Microsoft positions Fabric as an enterprise AI operational foundation with new context-layer capabilities, leveraging 20 million Power BI semantic models and Copilot distribution, while analysts highlight interoperability and data sovereignty as key challenges against rivals Databricks and Snowflake.
AWS’s new Context Ontology Accelerator transforms the traditional semantic layer from merely calculating metrics into a runtime business‑context infrastructure that agents can query, combining governed metrics, ontology, and knowledge‑graph services via MCP to enable accurate, auditable, and governed AI‑driven decisions.
The article analyzes the concept of an enterprise context layer for AI, breaking down its components—knowledge, expertise, and policies—into AI‑ready data, semantics, and reusable skills, and outlines the five capabilities needed to build, govern, and activate this shared corporate brain.
The article dissects self‑learning agents into model, harness, and context layers, evaluates real‑world approaches from Anthropic, Karpathy, DeepMind, Microsoft, and others, and argues that the most valuable learning signal comes from capturing genuine user feedback that most teams overlook.
A MIT report shows that 95% of generative AI pilots flop because data agents lack proper business context, and this article breaks down the underlying reasons, benchmark results, and a five‑step roadmap for building a dynamic context layer to bridge the gap.
