SAP AI Core talent and proof patterns

Problem

ABAP development in modern S/4HANA landscapes is increasingly complex — clean core constraints, RESTful ABAP Programming Model (RAP), and evolving best practices create a high cognitive load for developers working without real-time guidance.

Pattern

Use MCP-connected AI assistants to query SAP documentation and RAP patterns in-context → Generate ABAP class stubs, CDS view definitions, and behavior definitions from structured prompts

Problem

Supply chain plans go stale within hours — weather disruptions, port and carrier delays, supplier risk, and demand swings invalidate the plan before it is executed. Reconciling planning, logistics, and manufacturing across disconnected modules and spreadsheets leaves teams reacting to exceptions instead of orchestrating outcomes.

Pattern

Wire SAP IBP, EWM, and Digital Manufacturing through a unified orchestration layer on SAP BTP → Ingest external signals — weather, port and carrier feeds, supplier risk events — alongside SAP transactional data

Problem

Production engineers, shop-floor operators, and warehouse teams operate across siloed tools — engineering systems, MES, EWM, transport — losing time on context-switching and manual hand-offs. Insight from one stage rarely closes the loop back into the next, and non-technical operators have no natural way to drive the system.

Pattern

Connect engineering, CNC production, and logistics through SAP Digital Manufacturing as the unifying layer → Surface AI-driven dashboards that close the loop from production data back into engineering and warehouse decisions