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市場調查報告書

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1423509

全球可解釋人工智慧市場 - 2024-2031

Global Explainable AI Market - 2024-2031

出版日期: 2024年02月09日 | 出版商:

DataM Intelligence | 英文 245 Pages | 商品交期: 最快1-2個工作天內

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簡介目錄

概述

全球可解釋人工智慧市場將於 2023 年達到 52 億美元，預計到 2031 年將達到 221 億美元，2024-2031 年預測期間CAGR為 20.2%。

如今，大約 28% 的公民總體上願意信任人工智慧系統。對人工智慧日益缺乏的信任促使歐盟（EU）和美國加強監管。這些呼籲似乎是有效的，因為監管機構目前正在立法，要求人工智慧模型遵守特定的可解釋性水平，包括解釋和闡明人工智慧結果的能力。

專案智慧主要參與者不斷推出的產品有助於推動預測期內的市場成長。例如，2022 年 12 月 30 日，Digitite, Inc. 推出了世界上第一個用於企業專案智慧的可解釋人工智慧產品。 RISHI 代表 Digite 先進的企業專案智慧產品，整合了 eXplainable AI 和機器學習系統。 RISHI 專為 CXO、交付主管、PMO 和決策者量身定做，將源自 Digite 豐富 IT 領域經驗的知識系統與最先進的 ML 功能相結合。

由於金融領域擴大採用可解釋的人工智慧，北美成為市場的主導地區。政府不斷推出的可解釋人工智慧措施有助於推動預測期內區域市場的成長。人們越來越需要提高對深度學習（也稱為可解釋人工智慧）不透明本質的理解的方法。

美國國防部高級研究計劃局和電腦協會的公平、責任和透明度會議是可解釋的人工智慧活動的兩個著名例子。在醫學影像領域，電腦輔助干預主辦了醫學影像計算國際會議，每年一度的會議致力於醫學影像計算中機器智慧的可解釋性。

動力學

擴大採用可解釋的人工智慧 (XAI) 進行風險管理

風險管理是許多企業（包括銀行、醫療保健和網路安全）的重要組成部分。隨著可解釋的人工智慧方法擴大用於風險評估和決策過程，組織對人工智慧模型如何得出結果有了更多的了解。監管機構、客戶和內部決策者都是利益相關者，他們的信任因這種真實性的增加而得到加強。

許多企業的監管機構都需要可解釋的人工智慧系統，特別是在銀行和醫療保健等複雜領域。可解釋的人工智慧為人工智慧驅動的行動提供了可理解的理由，這可以幫助組織遵守監管標準。這種對法律的遵守進一步鼓勵了可解釋的人工智慧風險管理系統的使用。組織透過使用可解釋的人工智慧技術來識別並減少用於風險評估的人工智慧模型中的偏差和錯誤。可解釋的人工智慧透過為模型預測提供解釋來幫助識別潛在的偏差和不準確性。這使組織能夠採取糾正措施並提高風險管理程序的準確性和公平性。

4.O產業快速成長

第四次工業革命（4.0）產業的快速擴張對全球可解釋人工智慧市場的成長做出了重大貢獻。隨著各行業經歷數位轉型並將人工智慧等先進技術融入其營運中，對透明且可解釋的人工智慧解決方案的需求變得至關重要。可解釋的人工智慧解決了與信任、責任和監管合規相關的問題，使其在 4.0 行業中不可或缺。工業4.0背後的驅動力在於數位科技在製造業的運用，包括物聯網（IoT）、人工智慧（AI）和巨量資料分析。

隨著工業 4.0 的發展勢頭，製造商正在經歷前所未有的效率水平。據 MPI 集團稱，32% 的製造商預計工業 4.0 對流程、工廠和供應鏈的影響將導致盈利能力成長超過 10%。隨著 2023 年的臨近，越來越多的製造商正在利用數位化參與來增強營運。具體來說，56% 的製造商傾向於與供應商進行數位化合作，以促進品質指標的即時共享。

人工智慧模型的複雜性

複雜的人工智慧模型通常需要大量資源，例如熟練的資料科學家、運算能力以及漫長的開發和培訓週期。開發費用的增加和時間的延長可能會阻礙資源有限的小型企業或組織採用人工智慧模型。

在現實場景中部署高度複雜的人工智慧模型可能會遇到可擴展性挑戰，特別是當它們依賴大量運算資源或難以有效處理大量資料時。可擴展性限制可能會阻礙人工智慧模型在不同行業和應用中的廣泛採用。

