Automated Machine Learning Market Growth to Accelerate by 2035 Amid Data Science Talent Shortage – News and Statistics

Abstract

According to the latest IndexBox report on the global Automated Machine Learning market, the market enters 2026 with broader demand fundamentals, more disciplined procurement behavior, and a more regionally diversified supply architecture.

The global Automated Machine Learning (AutoML) market is undergoing a structural transformation as organizations across industries seek to operationalize artificial intelligence at scale. By automating the complex, iterative tasks of model development—algorithm selection, hyperparameter tuning, feature engineering, and deployment—AutoML platforms are dramatically lowering the barrier to entry for AI adoption. This democratization is catalyzing demand across a spectrum of industries, from traditional sectors seeking digital transformation to tech-native companies aiming to accelerate innovation. The market’s evolution is characterized by the convergence of advanced algorithmic research, scalable cloud infrastructure, and an acute industry-wide need to derive actionable insights from ever-expanding datasets. As of the latest analysis in 2026, the market is in a phase of robust expansion and competitive diversification. Growth is propelled not by a single factor but by a synergistic combination of technological maturation, increasing data proliferation, and a pronounced shortage of skilled data scientists. The competitive landscape is multifaceted, featuring specialized pure-play AutoML vendors, end-to-end cloud platform providers integrating these tools natively, and open-source frameworks that foster community-driven innovation. This structure creates a dynamic environment where ease of use, integration capabilities, and tangible return on investment are key determinants of success. Looking towards the 2035 horizon, the AutoML market is anticipated to transition from a tool for model building to a core component of enterprise AI governance and continuous learning systems. Its role will expand beyond initial development to encompass the full AI lifecycle, including monito

The baseline scenario for the Automated Machine Learning market from 2026 to 2035 projects sustained double-digit growth, underpinned by structural demand for AI automation across enterprise and SME segments. The market is expected to expand at a compound annual growth rate (CAGR) of approximately 28.5% over the forecast period, with the market index rising from 100 in 2025 to 1,250 by 2035. This trajectory reflects a shift from early adopter experimentation to mainstream deployment, as AutoML becomes embedded in standard business workflows. Key assumptions in this baseline include continued cloud infrastructure investment, increasing data volumes from IoT and digital platforms, and persistent talent shortages in data science. The market is also supported by regulatory tailwinds in sectors like finance and healthcare, where explainable AI and model governance are becoming mandatory. However, the baseline scenario accounts for moderate headwinds from integration complexity in legacy IT environments and competition from open-source alternatives that may suppress pricing power for commercial vendors. Regionally, North America and Asia-Pacific will remain the largest markets, while Europe and Latin America show above-average growth rates as digitalization accelerates. The forecast assumes no major disruptive technology shifts, but rather a steady maturation of existing AutoML capabilities, including automated feature engineering, neural architecture search, and MLOps integration. By 2035, AutoML is expected to be a standard component of enterprise software stacks, with cloud-based platforms capturing the majority of revenue, though on-premise and edge solutions will grow in specialized use cases such as real-time inference and data sovereignty-sensitive industries.

Demand Drivers and Constraints

Primary Demand Drivers

• Shortage of skilled data scientists pushing enterprises toward automated ML solutions
• Proliferation of big data from IoT, digital platforms, and connected devices requiring scalable analytics
• Cloud infrastructure maturity enabling cost-effective, scalable AutoML platform deployment
• Rising demand for real-time predictive analytics in finance, healthcare, and retail
• Regulatory pressure for model transparency and governance driving adoption of automated, auditable ML pipelines
• Growth of low-code/no-code platforms empowering business analysts to build models without coding expertise

Potential Growth Constraints

• High integration complexity with legacy IT systems and existing data architectures
• Data privacy and security concerns, especially in regulated industries like healthcare and banking
• Competition from open-source AutoML frameworks limiting pricing power for commercial vendors
• Lack of trust in automated model decisions and need for human-in-the-loop validation
• Limited awareness and technical readiness among small and medium enterprises in emerging markets

