ERP

SAP A.I. (Artificial Intelligence) on S/4 HANA Certification Program

This comprehensive 44-hour course provides in-depth knowledge of integrating Artificial Intelligence into your SAP S/4 HANA environment, preparing you for certification.

Hands-on integration and labs: Practical, scenario-based exercises using SAP S/4 HANA and SAP Business Technology Platform to design, train, and deploy AI/ML models within real S/4 HANA processes. Certification-focused curriculum: Exam-aligned modules, sample questions, and instructor guidance to prepare participants for the SAP A.I. on S/4 HANA certification. End-to-end AI lifecycle & governance: Coverage of data preparation, model selection, deployment, monitoring, and best practices for scalable, production-ready AI solutions in S/4 HANA.

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44 hrs

Total duration

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This program includes

Live, mentor-led classes Recognized certificate Placement support Hands-on projects Lifetime access

SAP A.I. (Artificial Intelligence) on S/4 HANA Certification Program

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Program overview

About this program

This ERP program blends live, mentor-led sessions with hands-on projects and real-world case studies, so you build genuinely job-ready skills — not just theory. You'll learn from working industry experts, apply what you learn on practical assignments, and finish with a portfolio, a recognized certificate, and dedicated placement support to help you land the right role.

This comprehensive 44-hour course provides in-depth knowledge of integrating Artificial Intelligence into your SAP S/4 HANA environment, preparing you for certification.

The SAP A.I. on S/4 HANA Certification Program is designed for professionals looking to enhance their skills in the field of Artificial Intelligence within the SAP ecosystem. This course covers the key concepts, tools, and techniques to effectively implement AI solutions in S/4 HANA environments, and includes practical exercises to solidify learning.

Participants will explore the various AI functionalities that can be integrated into SAP S/4 HANA, understand machine learning models, and learn how to leverage SAP’s cloud platform for AI applications. By the end of this program, you'll be well-equipped to take the certification exam and apply your skills in real-world scenarios.

Why learners love this program

Hands-on integration and labs: Practical, scenario-based exercises using SAP S/4 HANA and SAP Business Technology Platform to design, train, and deploy AI/ML models within real S/4 HANA processes.
Certification-focused curriculum: Exam-aligned modules, sample questions, and instructor guidance to prepare participants for the SAP A.I. on S/4 HANA certification.
End-to-end AI lifecycle & governance: Coverage of data preparation, model selection, deployment, monitoring, and best practices for scalable, production-ready AI solutions in S/4 HANA.

Lectures

Hours

All level

Level

Globally trusted accreditations

Recognitions that power your profile

Trusted by leading accreditation bodies — so your certificate is respected by employers worldwide.

Learning outcomes

What you'll be able to do

Fundamental AI and machine learning concepts and terminology relevant to SAP S/4 HANA

Key AI use cases across S/4 HANA modules (Finance, SCM, Manufacturing, Sales) and how to identify opportunities

Data preparation for AI in S/4 HANA: extraction, cleansing, feature engineering, and data quality best practices

Building and training ML models using SAP tools (SAP AI Core, AI Foundation, SAP BTP) and common ML frameworks

Deploying and integrating ML models into S/4 HANA processes, Fiori apps, and custom extensions

Designing and implementing conversational AI and RPA integrations (e.g., SAP Conversational AI, Intelligent RPA)

Implementing predictive analytics, anomaly detection, and recommendation systems within the SAP landscape

Model lifecycle management: versioning, monitoring, retraining, and CI/CD practices for ML in SAP environments

Model governance, explainability, bias mitigation, and compliance considerations for enterprise AI

Security and privacy best practices for AI solutions in SAP (authentication, authorization, data protection)

Performance tuning and scalability strategies for AI services on SAP BTP and hybrid deployments

Building automated end-to-end AI pipelines and integrating them with existing enterprise workflows

Hands-on development: labs and exercises to apply AI techniques to real-world S/4 HANA scenarios

Preparation for the SAP S/4 HANA AI certification: exam topics, study strategies, and sample question practice

Organizational and change-management aspects of adopting AI in an enterprise context, including ethical and legal considerations

Curriculum

Structured modules that build real skills

17 modules · designed for progressive, hands-on learning.

What is AI?

Define artificial intelligence and identify its primary goals in enterprise contexts.

1.00 hrs

History and evolution of AI

Summarize the development of AI technologies and major milestones that shaped the field.

1.00 hrs

AI use cases in enterprise

Describe common AI use cases and their business value in modern organizations.

1.50 hrs

Ethics and responsible AI

Explain ethical considerations and principles for responsible AI deployment.

1.00 hrs

Key components of AI systems

Identify the essential components of AI systems including data, models, and infrastructure.

1.00 hrs

S/4 HANA system architecture

Describe the architecture of SAP S/4 HANA and how it supports real-time processing.

1.00 hrs

Core modules and data model

Explain core S/4 HANA modules and the underlying data model used for analytics and transactions.

1.50 hrs

In-memory computing and performance

Describe the impact of in-memory computing on application performance and analytics.

