Objective: To operationalize data-architecture maturity as a predictor of enterprise AI success in regulated industries, where auditability, traceability, and lifecycle risk controls constrain deployment at scale.

Methods: A structured comparative synthesis of ten recent peer-reviewed and standards sources on data governance and quality, maturity modeling, lakehouse/platform capabilities, MLOps, and AI risk management was performed, followed by construct-to-indicator mapping that links maturity domains to observable operational signals.

Results: The synthesis yields a five-domain predictor framework—(1) governance maturity, (2) data quality maturity, (3) platform architecture maturity, (4) MLOps maturity, and (5) responsible-AI maturity—paired with an evidence-linked mapping to success mechanisms (reproducibility, controllability, deployment stability, auditable decision records, and sustained performance under drift). The framework is made assessable pre-deployment through a concise signal set: versioned datasets and reconstructable training snapshots, transformation lineage bound to metadata, continuous quality metrics with documented thresholds, automated drift monitoring with traceable alerts, and explicit lifecycle risk controls aligned with regulatory expectations.

Implications: The model supports feasibility screening and targeted remediation planning before scaling AI in compliance-constrained environments.