Explainable AI Market Recent Trends, Outlook, Size, Share, Top Companies, Industry Analysis, Future Development & Forecast - 2028

Explainable AI Market by Offering (Solutions & Services), Software Type (Standalone Software, Integrated Software, Automated Reporting Tools, Interactive Model Visualization), Methods, Vertical and Region - Global Forecast to 2028.
The market for explainable AI [https://www.marketsandmarkets.com/Market-Reports/explainable-ai-market-47650132.html?utm_campaign=explainableaimarket&utm_source=abnewswire.com&utm_medium=paidpr] is projected to expand from USD 6.2 billion in 2023 to USD 16.2 billion by 2028, reflecting a compound annual growth rate (CAGR) of 20.9% over the forecast period. This growth is largely fueled by rising regulatory pressures and the growing need for transparency and accountability in AI systems, as organizations strive to comply with regulations such as GDPR and HIPAA.

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As per verticals, the healthcare & life sciences segment is to grow at the highest CAGR during the forecast period.

The healthcare & life sciences vertical is expected to grow at the highest CAGR during the forecast period. The healthcare and life sciences industry stands at the forefront of leveraging Explainable AI to enhance decision-making processes and improve patient outcomes. In this vertical, Explainable AI technologies are employed to make the predictions and recommendations generated by AI models more transparent and interpretable for clinicians, researchers, and regulatory bodies. As the application of AI in healthcare becomes increasingly widespread, ensuring that AI-driven diagnostics, treatment recommendations, and drug discovery processes are explainable is crucial to build trust and mitigate risks.

By Solutions by type, Software toolkits and frameworks Segment to grow at the largest market size during the forecast period.

Software toolkits and frameworks for Explainable AI are more developer-centric solutions. They offer libraries, APIs, and pre-built algorithms that data scientists and machine learning engineers can integrate into their existing machine learning workflows. These toolkits provide flexibility and customization options for those who want to implement transparency and interpretability in AI models. Toolkits provide a variety of algorithms for explaining and interpreting AI model predictions, such as feature importance, partial dependence plots, and saliency maps.

By Methods, the Model-Agnostic Methods segment is to grow at the highest CAGR during the forecast period.

Model-agnostic methods are techniques that can be applied to any machine learning model, irrespective of the underlying architecture. They are often used when a black-box model, like a deep neural network, is in place, and the goal is to provide explanations without altering the model itself. Companies utilize these methods to make AI models more interpretable and to build trust with users and stakeholders.

Asia Pacific to account to grow at the highest CAGR during the forecast period.

By region, Asia Pacific countries, especially China, South Korea, and Japan, are at the forefront of technological innovation. They are quick to adopt and adapt to emerging AI technologies, including XAI, in various industries. The APAC region is home to a significant portion of the global population, with diverse industries ranging from manufacturing to healthcare, finance, and e-commerce. This diversity creates a wide array of use cases for XAI. Governments are actively promoting AI research and development. They are also introducing regulations that require AI systems to be transparent and explainable, contributing to the growth of the explainable AI market.

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Unique Features in the Explainable AI Market

One of the most distinctive features of explainable AI is its ability to provide insights into how AI models make decisions. Unlike traditional "black-box" models, XAI offers transparency, allowing stakeholders to understand, trust, and validate AI outputs. This interpretability is especially critical in sectors like healthcare, finance, and law where decision-making must be justified and accountable.

Explainable AI is uniquely positioned to help organizations meet stringent regulatory and ethical standards. Laws such as the GDPR (General Data Protection Regulation) and HIPAA (Health Insurance Portability and Accountability Act) require that automated decisions be understandable and justifiable. XAI tools enable companies to document, monitor, and audit their AI systems to ensure they comply with these evolving legal frameworks.

XAI solutions are increasingly being tailored to specific industries. For example, in the medical field, XAI helps clinicians understand AI-generated diagnoses or treatment suggestions. In finance, it clarifies credit risk assessments or fraud detection decisions. This ability to adapt and provide relevant explanations based on context is a unique feature driving adoption across diverse sectors.

Another unique aspect of the XAI market is its compatibility with both traditional machine learning and modern deep learning models. Whether it's decision trees, neural networks, or ensemble methods, XAI frameworks can offer explanations for a wide range of algorithms. This flexibility makes it easier for organizations to integrate XAI into their existing AI ecosystems.

Major Highlights of the Explainable AI Market

Government regulations and industry-specific compliance requirements are key drivers of the XAI market. Frameworks such as the GDPR, HIPAA, and emerging AI governance policies require explainability in automated decision-making processes, prompting companies to adopt XAI solutions to ensure legal and ethical compliance.

XAI is finding use in a wide array of sectors including healthcare, finance, insurance, automotive, and government. Each industry leverages XAI differently - from understanding diagnostic AI in medicine to justifying credit decisions in finance - highlighting the technology's versatility and broad applicability.

