The Global Explainable AI Market size is expected to reach $22.20 billion by 2032, rising at a market growth of 20.4% CAGR during the forecast period.
The IT & telecommunication sector leads the explainable AI market due to its early and extensive adoption of advanced AI technologies for network optimization, cybersecurity, customer service, and fraud detection. The industry’s complex data environments require AI models whose decisions can be interpreted and trusted by engineers, clients, and regulators alike. Explainable AI helps these organizations troubleshoot issues, ensure fair decision-making in automated systems, and meet regulatory standards for transparency. The sector’s commitment to digital transformation and data-driven decision-making continues to drive robust demand for explainable AI solutions. Thus, the IT & telecommunication segment procured 21% revenue share in the market in 2024.
The major strategies followed by the market participants are Partnerships as the key developmental strategy to keep pace with the changing demands of end users. For instance, In May, 2025, IBM Corporation teamed up with AWS, a cloud computing company to deliver agentic AI capabilities, integrating IBM’s watsonx Orchestrate with Amazon Q index for enhanced AI decision-making. They offer pre-built domain agents, AI governance tools, and software on AWS Marketplace, enabling scalable, trustworthy, and explainable AI to transform enterprise automation and workflows. Additionally, In May, 2025, SAS Institute Inc. announced the partnership with Microsoft, an IT company to launch AI-driven decision-making tools like SAS Decision Builder and Viya Copilot. These tools integrate AI models within Microsoft Fabric and Azure, enhancing enterprise analytics with transparency, control, and human oversight, while exploring quantum AI’s future potential in complex simulations.
Based on the Analysis presented in the Cardinal matrix; Google LLC, Microsoft Corporation, NVIDIA Corporation, and Amazon Web Services, Inc. are the forerunners in the Explainable AI Market. Companies such as Salesforce, Inc., IBM Corporation, and SAP SE are some of the key innovators in Explainable AI Market. In May, 2025, Microsoft Corporation announced the partnership with Accenture, an IT company to deploy Azure AI Foundry, enabling scalable, explainable, and secure generative AI solutions. With over 75 use cases across industries and 16 in production, the platform reduced development time by 50%, boosted efficiency, ensured compliance, and strengthened responsible AI through observability, red teaming, and content safety.
Additionally, trust is a cornerstone of technological adoption, and in the context of artificial intelligence, it is both vital and elusive. The third major driver for the Explainable AI market is the critical need to build and maintain user trust in AI-powered systems. As AI technologies increasingly influence decisions in everyday life - from loan approvals and medical diagnoses to legal recommendations and personalized marketing - the demand for transparency and understandable reasoning is growing among end-users. Black-box AI models, while powerful, often fail to gain user confidence because stakeholders cannot easily interpret how conclusions are reached or assess their reliability. In conclusion, building trust through explainability is emerging as a critical enabler of AI adoption, empowering users, organizations, and markets to harness the full potential of artificial intelligence with confidence, clarity, and peace of mind.
The Explainable AI (XAI) Market value chain begins with Research & Development (R&D), which focuses on model transparency, fairness, and interpretability. This flows into Technology Development and Integration, where explainability tools and AI algorithms are embedded into systems. Data Acquisition and Management ensures high-quality, unbiased datasets that fuel XAI models. In Productization and Service Development, these technologies are turned into deployable, user-friendly solutions. Sales, Marketing, and Distribution drive adoption, while Regulatory Compliance and Ethics ensure trust, privacy, and fairness. Continuous Customer Support and Feedback feeds back into R&D, fostering iterative innovation.
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Acquisitions, and Partnerships & Collaborations.
The Explainable AI (XAI) market experiences growing and dynamic competition, driven by rising demand for transparency in AI decision-making across sectors like healthcare, finance, and government. Numerous players are investing heavily in research to develop interpretable models, visual explanations, and rule-based systems. The market sees competition in algorithm innovation, integration capabilities with existing AI infrastructure, and compliance with ethical and regulatory standards. Open-source tools and academic contributions also intensify rivalry. As AI adoption rises, vendors compete on accuracy, interpretability, and trust-building features.
The IT & telecommunication sector leads the explainable AI market due to its early and extensive adoption of advanced AI technologies for network optimization, cybersecurity, customer service, and fraud detection. The industry’s complex data environments require AI models whose decisions can be interpreted and trusted by engineers, clients, and regulators alike. Explainable AI helps these organizations troubleshoot issues, ensure fair decision-making in automated systems, and meet regulatory standards for transparency. The sector’s commitment to digital transformation and data-driven decision-making continues to drive robust demand for explainable AI solutions. Thus, the IT & telecommunication segment procured 21% revenue share in the market in 2024.