Demand Structure by End-Use Industry

Banking, Financial Services, and Insurance (estimated share: 28%)

In BFSI, AutoML is transforming risk management and customer analytics. Banks and insurers are deploying automated models for real-time fraud detection, credit underwriting, and churn prediction, reducing model development time from months to weeks. The demand is fueled by regulatory requirements for model explainability and stress testing, which AutoML platforms address through automated documentation and audit trails. By 2035, the sector will see AutoML embedded in core banking systems, enabling continuous model retraining and deployment. Key demand-side indicators include the volume of digital transactions, regulatory compliance costs, and the adoption of cloud-native banking platforms. The shift toward open banking and real-time payments further accelerates the need for automated, scalable ML solutions. Current trend: Strong growth driven by fraud detection, credit scoring, and algorithmic trading automation.

Major trends: Automated fraud detection with real-time model updates, Explainable AI for regulatory compliance (e.g., GDPR, Basel III), and Integration of AutoML with core banking and insurance platforms.

Representative participants: JPMorgan Chase & Co, Goldman Sachs Group Inc, Allianz SE, HSBC Holdings plc, Wells Fargo & Company, and AXA SA.

Healthcare and Life Sciences (estimated share: 22%)

Healthcare organizations are leveraging AutoML to accelerate medical imaging analysis, genomic data interpretation, and clinical decision support. The automation of model development allows hospitals and research institutes to build predictive models for disease diagnosis, patient readmission risk, and treatment optimization without requiring deep ML expertise. Demand is driven by the explosion of electronic health records, wearable device data, and genomic sequencing outputs. By 2035, AutoML will be integral to precision medicine workflows, enabling real-time analysis of patient data for personalized treatment plans. Key indicators include healthcare IT spending, regulatory approvals for AI-based diagnostics, and the volume of clinical trial data. The sector’s growth is supported by partnerships between AutoML vendors and healthcare providers to ensure compliance with HIPAA and other data privacy regulations. Current trend: Rapid adoption for diagnostics, drug discovery, and personalized medicine.

Major trends: Automated medical image analysis for radiology and pathology, Predictive models for patient outcomes and hospital resource optimization, and Integration with electronic health records and clinical decision support systems.

Representative participants: UnitedHealth Group Incorporated, Johnson & Johnson, Pfizer Inc, Novartis AG, Siemens Healthineers AG, and Roche Holding AG.

Retail and E-commerce (estimated share: 18%)

Retailers and e-commerce platforms are adopting AutoML to enhance customer experience and operational efficiency. Automated models are used for demand forecasting, dynamic pricing, inventory management, and personalized product recommendations. The ability to quickly iterate models based on real-time sales data and customer behavior gives retailers a competitive edge. Demand is driven by the shift to omnichannel retail, increasing customer expectations for personalization, and the need to manage complex supply chains. By 2035, AutoML will enable fully automated pricing and inventory systems that adapt to market conditions in real time. Key indicators include e-commerce penetration rates, retail technology investment, and the volume of customer interaction data. The sector benefits from low-code AutoML tools that allow business analysts to build and deploy models without IT intervention. Current trend: Growing use for demand forecasting, personalized recommendations, and supply chain optimization.

Major trends: Real-time demand forecasting and dynamic pricing automation, Personalized product recommendations using automated collaborative filtering, and Supply chain optimization through predictive inventory and logistics models.

Representative participants: Amazon.com Inc, Walmart Inc, Alibaba Group Holding Limited, The Home Depot Inc, Target Corporation, and eBay Inc.

