Customer data becomes significantly more valuable when organizations can use it to understand what may happen next.

This module introduces the AI Workbench within SAP Customer Data Platform and focuses on transforming customer information into predictive insights.

Learners explore how customer data is prepared and provided to AI Workbench, how predictive models are applied, and how the resulting insights can be visualized through dashboards and analytics.

The uploaded curriculum identifies these three areas as the core outcomes of the module.

SAP's current official course further identifies capabilities such as predicting customer churn and customer lifetime value, using standard and custom model settings, and designing dashboards to present predictive insights. 

Reliable AI begins with reliable data.

This unit focuses on preparing and providing customer information that AI Workbench can use for predictive analysis.

Learners will understand why data quality, structure, relevance, and availability matter when creating predictive customer models.

The objective is to help participants understand the connection between the customer data stored within SAP Customer Data Platform and the predictive indicators produced by AI Workbench.

SAP describes AI Workbench as a capability that can provide analytical insights into customer data and generate predictive indicators that help organizations understand potential customer behavior.

Learners will therefore begin thinking beyond simply collecting customer data and toward preparing information that can support practical business predictions.

Once appropriate customer data is available, predictive models can be used to identify patterns and estimate future customer behavior.

This unit introduces learners to the predictive capabilities available within AI Workbench.

Participants explore how predictive models can be configured and executed using available settings and how the resulting predictions can support CX decision-making.

Current SAP documentation identifies predictive model types including Churn and Customer Lifetime Value, with models available at profile and group level. Additional scenarios may be available depending on the configured environment.

For example, churn prediction can help organizations identify customers who may be at risk of leaving, while Customer Lifetime Value can help teams better understand the potential long-term value of different customer relationships.

The emphasis is on using predictions to support better business decisions—not simply generating AI scores.

Predictions create value only when business users can understand and act on them.

This unit focuses on turning AI Workbench results into meaningful visual information.

Learners explore how predictive insights can be presented through dashboards and analytics so that marketing, sales, service, and other stakeholders can more easily interpret customer patterns and make informed decisions.

The official SAP course specifically includes Visualize Predictive Insights and Design Dashboards to display predictive insights among its learning outcomes.

Learners will therefore understand how to move through the complete analytical flow:

Customer Data → Predictive Model → Predictive Insight → Visualization → Business Action

This provides a practical foundation for using AI-generated customer intelligence within day-to-day CX decision-making.

This Interactive Value Journey demonstrates how AI can improve sales and customer service activities.

The uploaded curriculum describes the scenario around the SAP CX AI Toolkit, showing how AI can help optimize workflows, prioritize work, generate summaries, and assist users with more complex sales and service activities.

Current SAP documentation describes the SAP CX AI Toolkit as an AI-powered assistant that can work with SAP Customer Experience data and provide capabilities such as customer and account summaries, opportunity and case summaries, AI-generated content, intelligent Q&A, scheduling, and other role-specific tools. 

This Interactive Value Journey takes a broader end-to-end view of Customer Experience.

Using the From Farm to Consume scenario from the uploaded curriculum, learners explore how connected SAP Customer Experience solutions can help organizations operate as intelligent enterprises.

Rather than looking at sales, marketing, commerce, customer data, and service as disconnected activities, this exercise helps participants understand how customer information and business processes can work together across a broader customer journey.

This Interactive Value Journey focuses directly on how Joule can improve sales and service workflows.

The uploaded curriculum describes the scenario as demonstrating Joule's conversational AI and service agent capabilities within SAP Sales and Service Cloud, with an emphasis on improving productivity and streamlining sales and support operations.

Learners can see how conversational AI changes the way users interact with customer information and business processes—from manually navigating applications toward asking questions, receiving contextual summaries, and obtaining assistance directly within the flow of work.