Explainable AI Market about to hit USD 51.29 billion by 2035

Explainable AI Market Size and Forecast 2024 to 2034

Explainable AI Market size was estimated at USD 7.85 billion in 2024 and is anticipated to grow at a CAGR of more than 15.67% from 2025 to 2035.

The explainable AI market is likely to evolve significantly, in part because of ethical and regulatory reasons. Governments and regulatory bodies all over the world are increasingly concerned with the potential risks that AI systems could pose such as bias, discrimination, and the absence of accountability. They are implementing regulations mandating AI models to be transparent and explainable to mitigate these risks.

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Explainable AI Market Trends

One of the key trends driving the market is the application of explainable AI in core business processes. Companies in various industries are recognizing the value of transparency in AI to convince stakeholders and customers. Companies can provide understandable insights into their decision-making by incorporating explainable AI into their business.

Explainable AI is applied; for example, in financial institutions to inform credit decisions and detect fraud, and in healthcare to explain proposed diagnoses & treatments. This trend not only supports regulatory compliance but also enhances client satisfaction and trust. For this reason, to enhance business operations and maintain competitiveness, more businesses are making explainable AI a priority.
The market for explainable AI is growing because of significant advances in explainability methods. In order to deliver more sophisticated and real-world techniques for interpreting complex AI models, developers and researchers are always seeking new concepts. Approaches like SHapley Additive exPlanations (SHAP), Local Interpretable Model-agnostic Explanations (LIME), and attention mechanisms are being refined and employed more.

Users will be able to better understand and believe in AI systems due to these advancements, which enable more precise & transparent explanations of its decision-making. The adoption of explainable AI solutions is also driven by the development of model-agnostic interpretability methods, which make it possible to apply them to a range of different types of AI models.

Explainable AI Market Analysis
Discover more about the major segments defining this market

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On the basis of software type, the market is segmented into model-agnostic approaches and model-specific approaches. The model-agnostic approaches segment is anticipated to exhibit a CAGR of 19.1% over the forecast period.

Model-agnostic techniques offer a flexible and versatile method for assessing and understanding the output of various AI models, hence a crucial resource in the field of explainable AI. Compared to model-specific techniques, which are tailored to work with a particular type of algorithm (e.g., neural networks/decision trees), model-agnostic techniques can be applied to any AI model, no matter its design.
Their substantial utility across diverse application scenarios is based on their universality. LIME and SHAP are two popular model-agnostic approaches. To build interpretable models that locally behave similarly to the black-box model, LIME initially disturbs the input data and subsequently observes changes in the output.
On the other hand, SHAP offers a single measure of feature importance through the application of concepts from cooperative game theory to attribute the model’s output to its input features. These methods enable users to gain insights into the decision-making of complex models, identify biases, and effectively analyze model outputs.

They are particularly useful for companies that need transparency and accountability for a variety of AI applications. Model-agnostic methods are gaining popularity in the explainable AI market due to their flexibility and broad spectrum of applications, addressing the needs of various businesses, looking for reliable and understandable AI solutions.

Market Dynamics
Drivers
Increasing human and artificial intelligence collaborative activities

Human and AI collaboration is needed to fulfill compliance and regulatory demands. Explainable AI allows companies to present justifications and proof for the choices taken by AI programs, allowing transparency and fulfillment of regulation compliance. Human collaboration within AI choice interpretation and explanation serves to satisfy legal compliance and regulatory needs. Human-AI collaboration enables continuous learning and evolution. Human professionals can impart their experience and skills to AI systems, allowing them to learn based on human inputs and evolve to respond to changing situations. The process of collaborative learning enhances the performance of the AI system as time passes. In general, the human-AI collaboration enhances the expansion of the explainable AI market through mutual strengths.

Restraints
Performance limitations

In competitive markets, organizations might value the performance of AI models over interpretability. If black-box models always perform better than explainable AI models, companies might be reluctant to implement explainable AI solutions because they fear losing a competitive advantage. There is usually a compromise between model interpretability and performance. As models get more interpretable, they tend to simplify or leave out some of the complicated relationships in the data, potentially reducing performance. Overcoming such performance constraints needs continuous research and development work in the area of explainable AI.

Opportunities
Increasing education and awareness regarding AI

Education and awareness regarding AI open up opportunities for the explainable AI market by building understanding, dispelling concerns, encouraging regulatory compliance, closing the gap between technical and non-technical stakeholders, empowering users, and stimulating research and innovation in the area of explainable AI. Education and awareness programs can motivate researchers, developers, and innovators to investigate and develop the area of explainable AI. By emphasizing the value and possible use of explainability, these projects can draw people, capital, and resources toward creating new techniques and solutions. This establishes a virtuous feedback loop, whereby more research and innovation fuel yet more growth in the explainable AI market.

Component Insights
The solution segment led the market in 2023, the segment is forecast to continue to experience a visible growth throughout the forecast period. The segment is growing due to the rising use of AI technologies across various industries. The major portion where the explainable AI operates is fraud detection. Explainable AI identifies fraud and develops thoughtful solutions for it. Cybercrime is one of the biggest problems for the government and private organizations as well, and in the technology age, it has been a challenge.

Explainable AI Market Companies

Amelia US LLC
BuildGroup
DataRobot, Inc.
Ditto.ai
DarwinAI
Factmata
Google LLC
IBM Corporation
Kyndi
Microsoft Corporation

Recent Development
In June 2023, Reprocell announced the launch of advanced commercial services ‘Pharmacology-AI’. The launch of new services was followed by the completion of the first excellerate project by the National Center for Digital Innovation.
IBM has announced the introduction of a new platform for companies ‘IBM Watsonx’ in June 2023. IBM’s new platform introduced enables organizations to speed up operations with artificial intelligence solutions. In May 2023, the Singaporean government partnered with Google Cloud, to make it easier for artificial intelligence to be available to city-state public sector agencies through the new AI cloud platform.
In May 2023, Singapore government declared collaboration with Google Cloud. The collaboration will ensure availability of artificial intelligence platforms for public sector agencies through AI cloud platforms.

Segments Included in the Report:

By Component

Solution
Services

By Deployment

Cloud
On-premises

By Application

Fraud and Anomaly Detection
Drug Discovery & Diagnostics
Predictive Maintenance
Supply Chain Management
Identity and Access Management
Others

By End-use

Healthcare
BFSI
Aerospace & Defense
Retail and e-commerce
Public Sector & Utilities
IT & Telecommunication
Automotive
Others