The major strategies followed by the market participants are Partnerships as the key developmental strategy to keep pace with the changing demands of end users. For instance, In May, 2025, IBM Corporation teamed up with AWS, a cloud computing company to deliver agentic AI capabilities, integrating IBM’s watsonx Orchestrate with Amazon Q index for enhanced AI decision-making. They offer pre-built domain agents, AI governance tools, and software on AWS Marketplace, enabling scalable, trustworthy, and explainable AI to transform enterprise automation and workflows. Additionally, In May, 2025, SAS Institute Inc. announced the partnership with Microsoft, an IT company to launch AI-driven decision-making tools like SAS Decision Builder and Viya Copilot. These tools integrate AI models within Microsoft Fabric and Azure, enhancing enterprise analytics with transparency, control, and human oversight, while exploring quantum AI’s future potential in complex simulations.
Cardinal Matrix - Market Competition Analysis
Based on the Analysis presented in the Cardinal matrix; Google LLC, Microsoft Corporation, NVIDIA Corporation, and Amazon Web Services, Inc. are the forerunners in the Explainable AI Market. Companies such as Salesforce, Inc., IBM Corporation, and SAP SE are some of the key innovators in Explainable AI Market. In May, 2025, Microsoft Corporation announced the partnership with Accenture, an IT company to deploy Azure AI Foundry, enabling scalable, explainable, and secure generative AI solutions. With over 75 use cases across industries and 16 in production, the platform reduced development time by 50%, boosted efficiency, ensured compliance, and strengthened responsible AI through observability, red teaming, and content safety.
COVID-19 Impact Analysis
During the initial phase of the COVID-19 pandemic, the explainable AI (XAI) market experienced significant setbacks. Many organizations across industries prioritized operational continuity and business survival over innovation and digital transformation. As a result, the adoption of advanced AI solutions, including XAI, slowed considerably. Budget constraints and economic uncertainties forced companies to delay or cancel projects related to AI explainability, as investments were redirected toward more immediate concerns, such as enabling remote work and maintaining core operations. Overall, the COVID-19 crisis resulted in a negative, albeit temporary, impact on the explainable AI market, delaying its anticipated growth trajectory. Thus, the COVID-19 pandemic had negative impact on the market.Driving and Restraining Factors
Drivers
- Regulatory Compliance and Transparency Mandates
- Mitigating Bias and Enhancing Ethical AI Adoption
- Building User Trust and Driving Widespread AI Adoption
- Improving Model Performance and Enabling Effective Human-AI Collaboration
Restraints
- Trade-off Between Explainability and Model Accuracy
- Lack of Standardization and Benchmarking in XAI Approaches
- Technical Complexity and Resource Constraints
Opportunities
- Integration of Explainable AI in Critical Decision-Making Sectors
- Expansion of Explainable AI into Edge and IoT Applications
- Development of Explainable AI as a Competitive Differentiator in Consumer Applications
Challenges
- Complexity of Interpreting Deep Learning and Advanced AI Models
- Diverse Stakeholder Needs and the Challenge of Universal Explainability
- Balancing Intellectual Property Protection and Transparency
Market Growth Factors
One of the most significant drivers propelling the Explainable AI (XAI) market is the increasing global focus on regulatory compliance and transparency requirements across industries. As artificial intelligence systems are being embedded deeper into decision-making processes in sectors such as finance, healthcare, insurance, and the public sector, there is mounting pressure from regulators to ensure these decisions are both fair and understandable. In conclusion, stricter regulations and growing demands for transparency are accelerating Explainable AI adoption, making compliance a key driver in the market.Additionally, trust is a cornerstone of technological adoption, and in the context of artificial intelligence, it is both vital and elusive. The third major driver for the Explainable AI market is the critical need to build and maintain user trust in AI-powered systems. As AI technologies increasingly influence decisions in everyday life - from loan approvals and medical diagnoses to legal recommendations and personalized marketing - the demand for transparency and understandable reasoning is growing among end-users. Black-box AI models, while powerful, often fail to gain user confidence because stakeholders cannot easily interpret how conclusions are reached or assess their reliability. In conclusion, building trust through explainability is emerging as a critical enabler of AI adoption, empowering users, organizations, and markets to harness the full potential of artificial intelligence with confidence, clarity, and peace of mind.
