Explainable AI Market (By Component; By Deployment; By Application: Fraud & Anomaly Detection, Drug Discovery & Diagnostics, Predictive Maintenance) - Global Industry Analysis, Size, Share, Growth, Trends, Revenue, Regional Outlook and Forecast 2023-2032

The global explainable AI market was valued at USD 5.53 billion in 2022 and it is predicted to surpass around USD 28.99 billion by 2032 with a CAGR of 18.02% from 2023 to 2032. The explainable AI market in the United States was accounted for USD 1.7 billion in 2022.

Key Pointers

• North America dominated the market with a share of 41% in 2022 and is projected to grow at a CAGR of 13.43% over the forecast period. 
• Asia Pacific is anticipated to grow at the fastest CAGR of 24.84% during the forecast period. 
• The solution segment accounted for the largest revenue share of 83 % in 2022 
• The on-premises segment held the largest revenue share of 56% in 2022. 
• The fraud and anomaly detection segment accounted for the largest revenue share of 24% in 2022.
•  IT & telecommunication sector accounted for the highest revenue share of 18% in 2022.

Report Scope of the Explainable AI Market

Explainable AI (XAI) refers to the development and deployment of artificial intelligence systems that can provide human-interpretable explanations for their decision-making processes. While traditional AI models like deep neural networks can achieve high accuracy in tasks such as image recognition or natural language processing, they often lack transparency and can be considered "black boxes" due to their complex internal workings.

XAI is used across various industries which is fueling the growth of the market. For instance, XAI, used in medical diagnosis and treatment recommendation systems, helps doctors and healthcare professionals understand and trust the decisions made by AI algorithms. Explainable models provide explanations for diagnoses, suggest potential treatments, and highlight relevant medical factors, enabling healthcare practitioners to make more informed decisions.

Emerging technological advancements and the development of AI in North America are propelling the growth of the XAI market. Regulatory bodies in the U.S., including the Federal Trade Commission (FTC) and the Food and Drug Administration (FDA), have shown an increasing interest in the transparency and explainability of AI systems. They have emphasized the need for companies to provide clear explanations for AI-driven decisions, particularly in areas like consumer protection, algorithmic fairness, and healthcare. These regulatory considerations encourage the development and adoption of XAI practices to ensure compliance and mitigate potential risks associated with non-transparent AI systems in this region.

Explainable AI has many advantages, including improved inventory management and greater retention of clients through playing a significant role in advancing customer retention strategies by providing transparency, building trust, and improving customer satisfaction. A key to maximizing an AI model's performance is knowing its shortcoming. It is simpler to improve AI models when we better understand why they failed.

Explainable AI is an effective tool for finding system problems and eliminating biases in the data and increasing user confidence. Explainable AI helps validate estimates so that AI models can be improved, and novel insights can be gained to address the issue. For instance, in medical diagnosis, XAI provides explanations for the predictions made by AI systems, allowing medical professionals to validate the accuracy of the diagnoses and identify potential errors or biases.

Component Insights

Based on components, the market is bifurcated into solutions and services. The solution segment accounted for the largest revenue share of 83 % in 2022 and is expected to continue its dominance over the forecast period. The growth of the solution segment can be attributed to factors such as the growing complexities of AI models, lack of standardization, fraudulent activities, and others.

Fraud detection is a major application area of explainable AI where it helps predict fraudulent attacks and define which attack has a higher threat. Cybersecurity is a growing concern for businesses and governments. Vendors of cybersecurity solutions are increasingly using AI and explaining an AI algorithm's decisions brings several benefits, including greater confidence in the system and a better understanding of its operation. Explainable AI solutions are being applied in several areas of cybersecurity, fueling the market growth.

Explainable AI (XAI) consulting services specialize in helping organizations adopt and implement AI solutions that are transparent, interpretable, and accountable. These services focus on ensuring that AI models and systems can explain their decisions and behaviors, enhancing trust, understanding, and compliance, which is driving the market growth. XAI consultants work with organizations to develop a strategy and roadmap for incorporating explainability into their AI initiatives.

Innovative offerings by key players in the market, for instance, Google Cloud, which offers Explainable AI as a service, providing tools and frameworks to enhance the interpretability of AI models, is propelling the explainable AI market. It includes features like integrated gradients and feature importance, which help users understand how to input features to contribute to model predictions.

Deployment Insights

Based on deployment, the market is segmented into cloud and on-premises. The on-premises segment held the largest revenue share of 56% in 2022. Using on-premises explainable AI can provide several benefits, such as improved data security, reduced latency, and increased control over the AI system. Additionally, it may be preferable for organizations subject to regulatory requirements limiting the use of cloud-based services.

Organizations use various techniques such as rule-based systems, decision trees, and model-based explanations to implement on-premises explainable AI. These techniques provide insights into how the AI system arrived at a particular decision or prediction, allowing users to verify the system’s reasoning and identify potential biases or errors.

