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AI Governance12 min read
The Enterprise Guide to AI-Enriched Product Data (With Guardrails)
How to safely implement AI enrichment in complex catalogs using modeling, validation, and structured guardrails.
Executive Summary
AI can accelerate product data enrichment — but without governance, it introduces risk.
This guide explains how to safely implement AI enrichment in complex catalogs (5,000+ SKUs) using modeling, validation, and structured guardrails.
1. The Problem With Ungoverned AI
1.1 Why AI Alone Is Not Enough
1.2 Real Risks in Enterprise Catalogs
2. Modeling Before Enrichment
2.1 Why Structure Must Precede Automation
2.2 Category-Aware Schema Design
3. The Human-in-the-Loop Framework
3.1 Review Workflows
3.2 Confidence Scoring
4. Attribute-Level Traceability
4.1 Source References
4.2 Audit Logs
5. Governance Architecture
5.1 Role-Based Access Control (RBAC)
5.2 Field Locking Strategies
6. Safe AI Deployment Roadmap
6.1 Pilot Strategy
6.2 Scaling Strategy
7. Enterprise Maturity Model
Level 1 – Manual enrichment
Level 2 – Assisted enrichment
Level 3 – Governed AI enrichment
Level 4 – Scalable structured intelligence
Conclusion
AI enrichment is not about automation speed.
It is about controlled, structured intelligence at scale.
Governance is not optional.
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