首页 > 市场调查报告书 > 通讯

人工智能

市场调查报告书

商品编码

1423509

全球可解释人工智慧市场 - 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: 约2个工作天内

价格

※ 本网页内容可能与最新版本有所差异。详细情况请与我们联繫。

简介目录

概述

全球可解释人工智慧市场将于 2023 年达到 52 亿美元，预计到 2031 年将达到 221 亿美元，2024-2031 年预测期间CAGR为 20.2%。

如今，大约 28% 的公民总体上愿意信任人工智慧系统。对人工智慧日益缺乏的信任促使欧盟（EU）和美国加强监管。这些呼吁似乎是有效的，因为监管机构目前正在立法，要求人工智慧模型遵守特定的可解释性水平，包括解释和阐明人工智慧结果的能力。

专案智慧主要参与者不断推出的产品有助于推动预测期内的市场成长。例如，2022 年 12 月 30 日，Digitite, Inc. 推出了世界上第一个用于企业专案智慧的可解释人工智慧产品。 RISHI 代表 Digite 先进的企业专案智慧产品，整合了 eXplainable AI 和机器学习系统。 RISHI 专为 CXO、交付主管、PMO 和决策者量身定制，将源自 Digite 丰富 IT 领域经验的知识系统与最先进的 ML 功能相结合。

由于金融领域越来越多地采用可解释的人工智慧，北美成为市场的主导地区。政府不断推出的可解释人工智慧措施有助于推动预测期内区域市场的成长。人们越来越需要提高对深度学习（也称为可解释人工智慧）不透明本质的理解的方法。

美国国防部高级研究计划局和电脑协会的公平、责任和透明度会议是可解释的人工智慧活动的两个着名例子。在医学影像领域，电脑辅助干预主办了医学影像计算国际会议，每年一度的会议致力于医学影像计算中机器智慧的可解释性。

动力学

越来越多地采用可解释的人工智慧 (XAI) 进行风险管理

风险管理是许多企业（包括银行、医疗保健和网路安全）的重要组成部分。随着可解释的人工智慧方法越来越多地用于风险评估和决策过程，组织对人工智慧模型如何得出结果有了更多的了解。监管机构、客户和内部决策者都是利益相关者，他们的信任因这种真实性的增加而得到加强。

许多企业的监管机构都需要可解释的人工智慧系统，特别是在银行和医疗保健等复杂领域。可解释的人工智慧为人工智慧驱动的行动提供了可理解的理由，这可以帮助组织遵守监管标准。这种对法律的遵守进一步鼓励了可解释的人工智慧风险管理系统的使用。组织透过使用可解释的人工智慧技术来识别并减少用于风险评估的人工智慧模型中的偏差和错误。可解释的人工智慧透过为模型预测提供解释来帮助识别潜在的偏差和不准确性。这使组织能够采取纠正措施并提高风险管理程序的准确性和公平性。

4.O产业快速成长

第四次工业革命（4.0）产业的快速扩张对全球可解释人工智慧市场的成长做出了重大贡献。随着各行业经历数位转型并将人工智慧等先进技术融入其营运中，对透明且可解释的人工智慧解决方案的需求变得至关重要。可解释的人工智慧解决了与信任、责任和监管合规相关的问题，使其在 4.0 行业中不可或缺。工业4.0背后的驱动力在于数位科技在製造业的运用，包括物联网（IoT）、人工智慧（AI）和巨量资料分析。

随着工业 4.0 的发展势头，製造商正在经历前所未有的效率水平。据 MPI 集团称，32% 的製造商预计工业 4.0 对流程、工厂和供应链的影响将导致盈利能力增长超过 10%。随着 2023 年的临近，越来越多的製造商正在利用数位化参与来增强营运。具体来说，56% 的製造商倾向于与供应商进行数位化合作，以促进品质指标的即时共享。

人工智慧模型的复杂性

复杂的人工智慧模型通常需要大量资源，例如熟练的资料科学家、运算能力以及漫长的开发和培训週期。开发费用的增加和时间的延长可能会阻碍资源有限的小型企业或组织采用人工智慧模型。

在现实场景中部署高度复杂的人工智慧模型可能会遇到可扩展性挑战，特别是当它们依赖大量运算资源或难以有效处理大量资料时。可扩展性限制可能会阻碍人工智慧模型在不同行业和应用中的广泛采用。

随着人工智慧模型复杂性的增加，其可解释性和可解释性通常会降低。由于监管要求或道德问题，缺乏透明度可能会阻碍可解释性至关重要的行业的采用，例如医疗保健、金融和法律领域。虽然复杂的人工智慧模型通常在特定任务或领域表现出色，但它们在实现性能与可解释性、公平性和稳健性等其他基本因素之间的平衡时可能会遇到困难。这些因素之间的权衡可能会限制复杂人工智慧模型的实际适用性。

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

16. Appendix

16.1. About Us and Services
16.2. Contact Us

02-2729-4219

+886-2-2729-4219

License/价格

全球可解释人工智慧市场 - 2024-2031

Global Explainable AI Market - 2024-2031

目录

第 1 章：方法与范围

第 2 章：定义与概述

第 3 章：执行摘要

第 4 章：动力学

第 5 章：产业分析

第 6 章：COVID-19 分析

第 7 章：透过奉献

第 8 章：透过部署

第 9 章：按组织规模

第 10 章：按技术

第 11 章：按应用

第 12 章：最终用户

第 13 章：按地区

第14章：竞争格局

第 15 章：公司简介

第 16 章：附录