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27 AutoML Statistics: Market Size, Adoption & Benefits [2024]

Automated Machine Learning (AutoML) is an emerging technology to automate machine learning tasks to accelerate the model building process, help data scientists focus on higher value added tasks and improve the accuracy of ML models. To understand AutoML better, you can explore comparison of AutoML products, an in-depth guide to AutoML tools and case studies.

This article is a collection of 27 up-to-date AutoML statistics from surveys and researches of reputable sources. In this list, you will find AutoML stats about:

Market size forecasts

  • The global AutoML market has generated a revenue of $270 million in 2019 and is expected to reach $15 billion by 2030. (Research and Markets)
  • The global AutoML market is expected to advance at a CAGR of 44% during the forecast period (2020–2030). (Research and Markets)
  • Over 65% of the AutoML market is expected to be in North America and Europe by 2030. (Research and Markets)

AutoML Adoption

  • Current adoption: 61% of data and analytics decision-makers whose firms are adopting AI said they had implemented AutoML software or are in the process of implementing it. (Forrester)
  • Future adoption: 25% of data and analytics decision-makers whose firms are adopting AI said they are planning to implement AutoML software within the next year. (Forrester)

AutoML Benefits/Results of implementation

AutoML vendors claim the below benefits in their case studies.

Financial Benefits

  • Ascendas Singbridge Group (ASG) has experienced a 20% increase in their revenue after implementing an AutoML software. (DataRobot)
  • D&G, Domestic and General, has experienced a revenue uplift from 1.5% to 4% by deploying pricing optimization for the company’s customer base. AutoML enabled D&G to increase the number of customers getting an optimal price from 40k to 300k. (DataRobot)
  • Steward Health Care secured a net $2M saving per year from 1% reduction of registered nurses’ hours and a net $10 million saving per year from 0.1% reduction in patient length of stay. Given the limited impact, the fact that these KPIs vary with seasonality and competition and that impact measurement methodology was not shared, we have less confidence in these improvement metrics. (DataRobot)

Performance Improvement Benefits

  • Imagia reduced the test processing time from 16 hours to 1 hour by implementing Google’s AutoML platform. (Google)
  • California Design Den reduced inventory carryovers by 50% after implementing Google’s AutoML tool. (Google)
  • Evariant enjoyed 10 times shorter deployment time by implementing DataRobot. (DataRobot)
  • G5 achieved a lead scoring accuracy of 95% and reduced the model tuning and feature engineering time by 80% after implementing an AutoML software. (H2O)
  • Lenovo increased the accuracy of their predictions from 80% to 87.5% and reduced the model creation time from 4 weeks to 3 days in Brazil by using DataRobot. (Amazon)
  • A Danish marketing company improved mail opening rates of Copenhagen Concert Hall by 14%, mail click rates by 24% and increased ticket sales by 83% after implementing DataRobot. (DataRobot)
  • DMway helped Israel’s oldest and largest cellular phone provider Pelephone to increase their purchase rate by 3.5% in the first month and increase their conversion rate by 300%. (DMWay)
  • Trupanion was able to identify 2/3 of customers that churn before they churn and they increased productivity 10 times by speeding up processes after implementing DataRobot. (DataRobot)

Fraud Detection Benefits

  • Paypal employed’s AutoML tool to improve their fraud detection model. The accuracy of their model improved from 89% to 94.7% and models were created 6 times faster. (H2O)
  • Consensus Corporation benefited from 24% improvement in fraud detection and 19% gain in overall financial performance by deploying Trifacta Wrangler and DataRobot on AWS. (Trifacta-AWS-DataRobot Case Study)
  • AutoML implementation also provided a 55% reduction in false-positive rates while detecting frauds and reduced deployment time from 3-4 weeks to 8 hours. (Trifacta-AWS-DataRobot Case Study)

Top AutoML vendor funding stats

  • DataRobot raised $431 million in funding in 7 funding rounds.
  • raised $151 million in funding in 7 funding rounds.
  • 7 investments were made by Google Cloud Platform. Their most recent investment was on July 25, 2022.
  • dotData raised $43 million funding in 2 funding rounds.

Figure 1: AutoML Statistics by AIMultiple

Top AutoML vendors by number of employees

  • DataRobot has 1001-5000 employees.
  • has 11-50 employees.
  • Google Cloud Platform has 10001+ employees.
  • dotData has 51-100 employees.

For more detailed information about AutoMLvendors, please check our AutoML vendor selection guide or contact us:

Find the Right Vendors


Research and Markets*, Research and Markets**, Research and Markets***, Forrester*, Forrester**, DataRobot*, DataRobot**, DataRobot***, Google*, Google**, DataRobot****, H2O*, Amazon, DataRobot*****, DMWay, DataRobot******, H2O**, Trifacta-AWS-DataRobot Case Study*, Trifacta-AWS-DataRobot Case Study**, Funding and number of employees data is from Crunchbase

This article was originally written by former AIMultiple industry analyst Izgi Arda Ozsubasi and reviewed by Cem Dilmegani

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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