AutoML Market Set for 32.8% CAGR Through 2035 Amid Rising Demand for Scalable and Explainable AI

The global automated machine learning market is entering a transformative growth phase, driven by rapid enterprise adoption of artificial intelligence, cloud-based analytics, and the rising need to simplify complex model development processes. Valued at approximately USD 3.9 billion in 2025, the market is estimated to reach USD 5.17 billion in 2026 and is projected to surge to USD 66.4 billion by 2035, expanding at a robust CAGR of 32.8% during 2026–2035.

Market expansion is strongly supported by the accelerating democratization of AI, increasing enterprise demand for faster model deployment, and the global shortage of skilled data science professionals. Organizations across banking, healthcare, retail, manufacturing, and government sectors are increasingly adopting automated feature engineering, no-code machine learning tools, and cloud-native analytics platforms to streamline operations, enhance predictive accuracy, and reduce development timelines.

Government-backed artificial intelligence strategies and digital economy initiatives promoted by institutions such as the U.S. National Institute of Standards and Technology (NIST), the European Commission, and the OECD are further reinforcing adoption by emphasizing standardized governance, explainable AI, and scalable automation frameworks. These policies are reshaping enterprise AI development by encouraging transparent, auditable, and compliant model lifecycle management.

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Cloud-based deployment continues to dominate market share, driven by scalability, cost efficiency, and rapid system integration, while services are emerging as the fastest-growing segment as enterprises seek implementation support, workflow customization, and regulatory alignment. Large enterprises currently lead adoption, but small and medium businesses are rapidly embracing AutoML solutions due to simplified platforms and cloud-native pricing models.

Regionally, North America leads global adoption, supported by advanced cloud infrastructure and proactive AI governance frameworks, followed by Asia Pacific and Europe, where national AI missions, regulatory harmonization, and digital modernization programs are accelerating enterprise and public-sector uptake.

As enterprises prioritize cost efficiency, faster deployment cycles, and regulatory compliance, the automated machine learning market is set to become a cornerstone of next-generation analytics and enterprise AI strategies. The industry’s evolution toward integrated, end-to-end AI platforms positions AutoML as a critical enabler of scalable, responsible, and accessible artificial intelligence worldwide.

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