隨著人工智慧模型複雜性的增加，其可解釋性和可解釋性通常會降低。由於監管要求或道德問題，缺乏透明度可能會阻礙可解釋性至關重要的行業的採用，例如醫療保健、金融和法律領域。雖然複雜的人工智慧模型通常在特定任務或領域表現出色，但它們在實現性能與可解釋性、公平性和穩健性等其他基本因素之間的平衡時可能會遇到困難。這些因素之間的權衡可能會限制複雜人工智慧模型的實際適用性。

Nowadays about 28% of the citizens are willing to trust AI systems in general. The growing lack of trust in AI is prompting demands for heightened regulation in both the European Union (EU) and United States. The calls seem to be effective, as regulatory authorities are now progressing towards legislation mandating that AI models adhere to specific levels of explainability, encompassing the capacity to interpret and elucidate AI outcomes.

The growing product launches by the major key players for project intelligence help boost market growth over the forecast period. For instance, on December 30, 2022, Digite, Inc. launched the world's first Explainable AI product for Enterprise Project Intelligence. RISHI represents Digite's advanced Enterprise Project Intelligence product, integrating eXplainable AI and Machine Learning systems. Tailored for CXOs, Delivery Heads, PMOs and decision-makers, RISHI combines a knowledge system derived from Digite's extensive IT domain experience with state-of-the-art ML capabilities.

North America is a dominating region in the market due to the growing adoption of explainable AI in the finance sector. Growing Government's initiatives for explainable AI help to boost regional market growth over the forecast period. Approaches that improve understanding of the opaque nature of deep learning also referred to as explainable artificial intelligence are becoming more in demand.

U.S. Defence Advanced Research Projects Agency and the Association for Computing Machinery's Fairness, Accountability and Transparency conferences are two notable examples of explainable AI activities. Within the field of medical imaging Computer-Assisted Intervention hosts and the International Conference on Medical Image Computing an annual session devoted to the Interpretability of Machine Intelligence in Medical Image Computing.

Dynamics

Growing Adoption Of Explainable AI (XAI) For Risk Management

An important part of many businesses, including banking, healthcare and cybersecurity, is risk management. As explainable AI approaches are increasingly being used in risk assessment and decision-making processes organizations are gaining more understanding of how AI models arrive at their findings. Regulators, customers and internal decision-makers are among the stakeholders whose trust is strengthened by this increased authenticity.

Explicable AI systems are required by regulatory organizations in many businesses, particularly in complex fields like banking and healthcare. Explainable AI offers comprehensible justifications for AI-driven actions, which can help organizations comply with regulatory standards. The use of explainable AI risk management systems is further encouraged by this adherence to laws. Organizations identify and reduce biases and errors in AI models used for risk assessment by using explainable AI techniques. Explainable AI assists in recognizing underlying biases and inaccuracies by offering explanations for model predictions. The enables organizations to take corrective measures and enhance the precision and equity of risk management procedures.

Rapid growth in the 4.O industry

The rapid expansion of the Fourth Industrial Revolution (4.0) industry contributes significantly to the growth of the global Explainable AI market. As industries undergo digital transformation and integrate advanced technologies like AI into their operations, the need for transparent and interpretable AI solutions becomes crucial. Explainable AI addresses concerns related to trust, accountability and regulatory compliance, making it indispensable in the 4.0 industry. The driving force behind Industry 4.0 lies in the utilization of digital technologies, including the Internet of Things (IoT), Artificial Intelligence (AI) and big data analytics, within the manufacturing sector.

As Industry 4.0 gains momentum, manufacturers are experiencing unprecedented levels of efficiency. According to the MPI Group, 32% of manufacturers anticipate that Industry 4.0's influence on processes, plants and supply chains will lead to a profitability increase of over 10%. As we approach 2023, an increasing number of manufacturers are leveraging digital engagement to enhance their operations. Specifically, 56% of manufacturers are inclined to engage digitally with suppliers to facilitate real-time sharing of quality metrics.

Complexity of AI Models

Sophisticated AI models typically demand substantial resources such as proficient data scientists, computational capabilities and lengthy development and training periods. The elevated development expenses and extended timeframes may discourage smaller businesses or organizations with constrained resources from embracing AI models.

The deployment of highly intricate AI models in real-world scenarios can encounter scalability challenges, particularly if they rely on substantial computational resources or struggle to handle extensive data volumes efficiently. The scalability constraints may impede the widespread adoption of AI models across diverse industries and applications.

