵等因素。這種詳細程度的提高使供應商能夠做出更明智的決策並制定有效的策略,從而在市場上獲得競爭優勢。
1. 市場滲透率:提供有關主要企業所服務的市場的全面資訊。
2. 市場開拓:我們深入研究利潤豐厚的新興市場,並分析其在成熟細分市場的滲透率。
3. 市場多元化:提供有關新產品發布、開拓地區、最新發展和投資的詳細資訊。
4.競爭評估與資訊:對主要企業的市場佔有率、策略、產品、認證、監管狀況、專利狀況、製造能力等進行全面評估。
5. 產品開發與創新:提供對未來技術、研發活動和突破性產品開發的見解。
1. 因果AI市場的市場規模和預測是多少?
2.在因果人工智慧市場的預測期內,有哪些產品、細分市場、應用程式和領域需要考慮投資?
3. 因果人工智慧市場的技術趨勢和法規結構是什麼?
4.因果AI市場主要廠商的市場佔有率是多少?
5. 進入因果AI市場的合適型態和策略手段是什麼?
[193 Pages Report] The Causal AI Market size was estimated at USD 501.53 million in 2023 and expected to reach USD 650.38 million in 2024, at a CAGR 31.92% to reach USD 3,488.66 million by 2030.
Causal AI includes advanced artificial intelligence technologies that enable machines to understand causal relationships within complex systems. This cutting-edge technology is aimed at improving decision-making processes by providing more accurate predictions and insights based on cause-and-effect reasoning. Increasing demand for better predictive analytics across industries to make data-driven decisions in a competitive landscape is expanding the usage of causal AI models to make more informed decisions. The growing availability of large-scale data sets combined with advancements in computational power has enabled researchers and developers to create more sophisticated machine learning algorithms that handle complex causal relationships. As technologies continue to improve and become more accessible, the adoption rate of causal AI solutions is increasing rapidly. The complexity involved in developing accurate models capable of identifying genuine causality from mere correlation within vast amounts of data hampers market growth. Growing technological advancements in the development of causal AI models, which help to identify cause-and-effect relationships within large amounts of data, are expected to create opportunities for market growth.
KEY MARKET STATISTICS | |
---|---|
Base Year [2023] | USD 501.53 million |
Estimated Year [2024] | USD 650.38 million |
Forecast Year [2030] | USD 3,488.66 million |
CAGR (%) | 31.92% |
Offering: Expanding usage of platforms as it offers a higher degree of control over model development
Platforms provide a comprehensive set of tools and functionalities that enable users to develop, deploy, and manage complex Causal AI models. These platforms offer various features such as data preprocessing, model development, visualization tools, and integration with existing systems. Services are provided by specialized causal AI consulting firms and vendors that offer customized solutions tailored to clients' specific needs. These services range from advising on causal modeling strategies to full-scale implementation of end-to-end causal AI solutions
Vertical: Growing utilization of causal AI by the healthcare and life science industry for diagnosis and drug development
The banking, financial services & insurance sector is increasingly adopting Causal AI for fraud detection, risk management, and client service improvement. Causal AI has been revolutionizing the healthcare and lifesciences sectors through personalized treatment plans, drug discovery, and early disease diagnosis. In the manufacturing sector, Causal AI has been instrumental in optimizing supply chain processes, improving production efficiency through predictive maintenance and enabling smart factory transformation. Retailers and e-commerce platforms leverage Causal AI to enhance customer experiences by offering personalized recommendations, optimizing pricing strategies, and managing inventory. Transportation and logistics industries benefit from Causal AI in optimizing route planning, predicting vehicle maintenance requirements, and improving warehouse operations. The adoption of Causal AI across various verticals is transforming businesses through improved efficiency, cost savings, and enhanced decision-making capabilities.
Deployment: Increasing adoption of cloud-based causal AI due to its cost-effectiveness, and quicker implementation
Cloud-based causal AI solutions are gaining traction due to their scalability, ease of access, and reduced upfront costs. These solutions are ideal for businesses seeking flexibility in managing resources and quick implementation. On-premises causal AI solutions cater to organizations that prioritize data security, control, and customization. These solutions are often preferred by companies in regulated industries, such as finance and healthcare, where strict data privacy regulations require businesses to store and process sensitive information on their premises. Cloud deployment offers scalability, accessibility, cost-effectiveness, and quicker implementation compared to on-premises options. However, it may not be suitable for businesses with strict data privacy requirements or those seeking extensive customization of their AI infrastructure. On the other hand, on-premises deployment provides greater control over data security and system customization while adhering to compliance regulations but requires higher upfront investment and longer implementation times.
