Enterprise AI fails because organizations lack structured context layers, not better models¶
Insight: According to Juan Sequeda (guest on the Product Impact Pod), 75% of enterprise AI deployments fail not because of model capability but because organizations lack a structured context layer — the mapping between business metadata, technical metadata, and the semantic connections between them. RAG and longer context windows address symptoms, not root causes.
Detail: Sequeda proposes a three-part architecture: (1) business metadata — organizational terminology and definitions, (2) technical metadata — system specifications and data structures, (3) a mapping layer connecting business concepts to technical reality. Supporting data: 91% of ML models degrade over time without detection (CNBC), Gartner predicts 40% of agentic AI projects will be scrapped by 2027, and Microsoft Copilot enterprise licenses show only 30% active usage after 6 months. Software engineer postings up 11% YoY but requiring 70% more AI literacy. This corroborates the existing entry that AI failures in 2026 are structural and organizational, not technical.
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Related: ai-failure-structural-not-technical-2026 in external/design-leadership.md — CORROBORATES