AI business cases with SAP
How can your company benefit from AI? This book provides answers based on nine practical business cases and shows you how to optimize your business processes with AI. From inventory and invoice verification to software development, René Kessler and Marcel Kuchler explain how to implement AI projects with SAP technologies such as the SAP Business Technology Platform, SAP AI Core, and Joule. Learn what prerequisites you need to put in place and how to avoid common mistakes.
- A wide range of use cases from different departments
- Tools and technologies for your own AI project
- Including SAP Business AI, SAP AI Core, and SAP Generative AI Hub
You'll learn about:
- Fundamentals and potential of AI:
The authors explain the basics of AI, the different disciplines involved, and how companies can benefit from it. They show how AI technologies are used to make business processes more intelligent, efficient, and future-proof.
- The AI Toolkit from SAP:
Get to know SAP's AI tools: From the SAP Business Technology Platform to predefined AI functions in SAP Business AI to customized solutions with AI Services – this book provides a complete overview.
- Nine real-world use cases:
Using concrete examples from areas such as logistics, finance, and software development, the authors show how companies can use AI profitably, with all the details on objectives, technical requirements, and implementation. You will also learn how to implement your AI project in a compliant and secure manner.
Key Highlights:
- AI basics
- SAP Business Technology Platform
- SAP AI Core
- Joule
- System requirements
- Implementation strategies
- Security and compliance
View Full Table of Contents
- Introduction
- PART I Introduction: The tools for your AI project
- 1 AI and SAP: Intelligence in Your Business Processes
- 1.1 The historical development of AI
- 1.2 Definitions and distinctions
- 1.2.1 Fundamentals of AI and data science
- 1.2.2 Important disciplines and technologies of AI
- 1.2.3 Examples of AI in everyday life
- 1.3 Why AI is fundamentally changing the DNA of modern companies
- 1.3.1 AI as a transformative factor for companies
- 1.3.2 The benefits of AI: innovation meets efficiency
- 1.4 Relevance for SAP users
- 1.5 Summary
- 2 SAP's AI toolkit: An overview of the SAP product landscape
- 2.1 SAP BTP: The foundation for AI projects in the SAP ecosystem
- 2.1.1 SAP Business AI
- 2.1.2 SAP AI Services
- 2.1.3 SAP AI Core and SAP AI Launchpad
- 2.1.4 Generative AI Hub
- 2.2 Joule: The Intelligent Assistant
- 2.2.1 What is Joule?
- 2.2.2 How does Joule work?
- 2.2.3 Grounding mechanisms: How Joule embeds specific domain knowledge
- 2.3 Synergies between AI and other technologies
- 2.3.1 Process mining and AI
- 2.3.2 Robotic process automation and AI
- 2.3.3 SAP Analytics Cloud and AI
- 2.3.4 Interaction of process mining, RPA, and SAP Analytics Cloud with AI
- 2.4 Summary
- 3 System Landscapes and Their Consequences: Public, Private, and On-Premise
- 3.1 Introduction to system landscapes
- 3.1.1 Architecture of SAP S/4HANA versions and SAP BTP
- 3.1.2 Advantages and disadvantages for AI applications
- 3.2 Challenges in AI integration
- 3.2.1 Technical challenges of AI integration and their solutions
- 3.2.2 Organizational challenges of AI integration and their solutions
- 3.3 Tools and technologies for integrating AI solutions
- 3.4 Case studies
- 3.4.1 Case study 1: AI-supported predictive maintenance in the manufacturing industry
- 3.4.2 Case study 2: AI-supported customer analysis in retail
- 3.4.3 Case study 3: Automated invoice processing with AI in a financial services company
- 3.4.4 Case study 4: AI-driven automation in healthcare
- 3.5 Decision-making: The right architecture for your AI solution
- 3.5.1 Hyperscalers and their significance for AI architectures
- 3.5.2 Operating costs of SAP BTP
- 3.6 Summary
- PART II AI use cases