As AI models increase in complexity, their interpretability and explainability typically decrease. The lack of transparency can impede adoption in sectors where interpretability is vital, such as healthcare, finance and legal fields, due to regulatory mandates or ethical concerns. While complex AI models often excel in specific tasks or domains, they may encounter difficulties in achieving a balance between performance and other essential factors like interpretability, fairness and robustness. Trade-offs among these factors can restrict the practical applicability of complex AI models.

Segment Analysis

The global explainable AI market is segmented based on offering, deployment, organization size, technology, application, end-user and region.

Growing Demand for Explainable AI Services

Based on the offering, the explainable AI market is segmented into solutions and services. The explainable AI services segment accounted largest market share in the market due to its growing adoption in the finance sector. Rising regulations and compliance needs in sectors like finance, healthcare and retail are driving the requirement for AI systems capable of offering transparent and interpretable explanations for their decisions. Both businesses and consumers are seeking AI systems they can trust and comprehend and explainable AI services play a crucial role in providing transparency into the decision-making processes of AI models, thereby fostering trust and confidence in their utilization.

Some of the major key players in the market follow merger and acquisition strategies to expand their explainable AI operations in the finance industry. For instance, on December 07, 2022, Deutsche Bank partnered with NVIDIA to embed AI into Financial Services. The partnership helps to accelerate the use of AI to improve financial services. Deutsche Bank and NVIDIA have partnered to develop applications aimed at enhancing risk management, increasing operational efficiency and improving customer service through the utilization of NVIDIA AI Enterprise software.

Geographical Penetration

North America is Dominating the Explainable AI Market

North America has a well-established ecosystem that supports the growth of the technical industry. The includes a strong network of academic institutions, startups, research centers and established corporations collaborating on AI research and development. Growing demand for cutting-edge AI solutions in North America further helps to boost regional market growth. Collaboration between industry players, research institutions and government bodies can foster innovation and the widespread adoption of Explainable AI. North America has a history of such collaborations, driving advancements in technology.

The growing adoption of the explainable AI in the finance sector of North America helps to boost regional market growth. Financial services firms are progressively leveraging artificial intelligence to create solutions that bolster their operations, encompassing tasks such as credit score assignments, liquidity balance predictions and optimization of investment portfolios. AI enhances the speed, accuracy and efficiency of human endeavors associated with these processes, automating labor-intensive data management tasks.

Competitive Landscape

The major global players in the market include Kyndi, Alphabet, Inc., IBM Corporation, Microsoft Corporation, Amelia US LLC, BuildGroup, DataRobot, Inc., Ditto AI Ltd, DarwinAI and Factmata.

COVID-19 Impact Analysis

The COVID-19 pandemic has caused disruptions in supply chains that affect the production and distribution of technology components of explainable AI. The impacted the availability of software and hardware necessary for Explainable AI solutions. Organizations slow down or may postpone their adoption of Explainable AI technologies due to economic uncertainties and a focus on immediate operational needs.

The shift to remote work may present challenges in implementing and maintaining Explainable AI systems, especially if they require on-site installations or extensive collaboration. The importance of the global health issue has accelerated the digital transformation of several companies. Demand for Explainable AI solutions to meet pandemic-related needs, including supply chain optimization or healthcare analytics spike. Financial limitations and the fluctuating state of the economy lead organizations to decide to evaluate their investments in emerging technologies, which could affect the adoption of Explainable AI.

Russia-Ukraine War Impact Analysis

Geopolitical tensions and conflicts disrupt global supply chains. If key players in the Explainable AI market have dependencies on resources, components or talent from the regions affected by the conflict, it may lead to supply chain disruptions. Geopolitical instability often contributes to economic uncertainty. Businesses may become more cautious in their investments and decision-making, potentially affecting the demand for Explainable AI solutions.

Wars and geopolitical events can impact currency values. Changes in currency values have the potential to impact the expenses associated with importing and exporting technology, thereby influencing pricing strategies on a global scale. Geopolitical occurrences often result in alterations to regulations, trade policies and data protection laws. Entities engaged in the Explainable AI market may find it necessary to adjust to emerging regulatory landscapes. The confrontation between Russia and Ukraine has wider global ramifications, impacting markets around the globe.

