Mastering SAP S4 HANA Real-Time Analytics and AI Integration
You're under pressure to deliver faster insights, smarter forecasts, and real-time visibility-yet your current tools feel outdated, your data siloed, and your team overstretched. The board wants AI-driven decisions, but you're stuck bridging legacy systems and modern demands with no clear path forward. Uncertainty is costing you credibility. Every month without real-time analytics means missed opportunities, slower response times, and reactive strategies in a world that rewards speed and precision. You’re not alone-many SAP professionals are in the same position, waiting for the right breakthrough that bridges the gap between complexity and control. Mastering SAP S4 HANA Real-Time Analytics and AI Integration isn't just another course. It’s the exact blueprint used by top-tier consultants to transform fragmented data into boardroom-ready intelligence, with a structured 30-day pathway from concept to a fully scoped, stakeholder-approved AI use case for your organisation. Take Anita Patel, Senior Finance Systems Architect at a Fortune 500 manufacturing firm. After completing this program, she led the deployment of an AI-powered cash flow forecasting module that reduced planning errors by 67% and delivered real-time liquidity insights to the CFO within four weeks of implementation. Her project is now a benchmark across three divisions. This course gives you the clarity, confidence, and certified expertise to lead high-impact digital transformations. You’ll move from feeling overwhelmed by SAP analytics complexity to being the person who simplifies it, governs it, and leverages it for competitive advantage. No guesswork. No fluff. Just proven frameworks, actionable exercises, and a direct line to measurable business outcomes. Here’s how this course is structured to help you get there.Course Format & Delivery Details Self-Paced, On-Demand, Always Accessible
This is a self-paced learning experience with immediate online access upon enrollment. You choose when and where you study, with no fixed dates, deadlines, or time commitments. Most learners complete the core content in 25–30 hours and begin applying insights to real projects within the first week. The entire course is delivered in a mobile-friendly format, fully compatible across devices-ideal for professionals managing demanding schedules across geographies and time zones. Access your materials 24/7, whether you're on-site, in transit, or preparing for a critical meeting. Lifetime Access & Continuous Updates
You receive lifetime access to all course materials. This includes every future update at no additional cost. As SAP S4 HANA evolves, so does your knowledge. We regularly refresh content to reflect new integrations, AI capabilities, and best practices-ensuring your certification remains current and valuable for years. Your learning journey never expires. Revisit modules before audits, renewals, or implementation rollouts. Use the curriculum as a living reference guide throughout your career. Expert-Led Guidance & Support
While the course is self-directed, you are not alone. You’ll have direct access to SAP-certified instructors via structured guidance pathways, including detailed feedback loops, scenario-based consultations, and priority response channels. Expect clarity when you need it-with no reliance on live meetings or attendance. Support is built into the architecture of the course, ensuring you move forward confidently, even when tackling complex integration challenges or stakeholder alignment issues. Global Recognition: Certificate of Completion by The Art of Service
Upon finishing the course, you will earn a formal Certificate of Completion issued by The Art of Service-a globally recognised authority in enterprise technology training. This certificate is verifiable, professional, and designed to enhance your credibility with employers, clients, and internal stakeholders. It signals mastery of SAP S4 HANA analytics and AI integration in a way that resonates with hiring managers, audit teams, and digital transformation leads across industries. Transparent Pricing, No Hidden Fees
The total cost of the course is straightforward with zero hidden fees. What you see is what you pay-no surprise charges, no subscription traps, no upsells. The investment covers full access, all updates, certification, and support. We accept major payment methods, including Visa, Mastercard, and PayPal, with secure processing to protect your information at every step. Zero-Risk Enrollment: Satisfied or Refunded Guarantee
We back this course with a 30-day “Satisfied or Refunded” guarantee. If you complete the first two modules and feel the content isn’t delivering value, clarity, or career ROI, simply request a full refund-no questions asked. Your risk is eliminated. Immediate Confirmation & Secure Access
After enrollment, you’ll receive an automated confirmation email. Your access credentials and course entry details will be sent separately once your learner profile is activated and materials are ready for you-ensuring a secure, personalised onboarding experience. This Works Even If…
…you’re new to AI integration, this works because the course starts with foundational real-time analytics before layering in intelligent automation. …your organisation hasn’t fully migrated to S4 HANA yet, this works because we include migration alignment checkpoints and hybrid system strategies used by global SI partners. …you’re not in IT or development, this works because we’ve built it for cross-functional leaders-finance, supply chain, operations, and project managers-who need to lead, not code. Social Proof: I was leading an S/4HANA upgrade with no analytics roadmap until I took this course. Within 20 days, I delivered a real-time inventory optimisation dashboard using embedded AI that reduced excess stock by $4.2M annually. This is the missing manual for SAP transformation. – Mark T., Senior Supply Chain Director, Automotive Sector Your success is not left to chance. We’ve removed friction, reduced risk, and built trust into every element of this offering-so you can act with confidence.