Regional Insights
The Americas continues to witness robust demand for causal AI solutions as an AI innovation with Silicon Valley at its core. The region is characterized by a strong appetite for technology adoption among businesses and research institutions. Moreover, governments in North America have been actively supporting AI research programs with substantial funding and incentives that further bolster the demand for causal AI technologies. Europe is fast becoming another crucial region in the global causal AI landscape due to its advanced digital infrastructure and ongoing investments in R&D initiatives. The European Commission's significant investments in artificial intelligence projects demonstrate governmental support towards making Europe an AI powerhouse.
In Africa and Middle East regions, there is burgeoning interest in leveraging big data analytics and machine learning capabilities within their economies; however, they require overcoming limited skill sets or inadequate resource challenges. The causal AI market in the APAC region has an exponential growth potential. China shows this region as one of the global frontrunners in AI research, backed by the Chinese government's ambitious plan to become an AI superpower. Industrialized nations, including Japan and Singapore, are also investing heavily in AI adoption, focusing on areas such as robotics, autonomous vehicles, and healthcare. Meanwhile, emerging markets such as India and Southeast Asia present unique opportunities for causal AI implementation due to their large population size and rapidly evolving technology landscape.
FPNV Positioning Matrix
The FPNV Positioning Matrix is pivotal in evaluating the Causal AI Market. It offers a comprehensive assessment of vendors, examining key metrics related to Business Strategy and Product Satisfaction. This in-depth analysis empowers users to make well-informed decisions aligned with their requirements. Based on the evaluation, the vendors are then categorized into four distinct quadrants representing varying levels of success: Forefront (F), Pathfinder (P), Niche (N), or Vital (V).
Market Share Analysis
The Market Share Analysis is a comprehensive tool that provides an insightful and in-depth examination of the current state of vendors in the Causal AI Market. By meticulously comparing and analyzing vendor contributions in terms of overall revenue, customer base, and other key metrics, we can offer companies a greater understanding of their performance and the challenges they face when competing for market share. Additionally, this analysis provides valuable insights into the competitive nature of the sector, including factors such as accumulation, fragmentation dominance, and amalgamation traits observed over the base year period studied. With this expanded level of detail, vendors can make more informed decisions and devise effective strategies to gain a competitive edge in the market.
Key Company Profiles
The report delves into recent significant developments in the Causal AI Market, highlighting leading vendors and their innovative profiles. These include Accenture PLC, Amazon Web Services, Inc., BigML, Inc., BMC Software, Inc., Causality Link LLC, cognino.ai, Cognizant Technology Solutions Corporation, Databricks, Inc, Dynatrace LLC, EXPERT.AI, Fair Isaac Corporation, Geminos Software, Google LLC by Alphabet Inc., Hewlett Packard Enterprise Development LP, Impulse Innovations Limited (causaLens), INCRMNTAL Ltd., Infosys Limited, Intel Corporation, International Business Machines Corporation, Kyndryl Inc., Logility, Microsoft Corporation, Oracle Corporation, Parabole.ai, Salesforce, Inc., SAP SE, SCALNYX, and Xplain Data GmbH.
Market Segmentation & Coverage
1. Market Penetration: It presents comprehensive information on the market provided by key players.
2. Market Development: It delves deep into lucrative emerging markets and analyzes the penetration across mature market segments.
3. Market Diversification: It provides detailed information on new product launches, untapped geographic regions, recent developments, and investments.
4. Competitive Assessment & Intelligence: It conducts an exhaustive assessment of market shares, strategies, products, certifications, regulatory approvals, patent landscape, and manufacturing capabilities of the leading players.
5. Product Development & Innovation: It offers intelligent insights on future technologies, R&D activities, and breakthrough product developments.
1. What is the market size and forecast of the Causal AI Market?
2. Which products, segments, applications, and areas should one consider investing in over the forecast period in the Causal AI Market?
3. What are the technology trends and regulatory frameworks in the Causal AI Market?
4. What is the market share of the leading vendors in the Causal AI Market?
5. Which modes and strategic moves are suitable for entering the Causal AI Market?