Market Restraining Factors
However, A fundamental restraint on the widespread adoption of Explainable AI is the persistent trade-off between model explainability and predictive accuracy, particularly in complex, real-world applications. The most accurate AI models today - especially deep learning architectures such as neural networks, ensemble methods like random forests, and advanced gradient boosting algorithms - are often referred to as “black box” models. Their internal workings are highly complex, featuring layers upon layers of parameters and nonlinear transformations that are exceedingly difficult for humans to interpret. While these models typically achieve state-of-the-art performance on tasks ranging from image recognition and natural language processing to fraud detection, their opacity means the rationale behind any specific prediction is largely inscrutable to the end-user. In conclusion, the trade-off between explainability and model accuracy limits where explainable AI can be used, forcing organizations to choose between transparency and optimal performance.Value Chain Analysis
The Explainable AI (XAI) Market value chain begins with Research & Development (R&D), which focuses on model transparency, fairness, and interpretability. This flows into Technology Development and Integration, where explainability tools and AI algorithms are embedded into systems. Data Acquisition and Management ensures high-quality, unbiased datasets that fuel XAI models. In Productization and Service Development, these technologies are turned into deployable, user-friendly solutions. Sales, Marketing, and Distribution drive adoption, while Regulatory Compliance and Ethics ensure trust, privacy, and fairness. Continuous Customer Support and Feedback feeds back into R&D, fostering iterative innovation.
Market Share Analysis
The leading players in the market are competing with diverse innovative offerings to remain competitive in the market. The above illustration shows the percentage of revenue shared by some of the leading companies in the market. The leading players of the market are adopting various strategies in order to cater demand coming from the different industries. The key developmental strategies in the market are Acquisitions, and Partnerships & Collaborations.
Deployment Outlook
Based on deployment, the market is characterized into cloud and on-premise. The cloud segment garnered 63% revenue share in the market in 2024. Cloud-based explainable AI solutions offer unparalleled scalability and flexibility, enabling organizations to deploy and manage AI models without the need for extensive on-premise infrastructure. This is especially beneficial for businesses with fluctuating computational requirements, as cloud platforms allow for rapid provisioning of resources based on demand.
Component Outlook
On the basis of component, the market is classified into solution and services. The services segment recorded 19% revenue share in the market in 2024. It plays a vital supporting role in the XAI market. Services include consulting, implementation, integration, maintenance, and training. The demand for these services is being driven by organizations that require expert guidance to select, customize, and optimize XAI solutions for their specific needs. Many enterprises lack in-house expertise in AI explainability, making professional services essential for successful adoption and ongoing management.Application Outlook
By application, the market is divided into fraud & anomaly detection, drug discovery & diagnostics, predictive maintenance, supply chain management, identity access management & others. The identity access management & others segment held 22% revenue share in the market in 2024. This encompasses a broad range of applications, including cybersecurity, human resources, and regulatory compliance. In IAM, explainable AI helps organizations monitor user behaviors, detect unusual access patterns, and justify access decisions - crucial for maintaining robust security postures and meeting compliance requirements.End Use Outlook
Based on end-use, the market is segmented into IT & telecommunication, healthcare, BFSI, aerospace & defense, retail & e-commerce, public sector & utilities, automotive, and others. The BFSI segment attained 15% revenue share in the market in 2024. The BFSI segment relies heavily on explainable AI to address regulatory compliance, fraud detection, credit scoring, and risk assessment. Financial institutions face stringent requirements to demonstrate how decisions are made - especially when denying loans, flagging transactions, or making investment recommendations. Explainable AI ensures transparency, reduces bias, and builds trust with both customers and regulators. As digital banking and fintech adoption accelerates, so does the need for AI models that can clearly justify their actions and predictions.Regional Outlook
Region-wise, the market is analyzed across North America, Europe, Asia Pacific, and LAMEA. The North America segment recorded the largest revenue share in the market in 2024. North America dominates the explainable AI market owing to its early adoption of advanced AI technologies, robust investment climate, and a highly developed digital infrastructure. The region is home to many leading technology firms, research institutions, and AI startups, especially in the United States and Canada.Market Competition and Attributes
The Explainable AI (XAI) market experiences growing and dynamic competition, driven by rising demand for transparency in AI decision-making across sectors like healthcare, finance, and government. Numerous players are investing heavily in research to develop interpretable models, visual explanations, and rule-based systems. The market sees competition in algorithm innovation, integration capabilities with existing AI infrastructure, and compliance with ethical and regulatory standards. Open-source tools and academic contributions also intensify rivalry. As AI adoption rises, vendors compete on accuracy, interpretability, and trust-building features.