Module 1: Foundations of SAP S4 HANA Analytics Architecture - Understanding the S4 HANA data model and its analytical core
- Role of the Universal Journal in real-time reporting
- Key differences between ECC and S4 HANA analytics capabilities
- Overview of embedded analytics vs. separate BI systems
- Introduction to CDS Views and their function in real-time data access
- How Fiori enables role-based analytics consumption
- Navigating the S4 HANA Analytics Cloud integration layer
- Data volume management and performance optimisation principles
- Security and authorisation models for analytical access
- Setting up your first real-time KPI dashboard in Fiori
Module 2: Real-Time Data Modelling & CDS View Development - Core concepts of Core Data Services (CDS) in S4 HANA
- Designing analytical CDS views with aggregation and calculation
- Using annotations to expose data to analytical tools
- Joining multiple data sources within a single CDS view
- Extending standard SAP CDS views with custom fields
- Best practices for performance-tuned view design
- Implementing filters and input parameters for dynamic reporting
- Creating time-based aggregations and period comparisons
- Linking CDS views to hierarchical master data
- Version control and transport management for custom views
Module 3: SAP Analytics Cloud Integration Strategy - Connecting S4 HANA to SAP Analytics Cloud (SAC)
- Direct versus replicated data connections
- Using live data models for real-time insight delivery
- Configuring OAuth and SSO for secure SAC access
- Building responsive planning models in SAC
- Designing storyboards for executive consumption
- Integrating predictive scenarios into live dashboards
- Managing data refresh strategies for time-sensitive KPIs
- Role-based access control in SAC stories and models
- Automating report distribution and alerts in SAC
Module 4: Real-Time Operational Reporting with Embedded Analytics - Using transactional apps with real-time analytics tiles
- Configuring KPIs in Manage KPI App and Tile Catalog
- Creating custom analytical queries using Query Browser
- Linking operational processes to performance metrics
- Real-time order-to-cash tracking and anomaly detection
- Inventory movement analytics with live stock visibility
- Procurement cycle-time reporting and bottleneck analysis
- Production variance tracking in discrete manufacturing
- Time-based profitability reporting by segment
- Customising embedded analytics for local compliance needs
Module 5: AI and Machine Learning Integration Frameworks - Overview of SAP’s AI portfolio: Leonardo, Joule, and embedded ML
- Understanding Intelligent Robotic Process Automation (IRPA)
- Identifying use cases for predictive analytics in S4 HANA
- Overview of pre-packaged AI scenarios in finance and logistics
- Using the Embedded Machine Learning API framework
- Calling external AI models via REST in S4 HANA
- Setting up confidence thresholds and exception handling
- Data requirements for training and inference in SAP systems
- AI governance and model lifecycle management
- Monitoring model drift and retraining triggers
Module 6: Predictive Material and Demand Planning - Enabling predictive material coverage in MRP
- Configuring the Forecast and Reorder Point application
- Integrating historical data with seasonality detection
- Using AI to detect demand spikes and suppress noise
- Setting dynamic safety stock levels based on prediction
- Linking forecasting models to procurement automation
- Validating forecast accuracy with backtesting scenarios
- Adjusting lead time variables using predictive insights
- Handling discontinuation and new product introductions
- Exporting predictive results to SAC for executive review
Module 7: AI-Driven Financial Forecasting and Close Automation - Automating intercompany reconciliation with AI matching
- Using cash flow prediction models for liquidity planning
- AI-based anomaly detection in general ledger entries
- Accelerating financial close with predictive variance analysis
- Forecasting accounts receivable collections using payment history
- Dynamic provisioning for doubtful debts using ML models
- Automating journal entry suggestions with natural language processing
- Linking predictive results to document journal entries
- Integrating AI insights into group reporting packages
- Creating board-ready forecast dashboards with confidence intervals
Module 8: Intelligent Invoice and Payment Processing - Enabling SAP Invoice Management with AI capabilities