1.00 hrs

Extensibility and integration points

Identify extensibility options and integration points for connecting external services to S/4 HANA.

1.00 hrs

SAP Cloud Platform and services

Summarize relevant SAP cloud services that complement S/4 HANA for AI workloads.

1.00 hrs

Machine learning basics

Define machine learning and contrast supervised, unsupervised, and reinforcement learning.

1.00 hrs

Neural networks and deep learning

Explain the basics of neural networks and when deep learning techniques are appropriate.

1.00 hrs

Data preprocessing and feature engineering

Describe common data preprocessing steps and feature engineering techniques for model readiness.

1.50 hrs

Natural Language Processing

Summarize key NLP concepts and typical enterprise NLP use cases.

1.00 hrs

Computer Vision overview

Explain core computer vision techniques and scenarios where they apply in business processes.

1.00 hrs

APIs and service-based integration

Identify API-driven integration patterns for connecting AI services to S/4 HANA.

1.00 hrs

Event-driven and batch integration

Compare event-driven and batch integration approaches and their trade-offs for AI workflows.

1.00 hrs

Data pipelines and connectivity

Describe how to design data pipelines for reliable data flow between S/4 HANA and AI systems.

1.00 hrs

Data governance and quality

Explain the importance of data governance and methods to ensure high data quality for models.

1.50 hrs

Security and compliance considerations

Identify security controls and compliance requirements relevant to AI integrations in SAP.

1.00 hrs

Predictive maintenance example

Analyze a predictive maintenance case study and extract lessons relevant to implementation.

1.00 hrs

Finance and procurement use cases

Examine AI applications in finance and procurement and their measurable benefits.

1.00 hrs

Change management and adoption

Discuss strategies to drive user adoption and manage organizational change for AI projects.

1.00 hrs

Scaling AI solutions

Identify technical and operational challenges in scaling AI solutions across the enterprise.

1.00 hrs

Supervised learning algorithms

Describe common supervised algorithms and their appropriate problem types.

1.00 hrs

Unsupervised learning algorithms

Explain unsupervised techniques and how they are used for clustering and anomaly detection.

1.00 hrs

Reinforcement learning overview

Summarize core concepts of reinforcement learning and potential SAP use cases.

1.50 hrs

Model evaluation metrics

Define common evaluation metrics and how to choose them based on the business objective.

1.00 hrs

Monitoring and model drift

Explain methods to monitor models in production and detect drift over time.

1.00 hrs

AI Core capabilities

Describe the capabilities of SAP AI Core and how it supports model lifecycle management.

1.00 hrs

AI Foundation services and tools

Summarize AI Foundation services and tools available for building and operating AI solutions.

1.00 hrs

Model deployment workflows

Outline typical model deployment workflows and deployment considerations on SAP platforms.

1.00 hrs

CI/CD and MLOps practices

Explain CI/CD and MLOps best practices to ensure reliable model delivery and maintenance.

1.50 hrs

Data preparation and feature stores

Describe how to prepare data and use feature stores for reproducible model training.

1.00 hrs

Training and validation pipelines

Explain design of training and validation pipelines including cross-validation techniques.

1.00 hrs

Model optimization and tuning

Identify techniques for hyperparameter tuning and model optimization to improve performance.

1.00 hrs

Emerging AI technologies

Discuss emerging AI technologies and their potential impact on SAP ecosystems.

1.00 hrs

Implications for enterprise strategy

Analyze how future trends may influence enterprise strategy and technology roadmaps.

1.00 hrs

Key topic revision

Review core topics from the course and reinforce critical concepts for the exam.

1.00 hrs

Practice exams and time management

Practice exam questions under timed conditions and learn effective time management techniques.

1.00 hrs

Exam-day strategies and next steps

Learn practical exam-day strategies and recommended next steps after certification.

1.00 hrs

Your credential

The certificate you'll earn

Earn an industry-recognized certificate on successful completion of this program.

SAP A.I. (Artificial Intelligence) on S/4 HANA Certification Program certificate

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Reviews

What our learners say

Learning SAP AI improved my understanding of intelligent workflows. Applying AI concepts inside SAP felt natural and business-focused.

NKNavpreet Kaur Bhatia

SAP AI concepts felt simple once they were connected to real workflows. Seeing predictions and insights in action made learning exciting.

SMShikha Mathur

SAP AI helped me understand how artificial intelligence supports business decisions. Learning through real SAP use cases made the concepts easy to relate to.

NMNeha Malhotra

Understanding how AI models integrate with SAP workflows was very useful. The examples made it easy to see how decisions become faster and smarter.

ISIrfan Sabir

Learning how SAP uses AI for forecasting and decision support changed my perspective. Hands-on examples made it clear how businesses gain value from intelligent insights.

RKRohit Kaul

SAP AI helped me understand how artificial intelligence fits into real business processes. Working with intelligent scenarios inside SAP made automation and predictions feel practical, not theoretical.

ASAmitabh Saran

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