By Offering

Solution
Services

By Deployment

Cloud
On-premises

By Organization Size

Small and Medium-sized Enterprises
Large Enterprises

By Technology

Machine Learning (ML)
Natural Language Processing (NLP)
Computer Vision
Big Data Analytics
Others

By Application

Fraud and Anomaly Detection
Drug Discovery and Diagnostics
Predictive Maintenance
Supply Chain Management
Identity and Access Management
Others

By End-User

Healthcare
BFSI
Aerospace and Defense
Retail and E-commerce
Public Sector and Utilities
IT and Telecommunication
Automotive
Others

By Region

North America
- U.S.
- Canada
- Mexico
Europe
- Germany
- UK
- France
- Italy
- Spain
- Rest of Europe
South America
- Brazil
- Argentina
- Rest of South America
Asia-Pacific
- China
- India
- Japan
- Australia
- Rest of Asia-Pacific
Middle East and Africa

Key Developments

On July 05, 2023, Fujitsu collaborated with Informa D&B to incorporate explainable AI technology for financial-commercial information industry. The partnership heralds a new era in decision-making by integrating explainable AI technology.
On September 06, 2023, Temenos launched Generative AI solution for banks using Generative Artificial Intelligence (AI) to automatically classify customers' banking transactions. The categorization of transactions assists banks in delivering personalized insights and recommendations, creating more captivating and user-friendly digital banking experiences and fostering customer loyalty by presenting more pertinent products and offers.
On December 30, 2022, Digite, Inc. launched RISHI-XAI the world's first EXplainable AI product for Enterprise Project Intelligence. RISHI, Digite's advanced Enterprise Project Intelligence product with eXplainable AI capabilities, is designed to meet the needs of CXOs, Delivery Heads, PMOs and other decision-makers. It integrates a knowledge system derived from Digite's extensive domain expertise in IT, a state-of-the-art Machine Learning (ML) system and eXplainable AI.

Why Purchase the Report?

To visualize the global explainable AI market segmentation based on offering, deployment, organization size, technology, application, end-user and region, as well as understand key commercial assets and players.
Identify commercial opportunities by analyzing trends and co-development.
Excel data sheet with numerous data points of explainable AI market-level with all segments.
PDF report consists of a comprehensive analysis after exhaustive qualitative interviews and an in-depth study.
Product mapping available as excel consisting of key products of all the major players.

The global explainable AI market report would provide approximately 86 tables, 90 figures and 245 Pages.

Target Audience 2024

Manufacturers/ Buyers
Industry Investors/Investment Bankers
Research Professionals
Emerging Companies

1. Methodology and Scope

1.1. Research Methodology
1.2. Research Objective and Scope of the Report

2. Definition and Overview

3. Executive Summary

3.1. Snippet by Offering
3.2. Snippet by Deployment
3.3. Snippet by Organization Size
3.4. Snippet by Technology
3.5. Snippet by Application
3.6. Snippet by End-User
3.7. Snippet by Region

4. Dynamics

4.1. Impacting Factors
- 4.1.1. Drivers
  - 4.1.1.1. Growing Technological Advancements
  - 4.1.1.2. Growing Consumer's E-Waste Awareness
- 4.1.2. Restraints
  - 4.1.2.1. Initial High Implementation Costs
- 4.1.3. Opportunity
- 4.1.4. Impact Analysis

5. Industry Analysis

5.1. Porter's Five Force Analysis
5.2. Supply Chain Analysis
5.3. Pricing Analysis
5.4. Regulatory Analysis
5.5. Russia-Ukraine War Impact Analysis
5.6. DMI Opinion

6. COVID-19 Analysis

6.1. Analysis of COVID-19
- 6.1.1. Scenario Before COVID
- 6.1.2. Scenario During COVID
- 6.1.3. Scenario Post COVID
6.2. Pricing Dynamics Amid COVID-19
6.3. Demand-Supply Spectrum
6.4. Government Initiatives Related to the Market During Pandemic
6.5. Manufacturers Strategic Initiatives
6.6. Conclusion

7. By Offering

7.1. Introduction
- 7.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 7.1.2. Market Attractiveness Index, By Offering
7.2. Solution*
- 7.2.1. Introduction
- 7.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
7.3. Services

8. By Deployment

8.1. Introduction
- 8.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 8.1.2. Market Attractiveness Index, By Deployment
8.2. Cloud*
- 8.2.1. Introduction
- 8.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
8.3. On-premises

