Explainable AI Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component (Solution, Services), By Deployment (Cloud, On-Premises), By Application (Fraud & 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-comm
Published Date: November - 2024 | Publisher: MIR | No of Pages: 320 | Industry: ICT | Format: Report available in PDF / Excel Format
View Details Buy Now 2890 Download Sample Ask for Discount Request CustomizationExplainable AI Market – Global Industry Size, Share, Trends, Opportunity, and Forecast, Segmented By Component (Solution, Services), By Deployment (Cloud, On-Premises), By Application (Fraud & 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-comm
| Forecast Period | 2024-2028 |
| Market Size (2022) | USD 5.4 Billion |
| CAGR (2023-2028) | 22.4% |
| Fastest Growing Segment | Cloud |
| Largest Market | North America |
Market Overview
Global Explainable AI Market has valued at USD 5.4 Billion in 2022 and is anticipated to project robust growth in the forecast period with a CAGR of 22.4% through 2028. The Global Explainable AI (XAI) Market is experiencing significant growth as organizations increasingly adopt artificial intelligence solutions across various industries. XAI refers to the capability of AI systems to provide understandable and interpretable explanations for their decisions and actions, addressing the "black box" challenge of traditional AI. The market is poised for expansion, driven by the growing need for transparency, accountability, and ethical AI deployment. XAI is vital in sectors such as finance, healthcare, and autonomous vehicles, where the ability to understand AI-generated decisions is crucial for regulatory compliance and user trust. Additionally, the rise of AI-related regulations and guidelines further propels the demand for XAI solutions. The market is characterized by innovations in machine learning techniques, algorithms, and model architectures that enhance the interpretability of AI systems. As businesses prioritize responsible AI practices, the Explainable AI Market is set to continue its growth trajectory, offering solutions that not only deliver AI-driven insights but also ensure transparency and human-centric AI decision-making processes.
Key Market Drivers
Transparency in Decision-Making
The Global Explainable AI (XAI) Market is witnessing significant growth as a result of the growing demand for transparency and interpretability in artificial intelligence (AI) systems. XAI plays a crucial role in various sectors, including healthcare, finance, and autonomous vehicles, where comprehending the decisions made by AI systems is vital for regulatory compliance and user trust. With the increasing adoption of AI, there is a corresponding need to unravel the complexities of AI models and algorithms, making XAI solutions increasingly indispensable. The market thrives on continuous innovations in machine learning techniques and algorithms that enhance the interpretability of AI systems, ensuring that organizations can leverage the power of AI while upholding accountability and ethical AI practices.
The rising demand for transparency and interpretability in AI systems is a key driver behind the robust growth of the Global XAI Market. As AI becomes more prevalent in various industries, there is a growing need to understand the decision-making processes of AI systems. This is particularly crucial in sectors such as healthcare, where AI is used to make critical diagnoses and treatment recommendations. By providing explanations for AI-driven decisions, XAI enables healthcare professionals to trust and validate the outcomes, ensuring regulatory compliance and patient safety. Similarly, in the finance sector, where AI is employed for tasks like fraud detection and risk assessment, XAI plays a pivotal role in ensuring transparency and accountability. Financial institutions need to understand the reasoning behind AI-driven decisions to comply with regulations and maintain customer trust. XAI solutions provide insights into the inner workings of AI models, enabling organizations to explain and justify their decisions to regulators, auditors, and customers.
Autonomous vehicles are another area where XAI is of utmost importance. As self-driving cars become more prevalent, it is crucial to understand the decision-making processes of AI algorithms that control these vehicles. XAI allows manufacturers and regulators to comprehend the reasoning behind AI-driven actions, ensuring safety, reliability, and compliance with regulations. The continuous advancements in machine learning techniques and algorithms are driving the growth of the XAI market. Researchers and developers are constantly working on innovative approaches to enhance the interpretability of AI systems. These advancements include techniques such as rule extraction, feature importance analysis, and model-agnostic explanations. By making AI models more transparent and understandable, organizations can address concerns related to bias, fairness, and accountability, fostering trust and ethical AI practices.
