Artificial Intelligence Driven Pricing Optimization In Enterprise SAP Systems

Abstract

Static condition records and deterministic pricing procedures in enterprise SAP environments cannot respond to dynamic market signals, demand shifts, competitive pressure, and customer behaviour patterns, without manual intervention. This paper proposes a hybrid AI-driven pricing architecture for SAP S/4HANA in which a centralized SAP pricing engine governs transactional consistency while an external AI optimization engine handles dynamic price recommendation. The architecture integrates through a Business Technology Platform-hosted API proxy, injecting optimized condition values into SAP's pricing evaluation flow via the PRCG_DOC_CONDITION_AMOUNT Business Add-In (BAdI) without modifying any standard SAP objects. A feature set derived directly from SAP pricing context data, customer master, material master, sales area parameters, demand index, and historical pricing element records, eliminates the need for external data enrichment. A governance layer enforces confidence threshold validation, tolerance band checking, and audit trail generation for every AI-influenced pricing decision. Implementation evidence from IBM's Blue Harmony global enterprise program suggests measurable improvements in pricing accuracy, reduced manual interventions, and improved market alignment in volatile demand scenarios. The findings indicate the hybrid architecture may outperform both embedded ABAP-based pricing logic and batch-synchronized price lists on optimization capability, transactional integrity, and upgrade safety.