The AI Re-Think Blog

1. The Context Problem Enterprise AI is stuck. Not because models aren't capable. Not because compute is expensive. Because context — the enterprise data and signals that agents need to make real decisions — is fragmented, ungoverned, and inaccessible. The fragmentation is structural: ERP knows orders, invoices, and shipments Process mining  (Celonis, Signavio) knows cycle times, variants, and bottlenecks EDW knows historical trends and aggregates CRM knows customers, oppo

Executive Summary Enterprise AI fails at the last mile. Dashboards proliferate but nobody trusts the numbers. ML models degrade for weeks before anyone notices. RAG systems hallucinate and ship without evaluation. Agents fragment context and write back without contracts. Process intelligence lives in silos, disconnected from ERP facts and business semantics. The common root cause: context is fragmented, ungoverned, and treated as a byproduct rather than a product. UCL (Unifie

Graph-RAG knowledge fabric + AgentEvolver runtime, wired into your existing operational platforms. Technical Abstract The first large-scale study of production AI agents (Berkeley, December 2025; 306 practitioners, 20 case studies) reveals a striking gap: 68% of deployed agents execute at most 10 steps before requiring human intervention, 74% depend on human evaluation, and no team applies standard reliability metrics like five 9s. Production agents are far simpler than acade