- 4 Anomaly detection in financial transactions
- 4.1 Introduction and objectives
- 4.2 Creating the right conditions
- 4.2.1 Selecting and preparing data
- 4.2.2 Defining Normal and Anomalous
- 4.2.3 Selecting the appropriate algorithm
- 4.3 Technical implementation
- 4.3.1 Implementing Isolation Forest
- 4.3.2 Develop API
- 4.3.3 Preparing the Docker environment for training and deploying the API
- 4.3.4 Creating automated AI workflows with ArgoFlows in SAP AI Core
- 4.3.5 Deploy AI services with KServe and SAP AI Core
- 4.4 Deploy the AI solution on SAP BTP
- 4.4.1 Administrative basics in SAP AI Core
- 4.4.2 Registering the application
- 4.4.3 Configuring the scenario
- 4.4.4 Executing the workflow
- 4.4.5 Implementing the model
- 4.4.6 Integrating the model into new or existing applications via an API
- 4.5 Summary
- 5 Analysis and optimization of transport routes
- 5.1 Introduction and objectives
- 5.2 Creating the right conditions
- 5.2.1 AI agents
- 5.2.2 Joule
- 5.2.3 Generative AI Hub and Generative AI Hub SDK
- 5.3 Technical implementation
- 5.3.1 Selection and integration of the LLM
- 5.3.2 Implementation of the AI Agent
- 5.4 The agent in practical application
- 5.5 Summary
- 6 Automated invoice verification
- 6.1 Introduction and objectives
- 6.2 Creating the right conditions: SAP Document AI
- 6.3 Technical implementation
- 6.3.1 Automated information extraction with standard schemas
- 6.3.2 Custom Schemas
- 6.3.3 Automated information extraction with custom schemas
- 6.3.4 Using SAP Document AI via API
- 6.4 Summary
- 7 Inventory with AI
- 7.1 Introduction and objectives
- 7.2 Creating the right conditions
- 7.3 Technical implementation
- 7.3.1 Backend – Application logic and integration
- 7.3.2 Frontend – User interface in familiar SAP design
- 7.3.3 AI components – Intelligent image analysis for inventory
- 7.3.4 Step by step through the inventory app
- 7.4 Summary
- 8 AI in goods receipt and goods inspection
- 8.1 Introduction and objectives
- 8.2 Creating the right conditions
- 8.2.1 Hardware and network connection
- 8.2.2 SAP solutions and integrations
- 8.2.3 AI and training data
- 8.2.4 Necessary Extensions and Support
- 8.3 Technical implementation
- 8.3.1 Creating a destination in SAP BTP
- 8.3.2 Using services in the app to create a delivery
- 8.4 Summary
- 9 Custom chatbots with RAG
- 9.1 Introduction and objectives
- 9.2 Creating the right conditions
- 9.3 Technical implementation
- 9.3.1 Configuring SAP BTP
- 9.3.2 From workflow template to solution
- 9.4 Summary
- 10 Forecasting sales and inventory levels
- 10.1 Introduction and objectives
- 10.2 Creating the Right Conditions: SAP Analytics Cloud for Forecasting
- 10.2.1 Software stack
- 10.2.2 Architecture and integration
- 10.3 Technical implementation
- 10.3.1 Data modeling
- 10.3.2 Integration into operational processes
- 10.4 Results and potential uses
- 10.5 Summary
- 11 AI as an accelerator in software development
- 11.1 Introduction and objectives
- 11.2 Creating the right conditions
- 11.3 Technical implementation
- 11.3.1 Laying the foundation in SAP BTP
- 11.3.2 Setting up the first project
- 11.3.3 Developing the project
- 11.4 Summary
- 12 Predictive Maintenance: Real-Time Insights Through Live Data Connections
- 12.1 Introduction and objectives
- 12.2 Creating the right conditions
- 12.2.1 SAP Analytics Cloud for Predictive Maintenance
- 12.2.2 Live data connection and data sources
- 12.2.3 Architectural concept of SAP Analytics Cloud
- 12.3 Technical Implementation
- 12.3.1 Model structure: Predictive analytics with Smart Predict
- 12.3.2 Operational Integration into Maintenance Processes
- 12.4 Organizational Impact and Change Management
- 12.5 Explainable AI
- 12.6 Risks and success factors
- 12.7 Results and potential applications
- 12.8 Summary
- PART III Considerations before starting your AI project
- 13 Implementation Strategy for AI with SAP
- 13.1 How do I start an AI project?
