Explainable AI market set to surge to $16.2bn by 2028 driven by regulatory demand and healthcare growth

The global market for explainable artificial intelligence (XAI) is projected to experience significant growth, expanding from an estimated USD 6.2 billion in 2023 to USD 16.2 billion by 2028. This growth corresponds to a compound annual growth rate (CAGR) of 20.9% over the forecast period, driven largely by increasing regulatory pressures and the growing demand for transparency and accountability in AI systems across various sectors.

Explainable AI aims to provide transparency in AI decision-making processes, which are traditionally opaque in black-box models. XAI enables stakeholders to comprehend how AI models reach their conclusions, a feature critical to ensuring trust, validation, and compliance, particularly in highly regulated industries.

Key drivers of market expansion include evolving regulatory frameworks such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA), which require automated decisions to be explainable and justifiable. Organisations are therefore adopting XAI solutions to meet these legal and ethical standards, helping them document, monitor, and audit AI systems effectively.

Among industry verticals, healthcare and life sciences are expected to see the highest growth rate during this period. This sector utilises XAI to enhance clinical decision-making, improve patient outcomes, and assure regulatory bodies of the transparency in AI-driven diagnostics, treatment recommendations, and drug discovery processes.

In terms of solutions, software toolkits and frameworks represent the largest segment by market size. These developer-centric toolkits provide APIs, libraries, and algorithms—such as feature importance analysis, partial dependence plots, and saliency maps—that enable data scientists to integrate explainability into existing machine learning workflows with flexibility and customisation.

Among methods used in XAI, model-agnostic techniques are forecast to grow the fastest. These methods operate independently of any specific AI model architecture, allowing explanations to be generated for any black-box models such as deep neural networks without modifying the underlying algorithms. This versatility is instrumental in enhancing model interpretability and fostering trust among users and stakeholders.

Geographically, the Asia-Pacific region is anticipated to register the highest CAGR. Countries such as China, South Korea, and Japan are prominent for their technological innovation and rapid adoption of AI across diverse sectors including manufacturing, healthcare, finance, and e-commerce. Supportive governmental policies and regulatory frameworks in these countries further stimulate the integration of XAI technologies.

Several leading companies dominate the explainable AI market across various regions. Major US-based players include Microsoft, IBM, Google, Salesforce, Intel Corporation, NVIDIA, SAS Institute, Alteryx, Amazon Web Services (AWS), Equifax, FICO, C3.AI, H2O.ai, Fiddler, and Zest AI. Other notable technology firms include Temenos (Switzerland), Mphasis (India), 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 established a strong presence with a diverse AI portfolio encompassing natural language processing, machine learning, and enterprise AI solutions available through Azure Machine Learning and Cognitive Services. They focus on integrating XAI to strengthen trust and accountability in AI-powered applications.

IBM, with operations in over 175 countries, stresses advanced AI technologies through its Watson platform, which offers natural language processing and machine learning capabilities. IBM prioritises explainability in AI to ensure transparent and accountable decision-making across industries including aerospace, healthcare, government, finance, and telecommunications.

Temenos Group AG, based in Geneva, focuses primarily on banking software with integrated AI capabilities intended to enhance analytics and risk management in financial services, though its specific role in XAI remains less defined compared to other industry leaders.

London-based Seldon Technologies specialises in the deployment and operation of machine learning models with an emphasis on explainability. Their solutions assist sectors such as finance, healthcare, and telecommunications to understand AI-driven decisions.

Zurich-headquartered Squirro, offering augmented intelligence and data insight platforms, focuses on providing contextual explanations to enhance transparency in AI systems for industries like financial services, insurance, and manufacturing.

Innovative tools and approaches such as Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), and counterfactual explanations are advancing the field, enabling more accessible integration of explainability even in complex AI models. These developments support the broader agenda of responsible AI, promoting fairness, accountability, and transparency.

The expanding array of XAI applications across sectors—from healthcare diagnostics and treatment to financial risk assessments and fraud detection—demonstrates the technology’s versatility and critical role in modern AI deployment. The increasing regulatory demands and industry-specific needs are expected to sustain the market's robust growth through 2028.

The openPR.com is reporting on these trends and the comprehensive industry landscape shaping the future of explainable artificial intelligence.

Source: Noah Wire Services