Sep 30, 2024
•6 minute read
SAP Machine Learning
SAP Machine Learning focuses on how SAP customers use machine learning to improve analytics, automate decisions, and embed intelligence into business processes. The topic covers SAP Business Technology Platform, SAP AI Core, SAP AI Launchpad, SAP Analytics Cloud, SAP S/4HANA, SAP Business Data Cloud, and related data science and automation tools. It is relevant for CIOs, data leaders, analytics teams, enterprise architects, finance leaders, supply chain planners, and SAP practitioners looking to move from manual reporting toward predictive, automated, and AI-supported operations.
What is SAP Machine Learning?
SAP Machine Learning refers to he use of machine learning models and embedded AI capabilities across SAP applications, platforms, and data environments. In practical terms, it helps organizations analyze historical and real-time business data, identify patterns, predict outcomes, automate repetitive decisions, and surface recommendations inside SAP workflows.
SAP Machine Learning focuses on how SAP customers use machine learning to improve analytics, automate decisions, and embed intelligence into business processes. The topic covers SAP Business Technology Platform, SAP AI Core, SAP AI Launchpad, SAP Analytics Cloud, SAP S/4HANA, SAP Business Data Cloud, and related data science and automation tools. It is relevant for CIOs, data leaders, analytics teams, enterprise architects, finance leaders, supply chain planners, and SAP practitioners looking to move from manual reporting toward predictive, automated, and AI-supported operations.
What is SAP Machine Learning?
SAP Machine Learning refers to he use of machine learning models and embedded AI capabilities across SAP applications, platforms, and data environments. In practical terms, it helps organizations analyze historical and real-time business data, identify patterns, predict outcomes, automate repetitive decisions, and surface recommendations inside SAP workflows.
AP customers may use machine learning through embedded capabilities in SAP applications, AI services on SAP BTP, predictive features in SAP Analytics Cloud, or custom models connected to SAP and non-SAP data.
What are some SAP Machine Learning use cases?
Predictive Forecasting
Machine learning can help SAP users forecast demand, revenue, cash flow, inventory needs, or workforce requirements. In SAP environments, these models can support planning teams by identifying patterns in operational and financial data before they appear in standard reports.
Intelligent Finance Automation
Finance teams can apply machine learning to detect anomalies, classify transactions, improve cash application, and support forecasting. In SAP S/4HANA environments, this can reduce manual review while helping finance leaders focus on exceptions, risk, and business performance.
Supply Chain Optimization
SAP machine learning can help planners predict disruptions, optimize inventory, recommend supplier actions, and improve logistics planning. These use cases connect operational data from SAP systems with external signals to improve resilience and responsiveness.
Document Processing and Data Extraction
Machine learning can automate extraction, classification, and validation of data from invoices, purchase orders, contracts, and service documents. SAP BTP AI services and related automation tools can help reduce manual entry and improve process accuracy.
Customer and Employee Recommendations
Machine learning can support personalized recommendations, next-best actions, and service prioritization. In SAP CX, SuccessFactors, and analytics environments, these models can improve engagement by helping teams act on behavioral, workforce, and transactional data.
What does SAPinsider research say about SAP Machine Learning?
The SAPinsider Benchmark Report, AI Adoption and Maturity in the SAP Ecosystem, shows broad but still-maturing AI use: 91% report some AI use, while 47% are still testing or piloting AI in selected areas.
Technology Leader’s Strategic Agenda for 2026 shows SAP-connected AI and ML plans are focused on practical outcomes, with 40% targeting intelligent automation and decision support and 40% targeting predictive analytics and forecasting.
The SAP Business Data Cloud Use Cases and Adoption reports highlights the data foundation challenge behind machine learning: only 3% of organizations have a unified governed data layer.
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Feb 26, 2024
•1 minute read
Super-Powering Your Factory TransformationLeverage intelligent manufacturing solutions from Fujitsu to drive sustainable growth while saving energy, reducing waste, and minimizing your climate footprint. From initial stages to advanced initiatives, Fujitsu, in collaboration with Microsoft Azure and SAP, stands ready to be your trusted partner in creating new value for your business, society, and the environment. Don't miss this exclusive opportunity to gain insights from industry experts, analysts, and thought leaders, and discover how the integration of Microsoft Azure, SAP, and Fujitsu's smart manufacturing solutions can shape a more sustainable future for manufacturing.