Recent Strategies Deployed in the Market
- Mar-2025: IBM Corporation unveiled Granite 3.2, an advanced, efficient large language model with enhanced reasoning and vision-language capabilities. It offers cost-effective AI for enterprises, matching larger models in performance. Features include chain of thought reasoning, verbalized confidence for safety, and customization via IBM’s Docling toolkit, supporting document-heavy workflows.
- May-2025: Microsoft Corporation announced the partnership with Accenture, an IT company to deploy Azure AI Foundry, enabling scalable, explainable, and secure generative AI solutions. With over 75 use cases across industries and 16 in production, the platform reduced development time by 50%, boosted efficiency, ensured compliance, and strengthened responsible AI through observability, red teaming, and content safety.
- May-2025: Microsoft Corporation unveiled AI agent orchestrator, streamlines cancer care by coordinating multimodal healthcare data and workflows like tumor boards. It enhances clinical decisions, integrates with tools like Teams, and offers explainability in AI outputs. Leading medical institutions are exploring its impact on oncology and complex care.
- May-2025: IBM Corporation teamed up with AWS, a cloud computig company to deliver agentic AI capabilities, integrating IBM’s watsonx Orchestrate with Amazon Q index for enhanced AI decision-making. They offer pre-built domain agents, AI governance tools, and software on AWS Marketplace, enabling scalable, trustworthy, and explainable AI to transform enterprise automation and workflows.
- May-2025: SAS Institute Inc. announced a partnership with Microsoft, an IT company to launch AI-driven decision-making tools like SAS Decision Builder and Viya Copilot. These tools integrate AI models within Microsoft Fabric and Azure, enhancing enterprise analytics with transparency, control, and human oversight, while exploring quantum AI’s future potential in complex simulations.
- Apr-2025: Databricks, Inc. teamed up with Kinaxis, a software company to enhance AI-powered supply chain orchestration. Combining Kinaxis Maestro™ with Databricks’ platform enables unified data, faster insights, and scalable AI. This collaboration improves supply chain agility, transparency, and decision-making, helping businesses respond swiftly to disruptions with explainable, traceable AI.
- Feb-2025: Google LLC teamed up with Salesforce, a software company by integrating Google’s Gemini AI models into Salesforce’s Agentforce platform. This collaboration enhances AI flexibility, trust, and innovation, enabling multimodal capabilities, faster responses, and seamless integration across systems, empowering businesses with advanced automation and improved customer interactions.
List of Key Companies Profiled
- Microsoft Corporation
- IBM Corporation
- Google LLC (Alphabet Inc.)
- Salesforce, Inc.
- Intel Corporation
- NVIDIA Corporation
- SAS Institute Inc.
- Databricks, Inc.
- Amazon Web Services, Inc.
- SAP SE
Market Report Segmentation
By Deployment
- Cloud
- On-premise
By Component
- Solution
- Services
By Application
- Fraud & Anomaly Detection
- Drug Discovery & Diagnostics
- Predictive Maintenance
- Supply Chain Management
- Identity, Access Management & Others
By End-Use
- IT & Telecommunication
- Healthcare
- BFSI
- Aerospace & Defense
- Retail & E-commerce
- Public Sector & Utilities
- Automotive
- Other End-use
By Geography
- North America
- US
- Canada
- Mexico
- Rest of North America
- Europe
- Germany
- UK
- France
- Russia
- Spain
- Italy
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Singapore
- Malaysia
- Rest of Asia Pacific
- LAMEA
- Brazil
- Argentina
- UAE
- Saudi Arabia
- South Africa
- Nigeria
- Rest of LAMEA
Table of Contents
Chapter 1. Market Scope & Methodology
Chapter 2. Market at a Glance
Chapter 3. Market Overview
Chapter 4. Competition Analysis - Global
Chapter 5. Value Chain Analysis of Explainable AI Market
Chapter 7. Global Explainable AI Market by Deployment
Chapter 8. Global Explainable AI Market by Component
Chapter 9. Global Explainable AI Market by Application
Chapter 10. Global Explainable AI Market by End-use
Chapter 11. Global Explainable AI Market by Region
Chapter 12. Company Profiles
Companies Mentioned
- Microsoft Corporation
- IBM Corporation
- Google LLC (Alphabet Inc.)
- Salesforce, Inc.
- Intel Corporation
- NVIDIA Corporation
- SAS Institute Inc.
- Databricks, Inc.
- Amazon Web Services, Inc.
- SAP SE
