AutoML Case Studies: Applications and Results in 2025

Updated on April 10, 2025

Though there’s a lot of buzz around AutoML, finding a comprehensive compilation of real-world case studies can be challenging. We’ve built this list of 22 case studies to demonstrate how companies across various industries are leveraging automated machine learning. This exploration will help you understand AutoML's potential for your business functions, especially in improving decision-making and forecasting, ultimately saving time, enhancing accuracy, and democratizing data science.

Typical Results of AutoML Projects

Across these case studies, three benefits stand out:

Common AutoML Use Cases

Companies automate machine learning for diverse purposes, often to enhance existing ML implementations or to gain automated insights for better data-driven decisions and predictions. Organizations typically apply AutoML to:

Full List of Case Studies

Company Country AutoML Tool Industry Use Case Results
Ascendas-Singbridge Group (ASG) Singapore DataRobot Real Estate Parking Lot Efficiency
  • 20% revenue increase
  • Faster deployment
  • More accurate usage predictions
Avant US DataRobot Finance Loan Decisions
  • Time savings
  • More accurate risk identification
California Design Den US Google Cloud AutoML Retail & Consumer Goods E-Commerce
  • 50% reduction in inventory carryovers
  • Improved profit margins
Consensus Corporation US DataRobot Technology Fraud Detection
  • 24% improvement in fraud detection
  • 55% reduction in false positives
  • Deployment time cut to 8 hours (from 3–4 weeks)
DemystData US DataRobot Technology Product Quality
  • Process democratization
  • 90% cost reduction
Domestic & General (D&G) UK DataRobot Insurance Customer Experience
  • Optimal pricing for 300,000 customers (up from 40,000)
  • Pricing optimization boost from 1.5% to 4% revenue
Evariant US DataRobot Healthcare Service Delivery & Marketing
  • 10× faster deployment
  • Higher client engagement
  • Improved ROI & service quality
G5 US H2O.ai Real Estate Marketing & Call Center Management
  • 5× faster model building
  • 95% accuracy
Harmoney Australia DataRobot Fintech Credit Application Process
  • More accurate risk assessment
  • Increased profitability
  • Shortened application time
Hortifrut Chile H2O.ai Agriculture Product Quality
  • Deployment time cut from weeks to hours
Imagia Canada Google Cloud AutoML Healthcare R&D & Diagnosis
  • Test time reduced from 16h to 1h
  • Improved diagnostic accuracy
Lenovo Brazil DataRobot Technology Sales & Manufacturing
  • Accuracy up from 80% to 87.5%
  • Model creation cut from 4 weeks to 3 days
LogMeIn US DataRobot Technology Customer Experience
  • Data analysis time cut from days to minutes
  • Continuous accuracy improvements
  • Faster deployments
Meredith Corporation US Google Cloud AutoML Media & Entertainment Content Classification
  • Better trend forecasting
  • Enhanced user experience
NTUC Income Singapore DataRobot Insurance Pricing
  • Simplified data complexity
  • Key driver insights for pricing
One Marketing Denmark DataRobot Marketing Email Marketing
  • Spam reduced
  • Open rate +14%
  • Click rate +24%
  • Ticket sales +83%
PayPal US H2O.ai Financial Services Fraud Detection
  • 95% accuracy
  • Training time under 2 hours
Pelephone Israel DMWay Telecommunications Sales Management
  • Purchase rate +3.5% in month one
  • Conversion rate ×4
PGL Israel DMWay Transportation Planning Scheduling & Routing
  • Time savings in data analysis
  • Process democratization
Steward Health Care US DataRobot Healthcare Staff Planning
  • $2M savings/year (1% RN hours reduction)
  • $10M savings/year (0.1% patient stay reduction)
Trupanion US DataRobot Insurance Pricing & Churn Management
  • 10× productivity improvement
  • Churn risk identified two-thirds early
Vision Banco Paraguay H2O.ai Banking Risk Management
  • Propensity to buy doubled
  • Faster, more accurate credit scoring

About the Author & Source

This compilation is based on research by AIMultiple. The original article and analysis are significantly contributed to by Cem Dilmegani, principal analyst at AIMultiple since 2017. His work, cited by global publications like Forbes and Business Insider, focuses on helping businesses understand and implement emerging technologies. Cem holds a degree in computer engineering and an MBA from Columbia Business School. You can follow Cem Dilmegani on LinkedIn for more insights.

Further Reading & Resources

To continue your exploration of AutoML and related topics, consider these resources from AIMultiple:

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Content adapted and summarized for illustrative purposes. For the complete and original article, please refer to the source link.