- Using optical character recognition with machine learning
- Automating vendor invoice matching using three-way checks
- Handling exceptions with intelligent routing and escalation
- Learning from user approvals to improve future accuracy
- Integrating with Ariba and Concur for end-to-end flows
- AI-based duplicate invoice detection and prevention
- Reducing manual intervention in high-volume transactions
- Performance metrics for invoice process optimisation
- Exporting AI-processed data to audit trails and analytics
Module 9: Real-Time Supply Chain Monitoring and AI Alerts - Setting up supply chain exception monitoring in S4 HANA
- Creating custom alert rules for delivery delays
- Using predictive lead time modelling for procurement
- AI-based disruption risk scoring for vendors
- Monitoring inbound quality with predictive defect alerts
- Automating escalation workflows for critical anomalies
- Linking warehouse execution data to real-time visibility
- Building supplier performance dashboards with AI weights
- Proactive stockout prevention using consumption trends
- Integrating transportation data for end-to-end tracking
Module 10: Advanced Predictive Quality and Maintenance - Enabling predictive maintenance in SAP Plant Maintenance
- Integrating IoT sensor data with S4 HANA
- Setting up equipment failure probability models
- Automating work order creation based on predictive scores
- Linking maintenance predictions to spare parts availability
- Using historical repair data to train ML models
- Creating risk-ranked maintenance schedules
- Monitoring quality deviations in real-time production
- AI-based root cause suggestions for defects
- Exporting maintenance insights to operations dashboards
Module 11: Custom AI Integration Using SAP BTP - Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Understanding the S4 HANA data model and its analytical core
- Role of the Universal Journal in real-time reporting
- Key differences between ECC and S4 HANA analytics capabilities
- Overview of embedded analytics vs. separate BI systems
- Introduction to CDS Views and their function in real-time data access
- How Fiori enables role-based analytics consumption
- Navigating the S4 HANA Analytics Cloud integration layer
- Data volume management and performance optimisation principles
- Security and authorisation models for analytical access
- Setting up your first real-time KPI dashboard in Fiori
Module 2: Real-Time Data Modelling & CDS View Development - Core concepts of Core Data Services (CDS) in S4 HANA
- Designing analytical CDS views with aggregation and calculation
- Using annotations to expose data to analytical tools
- Joining multiple data sources within a single CDS view
- Extending standard SAP CDS views with custom fields
- Best practices for performance-tuned view design
- Implementing filters and input parameters for dynamic reporting
- Creating time-based aggregations and period comparisons
- Linking CDS views to hierarchical master data
- Version control and transport management for custom views
Module 3: SAP Analytics Cloud Integration Strategy - Connecting S4 HANA to SAP Analytics Cloud (SAC)
- Direct versus replicated data connections
- Using live data models for real-time insight delivery
- Configuring OAuth and SSO for secure SAC access
- Building responsive planning models in SAC
- Designing storyboards for executive consumption
- Integrating predictive scenarios into live dashboards
- Managing data refresh strategies for time-sensitive KPIs
- Role-based access control in SAC stories and models
- Automating report distribution and alerts in SAC
Module 4: Real-Time Operational Reporting with Embedded Analytics - Using transactional apps with real-time analytics tiles
- Configuring KPIs in Manage KPI App and Tile Catalog
- Creating custom analytical queries using Query Browser
- Linking operational processes to performance metrics
- Real-time order-to-cash tracking and anomaly detection
- Inventory movement analytics with live stock visibility
- Procurement cycle-time reporting and bottleneck analysis
- Production variance tracking in discrete manufacturing
- Time-based profitability reporting by segment
- Customising embedded analytics for local compliance needs
Module 5: AI and Machine Learning Integration Frameworks - Overview of SAP’s AI portfolio: Leonardo, Joule, and embedded ML
- Understanding Intelligent Robotic Process Automation (IRPA)
