The global explainable ai market size was estimated at USD 7.79 billion in 2024 and is projected to reach USD 21.06 billion by 2030, growing at a CAGR of 18.0% from 2025 to 2030. One of the key drivers behind this rapid expansion is the increasing demand for AI solutions capable of processing and interpreting multiple types of data—ranging from images and textual information to numerical and even complex genomic data.

The integration of multimodal data allows AI systems to combine these diverse data inputs into a cohesive and unified representation. This capability provides a more comprehensive and nuanced understanding of complex problems, which is especially critical in sectors like healthcare. In such fields, effective decision-making often relies on synthesizing information from multiple sources, making multimodal AI solutions invaluable. This growing trend is fueling the expansion of the explainable AI market, as users not only seek powerful AI tools but also require transparency and interpretability in decision-support applications. A notable example of this advancement occurred in May 2024, when Fujitsu developed an explainable AI system that integrates multimodal data—such as text, images, and numerical values—into knowledge graphs. This system is designed to aid clinical decision-making in genomic medicine and cancer treatment, demonstrating the practical applications of such technologies in high-stakes environments.

At the same time, as AI models, particularly those based on deep learning, grow increasingly complex, understanding how they arrive at specific decisions has become more challenging. This complexity creates a significant gap between the model’s predictive performance and the trust users place in these systems. To address this, developers are demanding advanced tools that can clarify the inner workings of AI models by identifying the key features influencing predictions. This level of transparency is essential not only to foster user trust but also to ensure fairness, accountability, and regulatory compliance. These requirements have led to a surge in demand for explainable AI frameworks that enable better model interpretability.

Explainability also plays a crucial role in supporting the debugging and validation of AI models in real-world deployments, ensuring their reliability and effectiveness. Overall, the ability to provide clear explanations about AI decision-making processes strengthens confidence in AI systems, particularly in sensitive and highly regulated industries, thereby driving further adoption of explainable AI technologies across the globe.

Key Market Trends & Insights:

• In 2024, North America emerged as the leading region in the explainable AI market, capturing a significant share of 40.7% of the total market revenue. This dominance is largely attributed to the presence of advanced technological infrastructure, substantial investments in AI research, and a vibrant ecosystem of innovation hubs. Within this region, the United States stands out as a major catalyst for growth in the explainable AI industry, driven by its strong focus on AI development, world-class research institutions, and a supportive environment for startups and technology companies specializing in artificial intelligence.

• When analyzing the market based on components, the solution segment led the industry with the largest revenue share, accounting for 81.2% of the market in 2024. This reflects the widespread adoption of comprehensive explainable AI solutions by enterprises looking to enhance transparency, interpretability, and trustworthiness in their AI-driven decision-making processes.

• In terms of deployment models, the on-premises segment held the largest share of revenue during the same year. This preference can be linked to organizations’ demands for enhanced security, data privacy, and control over sensitive information, which are critical considerations when implementing AI solutions in sectors such as healthcare, finance, and government.

• Looking at market applications, the fraud and anomaly detection segment captured the largest revenue share in 2024. This highlights the critical role explainable AI plays in identifying suspicious activities and preventing financial crimes, cyberattacks, and other irregularities. By providing transparent and interpretable insights into AI predictions, organizations can better understand the factors driving anomalies and make informed decisions to mitigate risks effectively.

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Market Size & Forecast:

• 2024 Market Size: USD 7.79 Billion

• 2030 Projected Market Size: USD 21.06 Billion

• CAGR (2025-2030): 18.0%

• North America: Largest market in 2024

Key Companies & Market Share Insights:

Some of the prominent companies operating in the explainable AI market include Amelia US LLC, BuildGroup, Factmata, Google LLC, IBM Corporation, Kyndi, and Microsoft. These organizations are actively working to expand their customer base as a strategic approach to gain a stronger competitive advantage within the rapidly evolving industry. To achieve this, key players are engaging in a variety of strategic initiatives, including mergers and acquisitions as well as forming partnerships with other major companies. These collaborations and consolidations help them enhance their technological capabilities, broaden their market reach, and accelerate innovation in explainable AI solutions.

Google LLC, one of the leading technology giants in this space, has developed several advanced tools designed to improve users’ understanding of how AI models arrive at their decisions. Notable examples include the What-If Tool and TCAV (Testing with Concept Activation Vectors). These tools provide users with the ability to visualize data and explain model predictions in an intuitive manner, thereby enhancing transparency. Moreover, Google integrates these explainability features into its broader AI platforms to promote fairness and accountability. The company’s ongoing research efforts are focused on making AI systems more interpretable and accessible across a wide spectrum of applications, ranging from healthcare to finance, ensuring that stakeholders can trust and effectively use AI-driven insights.

IBM Corporation is another key player, offering AI Explainability 360, an open-source toolkit that encompasses a comprehensive suite of algorithms aimed at interpreting and explaining AI models. IBM’s approach centers on building trustworthy AI systems by enabling users to better understand the behavior of AI models, identify and mitigate potential biases, and ensure compliance with regulatory requirements through transparent AI operations. Additionally, IBM continues to foster collaboration with academic institutions and industry partners to develop and promote standards and best practices in the field of explainable AI, contributing to the advancement and wider adoption of reliable AI technologies.

Key Players

• Amelia US LLC

• BuildGroup

• DataRobot, Inc.

• Ditto.ai

• DarwinAI

• Factmata

• Google LLC

• IBM Corporation

• Kyndi

• Microsoft

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Conclusion:

The explainable AI market is poised for significant growth driven by increasing demand for transparent and interpretable AI systems across various industries. Key factors fueling this growth include the rising complexity of AI models, the need for regulatory compliance, and the emphasis on fairness and accountability in AI-driven decisions. Innovations in multimodal data integration and advanced explainability tools are enhancing AI adoption in critical sectors such as healthcare and finance. As organizations prioritize trust and transparency in AI applications, the market is expected to continue expanding rapidly worldwide.