Manufacturing and Industrial (estimated share: 17%)

Manufacturers are deploying AutoML to reduce downtime and improve product quality through predictive maintenance and defect detection. Automated models analyze sensor data from production equipment to predict failures before they occur, while computer vision models inspect products for defects in real time. The demand is driven by the Industry 4.0 movement, the proliferation of IoT sensors in factories, and the need to optimize energy consumption and production yields. By 2035, AutoML will be a standard component of manufacturing execution systems, enabling autonomous decision-making on the factory floor. Key indicators include industrial IoT adoption rates, manufacturing output, and investment in smart factory technologies. The sector’s growth is supported by edge AutoML solutions that run models locally for low-latency inference in environments with limited connectivity. Current trend: Increasing adoption for predictive maintenance, quality control, and process optimization.

Major trends: Predictive maintenance using automated time-series analysis, Automated visual inspection for quality control, and Process optimization through reinforcement learning and simulation.

Representative participants: Siemens AG, General Electric Company, ABB Ltd, Rockwell Automation Inc, Schneider Electric SE, and Fanuc Corporation.

Telecommunications and IT (estimated share: 15%)

Telecom and IT companies use AutoML to manage network traffic, predict customer churn, and automate IT operations. Automated models analyze network performance data to optimize bandwidth allocation and detect anomalies, while customer analytics models identify at-risk subscribers for targeted retention campaigns. Demand is driven by the rollout of 5G networks, increasing data traffic, and the need to reduce operational costs. By 2035, AutoML will enable self-optimizing networks that adjust parameters in real time based on usage patterns. Key indicators include telecom capital expenditure, subscriber growth, and the volume of network data. The sector also benefits from AutoML integration with IT service management platforms for automated incident response and root cause analysis. Current trend: Steady growth for network optimization, customer churn prediction, and service automation.

Major trends: Automated network optimization and anomaly detection, Customer churn prediction and personalized retention campaigns, and IT operations automation through AIOps and automated root cause analysis.

Representative participants: AT&T Inc, Verizon Communications Inc, Deutsche Telekom AG, NTT Group, Telefonica S.A, and Vodafone Group Plc.

Key Market Participants

Regional Dynamics

Asia-Pacific (estimated share: 35%)

Asia-Pacific leads the market with rapid digitalization in China, India, and Southeast Asia. Strong government AI initiatives, a large manufacturing base, and growing fintech sector drive adoption. Cloud infrastructure expansion and a young tech-savvy workforce accelerate AutoML deployment across SMEs and enterprises. Direction: up.

North America (estimated share: 32%)

North America remains a dominant market, driven by early adoption in finance, healthcare, and tech sectors. Presence of major cloud providers and AutoML vendors, coupled with high data science talent costs, sustains demand. Mature regulatory environment supports governance-focused AutoML solutions. Direction: stable.

Europe (estimated share: 20%)

Europe shows robust growth, fueled by GDPR-driven demand for explainable AI and digital transformation in manufacturing and finance. Germany, UK, and France lead adoption. Increasing investment in AI research and industrial automation supports market expansion, though data sovereignty concerns favor on-premise solutions. Direction: up.

Latin America (estimated share: 8%)

Latin America is an emerging market with growing interest in AutoML for banking, retail, and agriculture. Brazil and Mexico lead due to fintech innovation and agritech adoption. Limited data science talent and cloud infrastructure gaps are being addressed by international vendors and local startups. Direction: up.

Middle East & Africa (estimated share: 5%)

Middle East & Africa is a nascent but fast-growing market, driven by smart city projects in UAE and Saudi Arabia, and fintech growth in South Africa and Nigeria. Government diversification efforts and investment in digital infrastructure create opportunities, though adoption is constrained by limited technical expertise and data availability. Direction: up.

Market Outlook (2026-2035)

In the baseline scenario, IndexBox estimates a 12.0% compound annual growth rate for the global automated machine learning market over 2026-2035, bringing the market index to roughly 420 by 2035 (2025=100).

Note: indexed curves are used to compare medium-term scenario trajectories when full absolute volumes are not publicly disclosed.

For full methodological details and benchmark tables, see the latest IndexBox Automated Machine Learning market report.