- Identifying use cases for predictive analytics in S4 HANA
- Overview of pre-packaged AI scenarios in finance and logistics
- Using the Embedded Machine Learning API framework
- Calling external AI models via REST in S4 HANA
- Setting up confidence thresholds and exception handling
- Data requirements for training and inference in SAP systems
- AI governance and model lifecycle management
- Monitoring model drift and retraining triggers
Module 6: Predictive Material and Demand Planning - Enabling predictive material coverage in MRP
- Configuring the Forecast and Reorder Point application
- Integrating historical data with seasonality detection
- Using AI to detect demand spikes and suppress noise
- Setting dynamic safety stock levels based on prediction
- Linking forecasting models to procurement automation
- Validating forecast accuracy with backtesting scenarios
- Adjusting lead time variables using predictive insights
- Handling discontinuation and new product introductions
- Exporting predictive results to SAC for executive review
Module 7: AI-Driven Financial Forecasting and Close Automation - Automating intercompany reconciliation with AI matching
- Using cash flow prediction models for liquidity planning
- AI-based anomaly detection in general ledger entries
- Accelerating financial close with predictive variance analysis
- Forecasting accounts receivable collections using payment history
- Dynamic provisioning for doubtful debts using ML models
- Automating journal entry suggestions with natural language processing
- Linking predictive results to document journal entries
- Integrating AI insights into group reporting packages
- Creating board-ready forecast dashboards with confidence intervals
Module 8: Intelligent Invoice and Payment Processing - Enabling SAP Invoice Management with AI capabilities
- Using optical character recognition with machine learning
- Automating vendor invoice matching using three-way checks
- Handling exceptions with intelligent routing and escalation
- Learning from user approvals to improve future accuracy
- Integrating with Ariba and Concur for end-to-end flows
- AI-based duplicate invoice detection and prevention
- Reducing manual intervention in high-volume transactions
- Performance metrics for invoice process optimisation
- Exporting AI-processed data to audit trails and analytics
Module 9: Real-Time Supply Chain Monitoring and AI Alerts - Setting up supply chain exception monitoring in S4 HANA
- Creating custom alert rules for delivery delays
- Using predictive lead time modelling for procurement
- AI-based disruption risk scoring for vendors
- Monitoring inbound quality with predictive defect alerts
- Automating escalation workflows for critical anomalies
- Linking warehouse execution data to real-time visibility
- Building supplier performance dashboards with AI weights
- Proactive stockout prevention using consumption trends
- Integrating transportation data for end-to-end tracking
Module 10: Advanced Predictive Quality and Maintenance - Enabling predictive maintenance in SAP Plant Maintenance
- Integrating IoT sensor data with S4 HANA
- Setting up equipment failure probability models
- Automating work order creation based on predictive scores
- Linking maintenance predictions to spare parts availability
- Using historical repair data to train ML models
- Creating risk-ranked maintenance schedules
- Monitoring quality deviations in real-time production
- AI-based root cause suggestions for defects
- Exporting maintenance insights to operations dashboards
Module 11: Custom AI Integration Using SAP BTP - Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Connecting S4 HANA to SAP Analytics Cloud (SAC)
- Direct versus replicated data connections
- Using live data models for real-time insight delivery
- Configuring OAuth and SSO for secure SAC access
- Building responsive planning models in SAC
- Designing storyboards for executive consumption
- Integrating predictive scenarios into live dashboards
- Managing data refresh strategies for time-sensitive KPIs
- Role-based access control in SAC stories and models
- Automating report distribution and alerts in SAC
Module 4: Real-Time Operational Reporting with Embedded Analytics - Using transactional apps with real-time analytics tiles
- Configuring KPIs in Manage KPI App and Tile Catalog
- Creating custom analytical queries using Query Browser