Major players across various industry verticals, especially in the BFSI, retail, and government, prefer XAI deployed on-premises, owing to its security benefits. For instance, the financial services company JP Morgan uses explainable AI on-premises to improve fraud detection and prevent money laundering. The system uses machine learning to analyze large volumes of data, identify potentially fraudulent activities, and provide clear and transparent explanations for its decisions.

Similarly, IBM, the technology company, provides an on-premises explainable AI platform termed Watson OpenScale, which helps organizations manage and monitor the performance and transparency of their AI systems. The platform provides clear explanations for AI decisions and predictions and allows organizations to track and analyze the data used to train their AI models.

Application Insights

Based on application, the market is segmented into fraud and anomaly detection, drug discovery & diagnostics, predictive maintenance, supply chain management, identity and access management, and others. Artificial intelligence (AI) plays a crucial role in fraud management.  The fraud and anomaly detection segment accounted for the largest revenue share of 24% in 2022.

Machine Learning (ML) algorithms, a component of AI, can examine enormous amounts of data to identify trends and anomalies that could indicate fraudulent activity. Systems for managing fraud powered by AI can detect and stop various frauds, including financial fraud, identity theft, and phishing attempts. They can also change and pick up on new fraud patterns and trends, thereby increasing their detection.

The prominent use of XAI in manufacturing with predictive maintenance is propelling the market growth. XAI predictive analysis in manufacturing involves using interpretable AI models to make predictions and generate insights in the manufacturing industry. Explainable AI techniques are used to develop models that predict equipment failures or maintenance needs in manufacturing plants. By analyzing historical sensor data, maintenance logs, and other relevant information, XAI models identify the key factors contributing to equipment failures and provide interpretable explanations for the predicted maintenance requirements.

Moreover, explainable AI models leverage predictive analysis in quality control processes. By analyzing production data, sensor readings, and other relevant parameters, XAI models can predict the likelihood of defects or deviations in manufacturing processes. The models can also provide explanations for the factors contributing to quality issues, helping manufacturers understand the root causes and take corrective actions.

End-use Insights

Based on end-use, the market is segmented into healthcare, BFSI, aerospace & defense, retail and e-commerce, public sector & utilities, it & telecommunication, automotive, and others. IT & telecommunication sector accounted for the highest revenue share of 18% in 2022. The rollout of 5G and the Internet of Things (IoT) is enabling organizations and individuals to collect more real-world data in real time. Artificial intelligence (AI) systems can use this data to become increasingly sophisticated and capable.

Mobile carriers can enhance connectivity and their customers' experiences thanks to AI in the telecom sector. Mobile operators can offer better services and enable more people to connect by utilizing AI to optimize and automate networks. For instance, While AT&T anticipates and prevents network service interruptions by utilizing predictive models that use AI and statistical algorithms, Telenor uses advanced data analytics to lower energy usage and CO2 emissions in its radio networks. AI systems can also support more personalized and meaningful interactions with customers.

Explainable AI in BFSI is anticipated to give financial organizations a competitive edge by increasing their productivity and lowering costs while raising the quality of the services and goods they provide to customers. These competitive advantages can subsequently benefit financial consumers by delivering higher-quality and more individualized products, releasing data insights to guide investment strategies, and enhancing financial inclusion by enabling the creditworthiness analysis of customers with little credit history. These factors are anticipated to augment the market growth.

Regional Insights

North America dominated the market with a share of 41% in 2022 and is projected to grow at a CAGR of 13.43% over the forecast period. Strong IT infrastructure in developed nations such as Germany, France, the U.S., the UK, Japan, and Canada is a major factor supporting the growth of the explainable AI market in these countries.

Another factor driving the market expansion of explainable AI in these countries is the substantial assistance from the governments of these nations to update the IT infrastructure. However, developing nations like India and China are expected to display higher growth during the forecast period. Numerous investments that are appropriate for the expansion of the explainable AI business are drawn to these nations by their favorable economic growth.

Asia Pacific is anticipated to grow at the fastest CAGR of 24.84% during the forecast period. Significant advancements in technology in Asia Pacific countries are driving market growth. For instance, in February 2021, a new system built on the 'explainable AI' principle was developed by Fujitsu Laboratories and Hokkaido University in Japan. It automatically shows users the steps they need to do to obtain a desired result based on AI results about data, such as those from medical exams.

Explainable AI Market Segmentations:

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

By Regional 

• North America
  • U.S.
  • Canada
• Europe
  • U.K.
  • Germany
  • France
• Asia Pacific
  • China
  • India
  • Japan
  • South Korea
  • Australia
• Latin America
  • Brazil
  • Mexico
• Middle East & Africa
  • Saudi Arabia
  • UAE
  • South Africa