Generative AI for SAP Development and
Data Migration Automation
Evaluation Methods and Implementation Cases 2026 — a practical guide to reproducible evaluation and responsible disclosure.
1. Purpose and scope
Evaluate total effort and quality from requirement review and AI generation through human review, correction, validation and reruns—not generation speed alone.
2. CodingZou internal validation
Twenty simulated ABAP programs were evaluated with a GPT-5.6-equivalent model and CodingZou evaluation build 0.9 equivalent. Effort from basic-design analysis to first code review decreased from 370 to 148 hours, approximately 60% on average.
3. MappingZou internal validation
The evaluation used 1,000 simulated material-master records and 1,000 PO business records with a GPT-5.6-equivalent model and MappingZou evaluation build 0.9 equivalent. Effort decreased from 160 to 50 hours, approximately 69%.
4. Security, governance and disclosure
Define data boundaries, training reuse, access, encryption, logs, retention, human approval and reevaluation before the PoC. Publish sample size, baseline, period, versions, review scope, results and limitations.
These provisional results use simulated and anonymized data. They are not customer implementation results and do not guarantee production outcomes.