- Linking operational processes to performance metrics
- Real-time order-to-cash tracking and anomaly detection
- Inventory movement analytics with live stock visibility
- Procurement cycle-time reporting and bottleneck analysis
- Production variance tracking in discrete manufacturing
- Time-based profitability reporting by segment
- Customising embedded analytics for local compliance needs
Module 5: AI and Machine Learning Integration Frameworks - Overview of SAP’s AI portfolio: Leonardo, Joule, and embedded ML
- Understanding Intelligent Robotic Process Automation (IRPA)
- Identifying use cases for predictive analytics in S4 HANA
- Overview of pre-packaged AI scenarios in finance and logistics
- Using the Embedded Machine Learning API framework
- Calling external AI models via REST in S4 HANA
- Setting up confidence thresholds and exception handling
- Data requirements for training and inference in SAP systems
- AI governance and model lifecycle management
- Monitoring model drift and retraining triggers
Module 6: Predictive Material and Demand Planning - Enabling predictive material coverage in MRP
- Configuring the Forecast and Reorder Point application
- Integrating historical data with seasonality detection
- Using AI to detect demand spikes and suppress noise
- Setting dynamic safety stock levels based on prediction
- Linking forecasting models to procurement automation
- Validating forecast accuracy with backtesting scenarios
- Adjusting lead time variables using predictive insights
- Handling discontinuation and new product introductions
- Exporting predictive results to SAC for executive review
Module 7: AI-Driven Financial Forecasting and Close Automation - Automating intercompany reconciliation with AI matching
- Using cash flow prediction models for liquidity planning
- AI-based anomaly detection in general ledger entries
- Accelerating financial close with predictive variance analysis
- Forecasting accounts receivable collections using payment history
- Dynamic provisioning for doubtful debts using ML models
- Automating journal entry suggestions with natural language processing
- Linking predictive results to document journal entries
- Integrating AI insights into group reporting packages
- Creating board-ready forecast dashboards with confidence intervals
Module 8: Intelligent Invoice and Payment Processing - Enabling SAP Invoice Management with AI capabilities
- Using optical character recognition with machine learning
- Automating vendor invoice matching using three-way checks
- Handling exceptions with intelligent routing and escalation
- Learning from user approvals to improve future accuracy
- Integrating with Ariba and Concur for end-to-end flows
- AI-based duplicate invoice detection and prevention
- Reducing manual intervention in high-volume transactions
- Performance metrics for invoice process optimisation
- Exporting AI-processed data to audit trails and analytics
Module 9: Real-Time Supply Chain Monitoring and AI Alerts - Setting up supply chain exception monitoring in S4 HANA
- Creating custom alert rules for delivery delays
- Using predictive lead time modelling for procurement
- AI-based disruption risk scoring for vendors
- Monitoring inbound quality with predictive defect alerts
- Automating escalation workflows for critical anomalies
- Linking warehouse execution data to real-time visibility
- Building supplier performance dashboards with AI weights
- Proactive stockout prevention using consumption trends
- Integrating transportation data for end-to-end tracking
Module 10: Advanced Predictive Quality and Maintenance - Enabling predictive maintenance in SAP Plant Maintenance
- Integrating IoT sensor data with S4 HANA
- Setting up equipment failure probability models
- Automating work order creation based on predictive scores
- Linking maintenance predictions to spare parts availability
- Using historical repair data to train ML models
- Creating risk-ranked maintenance schedules
- Monitoring quality deviations in real-time production
- AI-based root cause suggestions for defects
- Exporting maintenance insights to operations dashboards
Module 11: Custom AI Integration Using SAP BTP - Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Overview of SAP’s AI portfolio: Leonardo, Joule, and embedded ML
- Understanding Intelligent Robotic Process Automation (IRPA)
- Identifying use cases for predictive analytics in S4 HANA
- Overview of pre-packaged AI scenarios in finance and logistics
