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Explainable AI Market Size, Share, Growth & Industry Analysis, By Offering (Software, Services), By Deployment (Cloud Based, On Premises), By Application (Fraud and Anomaly Detection, Drug Discovery & Diagnostics), By End-Use Industry, and Regional Analysis, 2024-2031
Pages: 200 | Base Year: 2023 | Release: March 2025 | Author: Versha V.
The market encompasses software and services designed to enhance AI transparency across industries. It includes cloud-based and on-premises solutions for applications such as fraud detection, drug discovery, and predictive maintenance.
Key end-use sectors include BFSI, healthcare, IT & telecommunications, aerospace & defense, ensuring accountable AI decision-making.
The global explainable AI market size was valued at USD 8,730.0 million in 2023 and is projected to grow from USD 10,105.8 million in 2024 to USD 32,430.0 million by 2031, exhibiting a CAGR of 18.12% during the forecast period.
This growth is driven by the increasing adoption of AI across industries, coupled with the rising need for transparency, accountability, and regulatory compliance in AI-driven decision-making. Organizations are investing in explainable AI to improve risk management, customer trust, and AI model performance while mitigating bias and ensuring fairness.
Major companies operating in the explainable AI industry are Gyan, Inc., Intellico, Tensor AI Solutions, Accenture, Tredence.Inc., Fiddler, IBM, C3.ai, Inc., Intel Corporation, Google, Mphasis, Temenos Headquarters SA, Salesforce, Inc, Equifax, Inc., FICO, and others.
With increasing regulatory scrutiny and the expansion of generative AI, organizations are investing in interpretability solutions to enhance trust, mitigate risks, and ensure responsible AI deployment across diverse industries.
Market Driver
Rising Demand for Ethical and Responsible AI
The growth of the explainable AI market is driven by the increasing emphasis on ethical and responsible AI practices. As AI adoption accelerates, concerns regarding bias, fairness, and accountability in automated decision-making have intensified.
Organizations face pressure from stakeholders, customers, and regulatory bodies to ensure transparency and eliminate discrimination in AI-driven decisions.
Businesses deploying AI in critical applications such as hiring, lending, and medical diagnostics are recognizing the importance of responsible AI to foster trust and mitigate reputational risks. Explainable AI solutions help identify and rectify biases, ensuring fair outcomes while aligning with corporate social responsibility (CSR) initiatives.
Market Challenge
High Computational Costs
A major challenge hampering the expansion of the explainable AI market is the high computational cost associated with implementing interpretability techniques. Explainable AI implementation requires extensive computation to analyze model predictions and generate clear, traceable insights.
These methods often involve running multiple iterations, modifying input data, or training additional models, increasing processing time and infrastructure demands. For enterprises deploying AI at scale, particularly in time-sensitive applications such as fraud detection, healthcare diagnostics, and autonomous systems, these added resource requirements can hinder adoption and efficiency.
To overcome these challenges, businesses can optimize their AI workflows and use scalable, cost-effective technologies. A strategic approach is to prioritize model transparency in critical areas such as high-risk transactions, regulatory reporting, and customer-facing AI systems, ensuring efficiency without unnecessary complexityacross all AI functions.
Market Trend
Expansion of Explainable AI in Generative AI Applications
A key trend in the market is the increasing emphasis of interpretability in generative AI applications. As large language models (LLMs) and generative AI tools gain traction, businesses seek transparency into generative AI outputs, particularly in content creation, automated decision-making, and AI-driven recommendations.
Interpretability solutions for LLMs are being developed to provide token attribution, contextual reasoning, and bias detection, ensuring responsible deployment. This trend is expected to accelerate as generative AI adoption expands across industries such as media, marketing, and customer service.
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Segmentation |
Details |
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By Offering |
Software (Standalone Software, Integrated Software, Automated Reporting Tools, Others), Services (Consulting Services, Deployment and Integration Services, Training and Education Services, Others) |
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By Deployment |
Cloud Based, On Premises |
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By Application |
Fraud and Anomaly Detection, Drug Discovery & Diagnostics, Predictive Maintenance, Others |
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By End-Use Industry |
Banking, Financial Services, and Insurance (BFSI), Healthcare, IT and Telecommunications, Aerospace and Defense, Others |
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By Region |
North America: U.S., Canada, Mexico |
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Europe: France, UK, Spain, Germany, Italy, Russia, Rest of Europe |
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Asia-Pacific: China, Japan, India, Australia, ASEAN, South Korea, Rest of Asia-Pacific |
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Middle East & Africa: Turkey, UAE, Saudi Arabia, South Africa, Rest of Middle East & Africa |
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South America: Brazil, Argentina, Rest of South America |
Market Segmentation
Based on region, the global market has been classified into North America, Europe, Asia Pacific, Middle East & Africa, and Latin America.
North America explainable AI market share stood at around 36.42% in 2023, valued at USD 3,179.5 million. This dominance is is reinforced by substantial investments in AI research, a well-established technology ecosystem, and regulatory frameworks emphasizing transparency and accountability in AI-driven decision-making.
The region is home to leading AI companies, including Google and IBM, which are actively developing and integrating tools into AI solutions.
The increasing deployment of AI in fraud detection, autonomous systems, and medical diagnostics further fuels regional market growth. Continuous advancements in machine learning interpretability and responsible AI frameworks reinforce the position of the regional market.
Asia Pacific explainable AI industry is poised to grow at a significant CAGR of 18.45% over the forecast period, supported by rapid digital transformation, increasing AI adoption across industries, and supportive government initiatives promoting AI research and development.
Countries such as China, Japan, and India are investing heavily in AI-driven solutions, with a growing emphasis on enhancing transparency and fairness in automated decision-making. As businesses prioritize ethical and interpretable AI, Asia Pacific is expected to emerge as a key market for explainable AI.
The explainable AI industry is highly competitive, with established technology firms, emerging AI startups, and research-driven enterprises striving foe leadership. Companies are prioritizing AI transparency to ensure regulatory compliance and meet industry demands for responsible AI.
To strengthen their market presence, businesses are investing in advanced machine learning techniques, algorithmic transparency, and AI governance frameworks. Strategic initiatives such as partnerships, mergers, and acquisitions are fostering innovation, allowing firms to integrate features into AI-powered applications across sectors such as finance, healthcare, and autonomous systems.
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