9. By Organization Size

9.1. Introduction
- 9.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 9.1.2. Market Attractiveness Index, By Organization Size
9.2. Small and Medium-sized Enterprises*
- 9.2.1. Introduction
- 9.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
9.3. Large Enterprises

10. By Technology

10.1. Introduction
- 10.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 10.1.2. Market Attractiveness Index, By Technology
10.2. Machine Learning (ML)*
- 10.2.1. Introduction
- 10.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
10.3. Natural Language Processing (NLP)
10.4. Computer Vision
10.5. Big Data Analytics
10.6. Others

11. By Application

11.1. Introduction
- 11.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 11.1.2. Market Attractiveness Index, By Application
11.2. Fraud and Anomaly Detection*
- 11.2.1. Introduction
- 11.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
11.3. Drug Discovery and Diagnostics
11.4. Predictive Maintenance
11.5. Supply Chain Management
11.6. Identity and Access Management
11.7. Others

12. By End-User

12.1. Introduction
- 12.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 12.1.2. Market Attractiveness Index, By End-User
12.2. Healthcare*
- 12.2.1. Introduction
- 12.2.2. Market Size Analysis and Y-o-Y Growth Analysis (%)
12.3. BFSI
12.4. Aerospace and Defense
12.5. Retail and E-commerce
12.6. Public Sector and Utilities
12.7. IT and Telecommunication
12.8. Automotive
12.9. Others

13. By Region

13.1. Introduction
- 13.1.1. Market Size Analysis and Y-o-Y Growth Analysis (%), By Region
- 13.1.2. Market Attractiveness Index, By Region
13.2. North America
- 13.2.1. Introduction
- 13.2.2. Key Region-Specific Dynamics
- 13.2.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.2.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.2.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.2.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.2.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.2.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.2.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.2.9.1. U.S.
  - 13.2.9.2. Canada
  - 13.2.9.3. Mexico
13.3. Europe
- 13.3.1. Introduction
- 13.3.2. Key Region-Specific Dynamics
- 13.3.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.3.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.3.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.3.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.3.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.3.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.3.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.3.9.1. Germany
  - 13.3.9.2. UK
  - 13.3.9.3. France
  - 13.3.9.4. Italy
  - 13.3.9.5. Spain
  - 13.3.9.6. Rest of Europe
13.4. South America
- 13.4.1. Introduction
- 13.4.2. Key Region-Specific Dynamics
- 13.4.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.4.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.4.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.4.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.4.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.4.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.4.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.4.9.1. Brazil
  - 13.4.9.2. Argentina
  - 13.4.9.3. Rest of South America
13.5. Asia-Pacific
- 13.5.1. Introduction
- 13.5.2. Key Region-Specific Dynamics
- 13.5.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.5.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.5.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.5.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.5.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.5.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User
- 13.5.9. Market Size Analysis and Y-o-Y Growth Analysis (%), By Country
  - 13.5.9.1. China
  - 13.5.9.2. India
  - 13.5.9.3. Japan
  - 13.5.9.4. Australia
  - 13.5.9.5. Rest of Asia-Pacific
13.6. Middle East and Africa
- 13.6.1. Introduction
- 13.6.2. Key Region-Specific Dynamics
- 13.6.3. Market Size Analysis and Y-o-Y Growth Analysis (%), By Offering
- 13.6.4. Market Size Analysis and Y-o-Y Growth Analysis (%), By Deployment
- 13.6.5. Market Size Analysis and Y-o-Y Growth Analysis (%), By Organization Size
- 13.6.6. Market Size Analysis and Y-o-Y Growth Analysis (%), By Technology
- 13.6.7. Market Size Analysis and Y-o-Y Growth Analysis (%), By Application
- 13.6.8. Market Size Analysis and Y-o-Y Growth Analysis (%), By End-User

14. Competitive Landscape

14.1. Competitive Scenario
14.2. Market Positioning/Share Analysis
14.3. Mergers and Acquisitions Analysis

15. Company Profiles

15.1. Kyndi*
- 15.1.1. Company Overview
- 15.1.2. Product Portfolio and Description
- 15.1.3. Financial Overview
- 15.1.4. Key Developments
15.2. Alphabet, Inc.
15.3. IBM Corporation
15.4. Microsoft Corporation
15.5. Amelia US LLC
15.6. BuildGroup
15.7. DataRobot, Inc.
15.8. Ditto AI Ltd
15.9. DarwinAI
15.10. Factmata

LIST NOT EXHAUSTIVE