- Using the Embedded Machine Learning API framework
- Calling external AI models via REST in S4 HANA
- Setting up confidence thresholds and exception handling
- Data requirements for training and inference in SAP systems
- AI governance and model lifecycle management
- Monitoring model drift and retraining triggers
Module 6: Predictive Material and Demand Planning - Enabling predictive material coverage in MRP
- Configuring the Forecast and Reorder Point application
- Integrating historical data with seasonality detection
- Using AI to detect demand spikes and suppress noise
- Setting dynamic safety stock levels based on prediction
- Linking forecasting models to procurement automation
- Validating forecast accuracy with backtesting scenarios
- Adjusting lead time variables using predictive insights
- Handling discontinuation and new product introductions
- Exporting predictive results to SAC for executive review
Module 7: AI-Driven Financial Forecasting and Close Automation - Automating intercompany reconciliation with AI matching
- Using cash flow prediction models for liquidity planning
- AI-based anomaly detection in general ledger entries
- Accelerating financial close with predictive variance analysis
- Forecasting accounts receivable collections using payment history
- Dynamic provisioning for doubtful debts using ML models
- Automating journal entry suggestions with natural language processing
- Linking predictive results to document journal entries
- Integrating AI insights into group reporting packages
- Creating board-ready forecast dashboards with confidence intervals
Module 8: Intelligent Invoice and Payment Processing - Enabling SAP Invoice Management with AI capabilities
- Using optical character recognition with machine learning
- Automating vendor invoice matching using three-way checks
- Handling exceptions with intelligent routing and escalation
- Learning from user approvals to improve future accuracy
- Integrating with Ariba and Concur for end-to-end flows
- AI-based duplicate invoice detection and prevention
- Reducing manual intervention in high-volume transactions
- Performance metrics for invoice process optimisation
- Exporting AI-processed data to audit trails and analytics
Module 9: Real-Time Supply Chain Monitoring and AI Alerts - Setting up supply chain exception monitoring in S4 HANA
- Creating custom alert rules for delivery delays
- Using predictive lead time modelling for procurement
- AI-based disruption risk scoring for vendors
- Monitoring inbound quality with predictive defect alerts
- Automating escalation workflows for critical anomalies
- Linking warehouse execution data to real-time visibility
- Building supplier performance dashboards with AI weights
- Proactive stockout prevention using consumption trends
- Integrating transportation data for end-to-end tracking
Module 10: Advanced Predictive Quality and Maintenance - Enabling predictive maintenance in SAP Plant Maintenance
- Integrating IoT sensor data with S4 HANA
- Setting up equipment failure probability models
- Automating work order creation based on predictive scores
- Linking maintenance predictions to spare parts availability
- Using historical repair data to train ML models
- Creating risk-ranked maintenance schedules
- Monitoring quality deviations in real-time production
- AI-based root cause suggestions for defects
- Exporting maintenance insights to operations dashboards
Module 11: Custom AI Integration Using SAP BTP - Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Automating intercompany reconciliation with AI matching
- Using cash flow prediction models for liquidity planning
- AI-based anomaly detection in general ledger entries
- Accelerating financial close with predictive variance analysis
- Forecasting accounts receivable collections using payment history
- Dynamic provisioning for doubtful debts using ML models
- Automating journal entry suggestions with natural language processing
- Linking predictive results to document journal entries
- Integrating AI insights into group reporting packages
- Creating board-ready forecast dashboards with confidence intervals
Module 8: Intelligent Invoice and Payment Processing - Enabling SAP Invoice Management with AI capabilities
- Using optical character recognition with machine learning
- Automating vendor invoice matching using three-way checks
- Handling exceptions with intelligent routing and escalation
