Most AI analytics pilots fail at the same place. Not the model. The context. After watching enterprise after enterprise stall between pilot and production, the pattern is always the same. Here's the framework: Level 1: Metadata Search Your AI finds data assets. "Where is the churn table?" Catalog + embeddings + vector search. The ceiling: catalog quality. Invest in stewardship before retrieval. Level 2: Semantic Layer Queries Your AI computes metric answers. "What was ARR last quarter?" This is the highest-leverage investment in AI analytics. Covers 80% of production use cases. Teams with a semantic layer are 2-3 years ahead. Level 3: Dynamic Text-to-SQL Your AI generates ad-hoc SQL for exploratory questions. Most flexible. Most dangerous. Gate this behind Level 2. Never jump here first. The shared foundation under all three: Identity + access management. Data lineage. Query observability. Feedback capture. Prompt versioning. None of this is optional. The core insight: the model is not the product. The context infrastructure is. Teams winning at enterprise AI analytics didn't have better models. They had better infrastructure. Full article : https://medium.com/@deeswar95/from-pilot-to-production-the-context-maturity-model-every-ai-team-needs-7833147bb313 What level is your organization at right now? Drop it in the comments. #DataEngineering #AI #DataArchitecture #ConversationalAI #ContextLayer #ContextEngineering #SemanticLayer #Metadata