Sponsored by: SAP | SAP and Databricks Turn SAP Data Into AI-Driven Business Outcomes

Key Insights

• Unify governed SAP data: Access curated SAP data products through SAP Business Data Cloud while preserving business context and governance.
• Reduce data duplication: Use bidirectional Delta Sharing to connect SAP data with Databricks for AI and analytics without unnecessary replication.
• Improve financial analysis: Apply AI to general ledger anomaly detection, balance sheet analysis, and P&L variance analysis.
• Strengthen cash flow forecasting: Use supplier payment predictions to improve Treasury planning and working capital visibility.
• Operationalize AI insights: Publish Databricks-generated predictions back into SAP BDC for downstream analytics, planning, and business workflows.

Build a Governed Foundation for AI on SAP Data

Finance organizations often depend on SAP systems for critical financial and operational data. Traditional approaches to advanced analytics required extracting this information into separate environments and rebuilding business definitions, increasing complexity and data duplication.

SAP Business Data Cloud addresses this challenge by providing curated, SAP-managed data products that preserve business context, relationships, and lineage. The session describes more than 300 available data products across SAP applications, including S/4HANA and HR-related scenarios.

Through bidirectional Delta Sharing, organizations can access governed SAP data products in Databricks, combine them with non-SAP data, and apply advanced analytics and machine learning.

This architecture enables Finance teams to use trusted SAP information for AI while maintaining consistent business definitions and reducing unnecessary data movement.

Apply AI to Financial Analysis and Supplier Payment Forecasting

SAP and Databricks enable Finance teams to apply AI to financial processes that depend on timely, accurate enterprise data.

Examples include anomaly detection in general ledger entries and variance or movement analysis across balance sheet and profit-and-loss statements. Curated SAP data products can be shared with Databricks for modeling, with resulting insights returned to SAP BDC for reporting and planning.

A supplier payment forecasting example demonstrates the potential for Treasury and working capital management.

By combining SAP vendor invoice management and accounts payable data in Databricks, one customer developed a model to predict whether invoices would clear within three days, ten days, or beyond. The model achieved approximately 95.97% accuracy, helping Treasury teams better anticipate payment timing and align working capital with near-term cash requirements.

Connect AI Predictions to Enterprise Finance and Business Workflows

The SAP and Databricks partnership supports a bidirectional approach to enterprise AI.

Organizations can access curated SAP data products through Delta Sharing, develop predictive models in Databricks, and publish resulting outputs back into SAP BDC for downstream analytics and planning.

This approach extends beyond Finance. The session highlights energy trading optimization, retail returns forecasting, assortment planning, and trade promotion analysis as additional examples of combining SAP business data with external signals and AI models.

For Finance teams, the same architecture creates opportunities to connect financial predictions with existing enterprise reporting and planning workflows, turning governed data into actionable business insights.