- Learning from user approvals to improve future accuracy
- Integrating with Ariba and Concur for end-to-end flows
- AI-based duplicate invoice detection and prevention
- Reducing manual intervention in high-volume transactions
- Performance metrics for invoice process optimisation
- Exporting AI-processed data to audit trails and analytics
Module 9: Real-Time Supply Chain Monitoring and AI Alerts - Setting up supply chain exception monitoring in S4 HANA
- Creating custom alert rules for delivery delays
- Using predictive lead time modelling for procurement
- AI-based disruption risk scoring for vendors
- Monitoring inbound quality with predictive defect alerts
- Automating escalation workflows for critical anomalies
- Linking warehouse execution data to real-time visibility
- Building supplier performance dashboards with AI weights
- Proactive stockout prevention using consumption trends
- Integrating transportation data for end-to-end tracking
Module 10: Advanced Predictive Quality and Maintenance - Enabling predictive maintenance in SAP Plant Maintenance
- Integrating IoT sensor data with S4 HANA
- Setting up equipment failure probability models
- Automating work order creation based on predictive scores
- Linking maintenance predictions to spare parts availability
- Using historical repair data to train ML models
- Creating risk-ranked maintenance schedules
- Monitoring quality deviations in real-time production
- AI-based root cause suggestions for defects
- Exporting maintenance insights to operations dashboards
Module 11: Custom AI Integration Using SAP BTP - Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Setting up supply chain exception monitoring in S4 HANA
- Creating custom alert rules for delivery delays
- Using predictive lead time modelling for procurement
- AI-based disruption risk scoring for vendors
- Monitoring inbound quality with predictive defect alerts
- Automating escalation workflows for critical anomalies
- Linking warehouse execution data to real-time visibility
- Building supplier performance dashboards with AI weights
- Proactive stockout prevention using consumption trends
- Integrating transportation data for end-to-end tracking
Module 10: Advanced Predictive Quality and Maintenance - Enabling predictive maintenance in SAP Plant Maintenance
- Integrating IoT sensor data with S4 HANA
- Setting up equipment failure probability models
- Automating work order creation based on predictive scores
- Linking maintenance predictions to spare parts availability
- Using historical repair data to train ML models
- Creating risk-ranked maintenance schedules
- Monitoring quality deviations in real-time production
- AI-based root cause suggestions for defects
- Exporting maintenance insights to operations dashboards
Module 11: Custom AI Integration Using SAP BTP - Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Overview of SAP Business Technology Platform (BTP) architecture
- Setting up a BTP subaccount for AI development
- Connecting S4 HANA to BTP using Cloud Connector
- Deploying custom machine learning models using Python
- Using SAP AI Core for model orchestration
- Building APIs to expose AI predictions to S4 HANA
- Securing AI microservices with OAuth and IAM roles
- Monitoring AI service performance and uptime
- Versioning and rolling back AI models in production
- Cost optimisation strategies for BTP-based AI
Module 12: Data Preparation and Feature Engineering for AI - Identifying relevant data sources for predictive modelling
- Cleaning and transforming transactional data for training
- Engineering time-based features from operational data
- Creating lag variables and rolling averages for forecasting
- Handling missing values and outliers in SAP datasets
- Normalising data for ML model compatibility
- Segmenting customers, products, or vendors for targeted models
- Using master data attributes as model features
- Exporting training datasets securely via SAP export tools
- Validating data quality before model training
Module 13: AI Model Validation, Testing, and Governance - Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Defining success criteria for business-aligned AI
- Splitting data into training, validation, and test sets
- Measuring model performance using precision, recall, and F1
- Interpreting SHAP values for model explainability
- Ensuring fairness and bias detection in AI outputs
- Documenting model assumptions and data lineage