Regulatory Compliance
The global market for Explainable Artificial Intelligence (XAI) is experiencing significant growth due to the increasing number of regulations and guidelines related to AI. Governments and industry watchdogs are placing a strong emphasis on ethical AI practices, which is compelling organizations to adopt XAI solutions to meet compliance requirements. As regulatory frameworks continue to evolve, XAI plays a crucial role in helping organizations ensure that their AI systems adhere to legal and ethical standards. This growing demand for XAI, driven by regulatory requirements, is particularly prominent in industries where data privacy, fairness, and accountability are of utmost importance. The surge in AI-related regulations and guidelines worldwide has created a favorable environment for the XAI market to thrive. Governments and regulatory bodies are recognizing the potential risks associated with AI systems that lack transparency and interpretability. As a result, they are implementing measures to ensure that AI technologies are developed and deployed responsibly. These regulations often require organizations to provide explanations for the decisions made by their AI systems, especially in critical domains such as healthcare, finance, and criminal justice. By adopting XAI solutions, organizations can address these regulatory requirements and demonstrate their commitment to ethical AI practices. XAI enables organizations to understand and explain the reasoning behind AI-generated decisions, making the decision-making process more transparent and accountable. This not only helps organizations comply with regulations but also fosters trust among stakeholders, including customers, employees, and the public.
Industries that handle sensitive data, such as healthcare and finance, are particularly reliant on XAI to ensure data privacy and fairness. XAI techniques allow organizations to identify and mitigate biases in AI models, ensuring that decisions are not influenced by factors such as race, gender, or socioeconomic status. Moreover, XAI enables organizations to detect and rectify any unintended consequences or errors in AI systems, thereby minimizing potential harm to individuals or society. As the regulatory landscape continues to evolve, the demand for XAI is expected to grow further. Organizations across various sectors are recognizing the importance of aligning their AI systems with legal and ethical standards. By embracing XAI, these organizations can not only meet compliance requirements but also gain a competitive edge by demonstrating their commitment to responsible AI practices. The XAI market is poised for significant expansion as more industries prioritize transparency, fairness, and accountability in their AI deployments.
Improved Decision Support
XAI, or Explainable Artificial Intelligence, is a powerful tool that enables businesses and professionals to enhance their decision-making processes by offering clear and understandable explanations for insights generated by AI systems. This technology has proven particularly valuable in sectors such as healthcare and finance, where it assists clinicians, analysts, and decision-makers in comprehending and utilizing AI-driven information effectively. In the healthcare industry, XAI plays a crucial role in supporting clinicians in understanding AI-generated diagnoses and treatment recommendations. By providing comprehensible explanations for the insights produced by AI models, XAI helps healthcare professionals gain a deeper understanding of the reasoning behind these recommendations. This, in turn, leads to improved patient care as clinicians can make more informed decisions based on the AI-driven insights. XAI acts as a bridge between the complex algorithms used in AI systems and the human decision-makers, empowering healthcare professionals to trust and utilize AI technology to its fullest potential. Similarly, in the financial sector, XAI serves as a valuable tool for analysts and decision-makers. With the increasing adoption of AI-driven investment strategies, XAI aids in comprehending the reasoning behind these strategies. By providing transparent and interpretable explanations, XAI enables financial professionals to have a clear understanding of the insights generated by AI models. This empowers them to make better-informed decisions regarding investments, risk management, and overall portfolio management. The use of XAI in financial institutions helps bridge the gap between the complexity of AI models and the need for human decision-makers to have a clear understanding of the underlying rationale.
The market for XAI is experiencing significant growth due to the recognition of its value as a decision-support tool. As businesses and professionals increasingly understand the importance of comprehensible explanations for AI-generated insights, the demand for XAI continues to rise. XAI's ability to bridge the gap between complex AI models and human decision-makers is seen as a crucial factor in unlocking the full potential of AI technology across various industries. By empowering businesses and professionals to make better-informed decisions, XAI is driving positive change and improving outcomes in sectors such as healthcare and finance.
Enhanced User Trust
The increasing integration of AI into our everyday lives highlights the crucial importance of establishing user trust in AI systems. One approach to fostering this trust is through the adoption of Explainable AI (XAI), which aims to make AI systems transparent and explainable, thereby dispelling concerns associated with the "black box" nature of AI. This aspect of XAI is particularly vital in sectors such as autonomous vehicles and critical infrastructure, where safety and reliability are of utmost importance. As a result, organizations are recognizing the significance of XAI in bolstering user confidence in AI technologies, leading to a significant expansion of the market.