- 13.1.1 Project preparation and goal definition
- 13.1.2 Prerequisites: Data, Technologies, Teams
- 13.1.3 Strategic aspects of AI implementation
- 13.2 Best practices for implementation
- 13.2.1 Process models
- 13.2.2 CRISP-DM process and MLOps
- 13.2.3 Integration into existing systems
- 13.2.4 Training and acceptance
- 13.2.5 Performance measurement of AI implementations
- 13.3 Typical mistakes and how to avoid them
- 13.3.1 Lack of objectives and strategic integration
- 13.3.2 Poor data quality
- 13.3.3 Silo mentality and lack of collaboration
- 13.3.4 Unnecessary complexity and lack of scalability
- 13.3.5 Distorted and non-transparent AI
- 13.3.6 Unrealistic expectations and cost traps
- 13.3.7 Resistance to change
- 13.3.8 Security risks and technical debt
- 13.3.9 Lack of performance measurement
- 13.3.10 Lack of governance
- 13.4 Summary
- 14 A look into the future
- 14.1 Trends in AI research and their implications for ERP systems
- 14.1.1 Language models, process automation, and data analysis
- 14.1.2 Explainable AI, transparency, and human-AI collaboration
- 14.2 Recommendations for decision-makers
- 14.3 Summary
- Appendix
- A AI Use Case Canvas
- B Checklist: Is your company ready for AI?
- C Checklist: How to successfully plan and implement AI projects
- D Complete code example from Chapter 9
- The Authors
- Index
AI Business Cases mit SAP
Wie kann Ihr Unternehmen von KI profitieren? Dieses Buch liefert anhand von neun praxisnahen Business Cases Antworten und zeigt, wie Sie Ihre Geschäftsprozesse mit KI optimieren. Von der Inventur über die Rechnungsprüfung bis zur Softwareentwicklung – René Kessler und Marcel Kuchler erklären, wie Sie KI-Projekte mit SAP-Technologien wie der SAP Business Technology Platform, SAP AI Core und Joule umsetzen. Erfahren Sie, welche Voraussetzungen Sie schaffen müssen und wie Sie typische Fehler vermeiden.
- Vielfältige Anwendungsfälle aus verschiedenen Fachbereichen
- Tools und Technologien für Ihr eigenes KI-Projekt
- Inkl. SAP Business AI, SAP AI Core und SAP Generative AI Hub
Du lernst etwas über:
- Grundlagen und Potenziale von KI:
Die Autoren erklären die KI-Grundlagen, welche Disziplinen es gibt und wie Unternehmen davon profitieren können. Sie zeigen auf, wie KI-Technologien genutzt werden, um Geschäftsprozesse intelligenter, effizienter und zukunftsfähiger zu gestalten.
- Der KI-Baukasten von SAP:
Lernen Sie die KI-Tools von SAP kennen: Von der SAP Business Technology Platform über vordefinierte KI-Funktionen in SAP Business AI bis hin zu individuellen Lösungen mit den AI Services – dieses Buch gibt einen kompletten Überblick.
- Neun Anwendungsfälle aus der Praxis:
Anhand konkreter Beispiele aus Bereichen wie Logistik, Finanzwesen und Softwareentwicklung zeigen die Autoren, wie Unternehmen KI gewinnbringend einsetzen, mit allen Details zu Zielsetzung, technischen Voraussetzungen und Umsetzung. Erfahren Sie auch, wie Sie Ihr KI-Projekt compliant und sicher umsetzen.
Aus dem Inhalt:
- KI-Grundlagen
- SAP Business Technology Platform
- SAP AI Core
- Joule
- Systemvoraussetzungen
- Umsetzungsstrategien
- Security und Compliance
Komplettes Inhaltsverzeichnis
