AI-Augmented Data Migration in S/4HANA Implementations: A Framework for Enterprise Resource Planning Transformation

High costs, long duration, and higher error rates are still challenges for the data migration part of Enterprise Resource Planning (ERP) transformation programs moving organizations from SAP ECC to SAP S/4HANA. The heterogeneity, volume, and quality issues with legacy master and transactional data have been hard for conventional ETL (rule-based) approaches to handle. In this paper, an AI-Augmented Data Migration (AIADM) framework is suggested that introduces machine learning (ML)–based data profiling, anomaly detection, natural language processing (NLP)–assisted object and field mapping, and predictive reconciliation into the traditional S/4HANA migration lifecycle. The framework consists of four interacting layers of source extraction, AI-based cleansing and mapping, validation and reconciliation, and continuous-learning feedback. The evaluation is carried out using a mixed methods approach with a simulated pilot migration over 3 mock-load cycles and a structured literature synthesis, and the proposed framework is compared to a traditional rule based baseline. The AI-enhanced solution is found to deliver a data cleansing time reduction of about 61 percent, a post-load error rate of 2.1 percent down from baseline's 8.6 percent, and a cutover downtime reduction of nearly half, with data quality scores reaching over 97 percent in just six iterations of the AI-enhanced solution. The results indicate that AI augmentation delivers quantifiable efficiency, accuracy and governance improvements for large-scale ERP transformation, and that organizations have essential change-management and data-governance requirements to meet in order to achieve these benefits.