Ongoing research and development are leading to innovative tools like LIME (Local Interpretable Model-agnostic Explanations), SHAP (SHapley Additive exPlanations), and counterfactual explanations. These advancements are making it easier to integrate explainability into complex AI models, boosting adoption rates among organizations with high technical requirements.

Explainable AI plays a crucial role in the broader movement toward responsible AI development. It supports fairness, accountability, and transparency (FAT) principles by offering visibility into AI logic and helping mitigate bias, fostering greater public trust and ethical alignment in AI-powered systems.

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Top Companies in the Explainable AI Market

Some major players in the explainable AI market include Microsoft (US), IBM (US), Google (US), Salesforce (US),Intel Corporation(US), NVIDIA(US), SAS Institute(US), Alteryx(US), AWS(US), Equifax(US), FICO(US), Temenos(Switzerland),Mphasis(India), C3.AI(US), H2O.ai(US), Fiddler(US), Zest AI(US), Seldon(London), Squirro(Switzerland), Kyndi(US), DataRobot(US),Databricks(US), Tredence(US), DarwinAI(Canada), Tensor AI solutions(Germany), . EXPAI(Spain), Abzu(Denmark), Arthur(US), and Intellico(Italy.

Microsoft has a diverse portfolio of AI solutions, which includes NLP and ML-based offerings, that have gained traction across various industries. They have bolstered their presence with the introduction of Azure Machine Learning, Visual Studio Tools for AI, Cognitive Services, and enterprise AI solutions. Furthermore, Microsoft is actively engaged in creating software, services, devices, and solutions to remain competitive in the era of intelligent cloud and intelligent edge. By heavily investing in mixed reality cloud, Microsoft empowers its customers to modernize their business processes. The company provides a comprehensive suite of cloud-based solutions that encompass software, platforms, and content. This encompasses a range of products, including operating systems, productivity applications that work seamlessly across devices, server applications, business solutions, desktop and server management tools, software development tools, and a library of video games. Microsoft has positioned itself at the forefront of the rapidly evolving landscape of explainable Artificial Intelligence (XAI). XAI, which aims to make AI models and their decision-making processes more transparent and interpretable, is a pivotal component of Microsoft's broader AI strategy. Microsoft leverages XAI to enhance trust and accountability in AI-driven solutions across various industries.

IBM is a globally renowned technology and consulting company with a long-standing history of innovation and leadership in the IT industry. With a strong presence across multiple business segments, IBM offers a diverse range of products, services, and solutions to cater to the needs of enterprises worldwide. IBM caters to a wide range of industries, encompassing aerospace and defense, government, manufacturing, healthcare, oil & gas, automotive, electronics, insurance, retail & consumer goods, banking & finance, life sciences, telecommunications, media & entertainment, chemicals, and numerous others. Operating in over 175 countries, IBM maintains a substantial presence across the Americas, Europe, the Middle East & Africa, as well as the Asia Pacific region. It has become the favored platform for all corporate applications. IBM focuses on advanced technologies like artificial intelligence (AI), data analytics, and cloud platforms. The flagship offering within this segment is Watson, an AI platform renowned for its natural language processing and machine learning capabilities. Watson enables organizations to analyze vast amounts of data, derive valuable insights, and enhance decision-making processes across various industries. IBM has made significant strides in the field of explainable Artificial Intelligence (XAI). XAI is a critical component of AI systems, ensuring transparency and interpretability in AI models and decision-making processes. IBM's commitment to XAI is evident through its focus on creating AI solutions that not only deliver high-performance results but also offer clear explanations for the decisions they make.

Temenos Group AG, headquartered in Geneva, Switzerland, is a prominent provider of banking software and financial technology solutions. While primarily focused on banking software, Temenos has also been incorporating artificial intelligence (AI) capabilities into its offerings. However, there is limited specific information available regarding their involvement in Explainable AI (XAI) compared to other global leaders in AI. Generally, Temenos aims to empower banks with advanced analytics and AI-driven insights to enhance operational efficiency, customer experience, and risk management within the financial services industry.

Seldon Technologies, based in London, specializes in machine learning deployment and operations, particularly in the realm of Explainable AI (XAI). The company focuses on enabling organizations to deploy and manage machine learning models at scale, emphasizing transparency and interpretability. Seldon provides tools and frameworks that enhance the explainability of AI models, allowing users to understand and trust AI-driven decisions. Their solutions are designed to support various industries, including finance, healthcare, and telecommunications, aiming to bridge the gap between complex AI systems and human comprehension.

Squirro, headquartered in Zurich, Switzerland, specializes in augmented intelligence and data insights solutions, including Explainable AI (XAI). The company offers AI-driven platforms that provide contextual insights from unstructured data sources. Squirro's XAI capabilities focus on enhancing transparency and interpretability of AI algorithms, enabling organizations to understand the reasoning behind AI-driven decisions. Their solutions are utilized across industries such as financial services, insurance, and manufacturing, helping businesses leverage data effectively while maintaining compliance and trust.

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