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PUBLISHER: DataM Intelligence | PRODUCT CODE: 1423509

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PUBLISHER: DataM Intelligence | PRODUCT CODE: 1423509

Global Explainable AI Market - 2024-2031

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Overview

Global Explainable AI Market reached US$ 5.2 Billion in 2023 and is expected to reach US$ 22.1 Billion by 2031, growing with a CAGR of 20.2% during the forecast period 2024-2031.

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
Product Code: ICT7935

Table of Contents

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
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