In an era where AI is becoming increasingly pervasive, users are understandably concerned about the inner workings of AI systems. The traditional "black box" nature of AI, where decisions are made without clear explanations, has raised questions about the reliability, fairness, and accountability of these systems. XAI addresses these concerns by providing insights into how AI systems arrive at their decisions, making the decision-making process more transparent and understandable to users. In sectors like autonomous vehicles, where AI plays a crucial role in ensuring safe and efficient transportation, user trust is paramount. The ability to explain the reasoning behind AI-driven decisions can help alleviate concerns related to accidents or malfunctions. By providing clear explanations, XAI enables users to understand why a particular decision was made, increasing their confidence in the technology, and fostering trust.
Similarly, in critical infrastructure sectors such as energy, healthcare, and finance, where AI systems are relied upon for making important decisions, XAI can play a vital role in ensuring the safety and reliability of these systems. By making AI systems explainable, organizations can address concerns related to biases, errors, or malicious attacks, thereby enhancing user trust and confidence in the technology. Recognizing the significance of user trust in AI systems, organizations are investing in XAI to bolster confidence in AI technologies. This investment is driven by the understanding that user trust is a key driver for market expansion. By adopting XAI, organizations can differentiate themselves by offering transparent and explainable AI systems, which in turn can attract more users and customers.
Key Market Challenges
Limited Understanding of Explainable AI
One of the primary challenges facing the global explainable AI market is the limited understanding and awareness among organizations regarding the importance and benefits of adopting explainable AI solutions. Many businesses may not fully grasp the significance of explaining ability in AI models and the potential risks associated with black-box algorithms. This lack of awareness can lead to hesitation in investing in explainable AI, leaving organizations vulnerable to issues such as biased decision-making, lack of transparency, and regulatory compliance concerns. Addressing this challenge requires comprehensive educational initiatives to highlight the critical role that explainable AI plays in building trust, ensuring fairness, and enabling interpretability in AI systems. Organizations need to recognize that explainable AI can provide insights into how AI models make decisions, enhance accountability, and facilitate better decision-making processes. Real-world examples and case studies showcasing the tangible benefits of explainable AI can help foster a deeper understanding of its significance.
Complexity of Implementation and Integration
The implementation and integration of explainable AI solutions can pose complex challenges for organizations, particularly those with limited technical expertise or resources. Configuring and deploying explainable AI models effectively, and integrating them with existing AI systems and workflows, can be technically demanding. Compatibility issues may arise during integration, leading to delays and suboptimal performance. To address these challenges, it is crucial to simplify the deployment and management of explainable AI solutions. User-friendly interfaces and intuitive configuration options should be provided to streamline setup and customization. Additionally, organizations should have access to comprehensive support and guidance, including documentation, tutorials, and technical experts who can assist with integration and troubleshoot any issues. Simplifying these aspects of explainable AI implementation can lead to more efficient processes and improved model interpretability.
Balancing Explain ability and Performance.
Explainable AI models aim to provide transparency and interpretability, but they face the challenge of striking the right balance between explain ability and performance. Highly interpretable models may sacrifice predictive accuracy, while complex models may lack interpretability. Organizations need to find the optimal trade-off between model explain ability and performance to ensure that AI systems are both trustworthy and effective. This challenge requires ongoing research and development efforts to improve the interpretability of AI models without compromising their performance. Advanced techniques, such as model-agnostic approaches and post-hoc interpretability methods, can help address this challenge by providing insights into model behavior and decision-making processes. Striving for continuous improvement in these areas will enable organizations to leverage explainable AI effectively while maintaining high-performance standards.
Regulatory and Ethical Considerations
The global explainable AI market also faces challenges related to regulatory compliance and ethical considerations. As AI systems become more prevalent in critical domains such as healthcare, finance, and autonomous vehicles, there is a growing need for transparency and accountability. Regulatory frameworks are being developed to ensure that AI systems are fair, unbiased, and explainable. Organizations must navigate these evolving regulations and ensure that their explainable AI solutions comply with legal and ethical standards. This challenge requires organizations to stay updated with the latest regulatory developments and invest in robust governance frameworks to address potential biases, discrimination, and privacy concerns. Collaboration between industry stakeholders, policymakers, and researchers is essential to establish guidelines and standards that promote responsible and ethical use of explainable AI.