Jun 28, 2023
•1 minute read
Machine Learning Renaissance with SAP Kyma 2.0Open source development is very normal in the data science world. The popularity of languages like Python and R can be attributed to the explosion of development work happening to leverage open-source tools and technologies. However, many who are on SAP technologies are often unsure how to leverage and integrate open source tools within their SAP technology portfolio. In this video, SAPinsider analyst Kumar Singh provides his perspectives on how SAP Kyma opens a floodgate of opportunities who want to exploit machine learning to its full potential in their SAP environments.
Jun 22, 2022
•1 minute read
Automated Machine Learning in the CloudAutomated machine learning tools have evolved into the democratization of data science. They now include a broader scope, encompassing the automation of the entire data-to-insights pipeline — from cleaning data to tuning algorithms through feature selection and creation, even operationalization. And now, with the advent of the cloud, the case of AutoML has become much more substantial. This article will explore:
Why the cloud has fueled the need for Automated ML
What are the Automated ML solutions offered by hyperscalers
What are some critical aspects SAPinsiders need to be aware of when strategizing about Automated ML in the cloud
Oct 28, 2021
•8 minute read
Machine Learning Powered Intelligent Replenishment in Retailby Kumar Singh, Research Director, Automation & Analytics, Supply Chain Management, SAPinsider The criticality of establishing an efficient store replenishment process The process efficiency of replenishing store inventory is critical to a retailer’s overall operating efficiency and even profitability. It is not news to anyone that store replenishment impacts the on-the-shelf availability. In today’s […]
Jul 6, 2021
•4 minute read
Drive Predictive planning through machine learning with SAP Analytics CloudSAP Analytics Cloud provides machine learning capabilities that can help organizations integrate advanced and predictive analytics within their planning processes. Given the rapid pace of change and disruption within global organizations, these features can provide useful insight to the business. In this session you will:
- Gain a detailed introduction to the concept of predictive planning and how it is improving the way enterprise organizations run their business
- Walk through the machine learning and predictive planning capabilities available in SAP Analytics Cloud
- Understand the skills and technology prerequisites needed to unlock the potential of SAP Analytics Cloud’s predictive planning capabilities
Jun 7, 2021
•1 minute read
Leveraging Machine Learning (ML) in Spend Analysis solutionsMachine Learning (ML) based algorithms are slowly percolating into a wide variety of supply chain solutions and spend analysis solutions are no exception. In this article, I share few ways Machine Learning (ML) algorithms are already being used or can be used in spend analysis context. Note that these suggestions are focused only on spend […]
Jan 25, 2021
•3 minute read
Machine Learning in the Financial Close Process: Where can it add the most value?This session explores how utilizing Machine Learning can help automate many of the day-to-day tasks that can often consume the finance organization, helping companies shift focus from a transactional to a more strategic approach to finance. Topics include:
- An overview of Machine Learning (what it is, how does it work)
- An overview of Predictive Accounting and how it can help transform the way in which an organization takes on the accounting process
- Finance Scenarios where ML can add value
Oct 20, 2020
•1 minute read
SAP Leonardo Vs. TensorFlowMachine learning (ML), considered a subset of artificial intelligence, is growing in popularity among businesses that want to create better computing models with high volumes of data in order to make decisions faster. SAP Leonardo and Google’s TensorFlow are both known, in part, for their ability to prototype and integrate with ML projects. In this article, the author compares the subtle differences between the two solutions to help customers determine which utility will meet their individual ML project needs, including real-life examples that demonstrate how two well-known companies are using each product.
After reading this article you will be able to:
- State several similarities between SAP Leonardo and TensorFlow;
- Identify specific differences between the two solutions; and
- Understand which components might make one solution the best option for your ML project.
Jul 7, 2020
•4 minute read
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