List of SAP Leonardo Machine Learning Customers
Walldorf, 69190,
Germany
Since 2010, our global team of researchers has been studying SAP Leonardo Machine Learning customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased SAP Leonardo Machine Learning for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using SAP Leonardo Machine Learning for ML and Data Science Platforms include: Costco, a United States based Retail organisation with 333000 employees and revenues of $254.45 billion, Mercedes Benz, a Germany based Automotive organisation with 175264 employees and revenues of $171.47 billion, BASF, a Germany based Life Sciences organisation with 111408 employees and revenues of $75.74 billion, Cisco Systems, a United States based Professional Services organisation with 90400 employees and revenues of $53.80 billion, Jabil, a United States based Manufacturing organisation with 250000 employees and revenues of $33.48 billion and many others.
Contact us if you need a completed and verified list of companies using SAP Leonardo Machine Learning, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the Machine Learning software purchases.
The SAP Leonardo Machine Learning customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of Machine Learning software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Aegea | Professional Services | 4977 | $445M | Brazil | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, Aegea engaged the SAP Leonardo Lab in Brazil and deployed SAP Leonardo Machine Learning as a fast track prototyping effort for its Prolagos water distribution business. Aegea SAP Leonardo Machine Learning ML and Data Science Platforms deployment centered on rapid experimentation with IoT ingest, Big Data analytics, and machine learning driven predictive analytics to inform operational decisions in the sanitation and water distribution domain.
The implementation used SAP Leonardo Platform capabilities to prototype machine learning models and predictive analytics workflows, and to author Leonardo Apps that surface analytics into operational processes. Functional modules explored included time series analytics for sensor data, model training and scoring pipelines, and analytics visualization tied to business process contexts. SAP Leonardo Machine Learning was used to contextualize operational data rather than as a standalone reporting layer.
Integrations were explicitly focused on ingesting Big Data from operational technology systems, geographical information systems, and IoT sensors, with the SAP Leonardo Foundation providing the technical services and platform tooling. The deployment pattern emphasized lab-driven prototypes hosted through the SAP Leonardo Lab, enabling data collection from OT and GIS sources and iterative model refinement within the Leonardo environment.
Governance and rollout followed a lab to pilot approach managed by the SAP Leonardo Lab and Aegea teams, with prototypes presented externally at Leonardo Live as part of regional innovation activities. The project scope was prototyping and app building for Prolagos in Brazil, intended to support existing process models or enable new operational workflows through Leonardo Apps and embedded analytics.
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AMAG | Automotive | 6500 | $4.6B | Switzerland | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, AMAG implemented SAP Leonardo Machine Learning to support Connected Fleet use cases, presenting the work at SAP Forum Basel 2017. AMAG presented to customers and partners, demonstrating the business value of Connected Fleet from the SAP Leonardo portfolio while framing SAP Leonardo Machine Learning as the ML and Data Science Platforms component for fleet analytics.
The implementation centered on SAP Leonardo Machine Learning capabilities aligned to connected fleet scenarios, including model training and deployment workflows, feature engineering pipelines, and operational analytics for fleet operations. Functional modules emphasized real time scoring, anomaly detection, and predictive analytics patterns common to ML and Data Science Platforms, with model lifecycle management and orchestration embedded in the Leonardo portfolio.
Operational scope targeted AMAGs fleet services and connected vehicle initiatives in Switzerland, embedding machine learning outputs into service planning and connected fleet decision processes. Governance focused on data pipeline standardization and model validation workflows to support repeatable deployments, and AMAG used the SAP Forum Basel demonstration to prove the business value of the Connected Fleet use case while positioning SAP Leonardo Machine Learning within its ML and Data Science Platforms strategy for fleet operations.
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Arctic Wind | Professional Services | 20 | $12M | Norway | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
In 2017, Arctic Wind implemented SAP Leonardo Machine Learning as a maintenance helper. Arctic Wind deployed SAP Leonardo Machine Learning within its ML and Data Science Platforms footprint to support maintenance workflows, tying the company, SAP Leonardo Machine Learning, the ML and Data Science Platforms category, and the maintenance business function together in a single implementation narrative.
For a 20-employee professional services firm, the implementation emphasized lightweight model training and inference pipelines and the use of classification and anomaly detection workflows typical of ML and Data Science Platforms. Configuration focused on embedding model outputs into maintenance decision processes and operational workflows, with governance activity centered on model lifecycle management, retraining cadence, and monitoring to sustain the maintenance helper capability across operations and service delivery teams.
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Life Sciences | 111408 | $75.7B | Germany | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
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Professional Services | 1400 | $200M | South Africa | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2018 | n/a |
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Utilities | 22147 | $26.8B | United Kingdom | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
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Utilities | 39000 | $5.7B | China | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
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Professional Services | 90400 | $53.8B | United States | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2017 | n/a |
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Retail | 333000 | $254.5B | United States | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2018 | n/a |
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Automotive | 20000 | $7.3B | South Korea | SAP | SAP Leonardo Machine Learning | ML and Data Science Platforms | 2018 | n/a |
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Buyer Intent: Companies Evaluating SAP Leonardo Machine Learning
- Naver, a South Korea based Communications organization with 14000 Employees
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