Key Market Trends
Rise in Demand for Explainable AI Solutions
The global market for Explainable AI (XAI) is witnessing a surge in demand as organizations recognize the importance of transparency and interpretability in AI systems. With the increasing adoption of AI across various industries, there is a growing need to understand how AI algorithms make decisions and provide explanations for their outputs. This demand is driven by regulatory requirements, ethical considerations, and the need to build trust with end-users.
Explainable AI solutions aim to address the "black box" problem by providing insights into the decision-making process of AI models. These solutions utilize techniques such as rule-based systems, model-agnostic approaches, and interpretable machine learning algorithms to generate explanations that can be easily understood by humans. By providing clear explanations, organizations can gain valuable insights into the factors influencing AI decisions, identify potential biases, and ensure fairness and accountability in AI systems.
Shift towards Industry-Specific Explainable AI Solutions
The global market is experiencing a shift towards industry-specific Explainable AI solutions. As different industries have unique requirements and challenges, there is a need for tailored XAI solutions that can address specific use cases effectively. Organizations are seeking XAI solutions that can provide explanations relevant to their industry domain, such as healthcare, finance, or manufacturing.
Industry-specific XAI solutions leverage domain knowledge and contextual information to generate explanations that are meaningful and actionable for end-users. These solutions enable organizations to gain deeper insights into AI decision-making processes within their specific industry context, leading to improved trust, better decision-making, and enhanced regulatory compliance.
Integration of Human-AI Collaboration
The integration of human-AI collaboration is a significant trend in the global Explainable AI market. Rather than replacing humans, XAI solutions aim to augment human decision-making by providing interpretable insights and explanations. This collaboration between humans and AI systems enables users to understand the reasoning behind AI outputs and make informed decisions based on those explanations.
Explainable AI solutions facilitate human-AI collaboration by presenting explanations in a user-friendly manner, using visualizations, natural language explanations, or interactive interfaces. This allows users to interact with AI systems, ask questions, and explore different scenarios to gain a deeper understanding of AI-generated outputs. By fostering collaboration, organizations can leverage the strengths of both humans and AI systems, leading to more reliable and trustworthy decision-making processes.
Segmental Insights
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 17.99% 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.
Deployment
Based on deployment, the market is segmented into cloud and on-premises. The on-premises segment held the largest revenue share of 55.73% 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 23.86% 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.
Regional Insights
North America dominated the market with a share of 40.52% in 2022 and is projected to grow at a CAGR of 13.4% 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 24.8% 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.
Recent Developments
- In March 2023, GyanAI released itsinitial explainable Language Model and Research Engine. Based on its uniquetechnology, Gyan fulfills the promise of explainable AI by using a model thatunderstands "meaning" as similarly as possible to how people have doneit. Gyan is entirely explicable, and a user may follow the trail of its outputto its origin. It can explain its results. Gyan produces a mixture ofgenerative and extractive writing.
- In March 2023, Amelia, an AI firm,partnered with Monroe Capital and BuildGroup, which will provide financialcapital and business leadership. The strategic alliance speeds up marketacceptance of Amelia's AI products. BuildGroup, an Austin-based company thatcreates high-growth digital companies, and Monroe Capital, an elite boutiqueasset management company specializing in private credit markets, are leadingthe $175 million transaction. With this round of funding, Amelia has theresources necessary to maintain its market-leading position and intensify itscommitment to providing its clients with cutting-edge, understandable AIproducts.
Key Market Players
- Amelia US LLC
- BuildGroup
- DataRobot, Inc.
- Ditto.ai
- DarwinAI
- Factmata
- Google LLC
- IBM Corporation
- Kyndi
- Microsoft Corporation
| By Component | By Deployment | By End-use | By Application | By Region |
| Solution Services | Cloud On-premise | Healthcare BFSI Aerospace & defense Retail and e-commerce Public sector & utilities IT & telecommunication Automotive Others | Fraud & Anomaly Detection Drug Discovery & Diagnostics Predictive Maintenance Supply chain management Identity and access management Others | North America Europe Asia Pacific South America Middle East & Africa |
FAQ'S
For a single, multi and corporate client license, the report will be available in PDF format. Sample report would be given you in excel format. For more questions please contact:
Within 24 to 48 hrs.
You can contact Sales team (sales@marketinsightsresearch.com) and they will direct you on email
You can order a report by selecting payment methods, which is bank wire or online payment through any Debit/Credit card, Razor pay or PayPal.
Discounts are available.
Hard Copy