“Global Explainable AI Market to reach a market value of USD 22.20 Billion by 2032 growing at a CAGR of 20.4%”
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 KBV 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.
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.
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.
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.
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.
Free Valuable Insights: Global Explainable AI Market size to reach USD 22.20 Billion by 2032
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.
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.
| Category | Details |
|---|---|
| Use Case Title | Confidential |
| Date | 2025 |
| Entities Involved | Confidential |
| Objective | To support insurance companies in implementing and maintaining compliant, transparent AI models for risk assessment, underwriting, and claims management through specialized Explainable AI services. |
| Context and Background | With rising regulatory and ethical demands in 2025, PwC expanded its Explainable AI services to assist insurers in embedding fairness and accountability in their AI systems. This included advisory and managed services for AI in risk and claims workflows. |
| Description | PwC’s services included audits for AI transparency and bias, integration of XAI frameworks, regulatory compliance support, training for underwriters and claims teams, and continuous monitoring. AXA partnered with PwC to align with EU/global standards. |
| Key Capabilities Deployed |
|
| Benefits |
|
| Source | Confidential |
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.
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.
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.
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.
| Report Attribute | Details |
|---|---|
| Market size value in 2024 | USD 5.11 Billion |
| Market size forecast in 2032 | USD 22.20 Billion |
| Base Year | 2024 |
| Historical Period | 2021 to 2023 |
| Forecast Period | 2025 to 2032 |
| Revenue Growth Rate | CAGR of 20.4% from 2025 to 2032 |
| Number of Pages | 425 |
| Number of Tables | 503 |
| Report coverage | Market Trends, Revenue Estimation and Forecast, Segmentation Analysis, Regional and Country Breakdown, Competitive Landscape, Market Share Analysis, Porter’s 5 Forces Analysis, Company Profiling, Companies Strategic Developments, SWOT Analysis, Winning Imperatives |
| Segments covered | Deployment, Component, Application, End-use, Region |
| Country scope |
|
| Companies Included | Microsoft Corporation, IBM Corporation, Google LLC (Alphabet Inc.), Salesforce, Inc., Intel Corporation, NVIDIA Corporation, SAS Institute Inc., Databricks, Inc., Amazon Web Services, Inc. and SAP SE |
By Deployment
By Component
By Application
By End-Use
By Geography
This Market size is expected to reach $22.20 billion by 2032.
Regulatory Compliance and Transparency Mandates are driving the Market in coming years, however, Trade-off Between Explainability and Model Accuracy restraints the growth of the Market.
Microsoft Corporation, IBM Corporation, Google LLC (Alphabet Inc.), Salesforce, Inc., Intel Corporation, NVIDIA Corporation, SAS Institute Inc., Databricks, Inc., Amazon Web Services, Inc. and SAP SE
The expected CAGR of this Market is 20.4% from 2023 to 2032.
On-premise segment is leading the Market by deployment in 2024; thereby, achieving a market value of $8.01 billion by 2032.
The North America region dominated the Market by Region in 2024, and would continue to be a dominant market till 2032; thereby, achieving a market value of $8.39 billion by 2032.
Our team of dedicated experts can provide you with attractive expansion opportunities for your business.
Unique Offerings 