- Creating audit trails for regulatory compliance
- Setting up retraining schedules based on data drift
- Monitoring model performance degradation over time
- Establishing AI governance committees and approval workflows
Module 14: Integration of Generative AI in SAP Workflows - Understanding SAP’s generative AI strategy with Joule
- Using natural language queries to retrieve real-time data
- Generating draft reports and summaries from analytics
- Creating dynamic commentary for board presentations
- Automating routine email responses using AI drafts
- Integrating Joule into SAP Fiori for contextual assistance
- Setting up secure prompts and guardrails for AI responses
- Using generative AI for training documentation generation
- Customising templates for consistent output
- Measuring time savings from AI-assisted reporting
Module 15: Cross-Functional AI Use Case Development - Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Mapping business pain points to AI solution categories
- Using the AI Use Case Canvas for stakeholder alignment
- Identifying quick-win versus strategic AI initiatives
- Building financial justification models for AI projects
- Creating RACI matrices for cross-team ownership
- Defining KPIs and success metrics for AI pilots
- Prototyping use cases in sandbox environments
- Conducting user acceptance testing with real data
- Gathering feedback for iterative improvement
- Developing rollout playbooks for enterprise scaling
Module 16: Change Management and Stakeholder Enablement - Overcoming resistance to AI-driven decision making
- Designing communication plans for AI transparency
- Training end users on interpreting AI outputs
- Creating role-based playbooks for AI adoption
- Setting up feedback loops for continuous improvement
- Managing data literacy across finance, procurement, and logistics
- Leading cross-departmental AI task forces
- Documenting lessons learned from pilot implementations
- Building a culture of data-driven accountability
- Measuring user adoption and engagement rates
Module 17: Real-Time KPI Design and Executive Storytelling - Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Selecting KPIs that drive action, not just visibility
- Designing executive dashboards with clear narrative flow
- Using visual hierarchy to guide stakeholder attention
- Creating before-and-after impact stories for AI projects
- Linking operational metrics to strategic goals
- Building dynamic commentary into dashboards
- Using colour psychology for effective data presentation
- Incorporating predictive confidence ranges in forecasts
- Automating quarterly business review packs
- Exporting board-ready presentations from SAC
Module 18: Performance Optimisation and Scalability - Tuning CDS views for high-volume queries
- Using aggregates and materialised views for speed
- Monitoring system performance during peak loads
- Scaling AI integrations across multiple clients
- Optimising data transfer between S4 HANA and BTP
- Using SAP HANA calculation scenarios for speed
- Implementing caching strategies for dashboards
- Load testing AI-enabled workflows under stress
- Monitoring memory and CPU usage in real-time analytics
- Planning for multi-tenancy and global rollouts
Module 19: Audit, Compliance, and Data Governance - Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers
Module 20: Capstone Implementation & Certification - Completing a real-world AI integration project using your data
- Structuring a board-ready proposal with ROI analysis
- Presenting your use case using professional templates
- Submitting your project for expert review
- Receiving feedback and refinement suggestions
- Finalising your implementation plan
- Earning your Certificate of Completion from The Art of Service
- Accessing the alumni community for ongoing support
- Adding your certification to LinkedIn and professional profiles
- Preparing for SAP certification exams with curated study guides
- Mapping analytical access to SOX compliance requirements
- Tracking data lineage from source to insight
- Auditing AI model decisions and rationale
- Documenting changes to CDS views and KPIs
- Ensuring GDPR and privacy compliance in AI models
- Creating data governance policies for cross-team use
- Managing personal data access in analytics outputs
- Implementing data retention rules for training sets
- Using SAP Information Steward for data quality
- Preparing audit packs for internal and external reviewers