The AI Re-Think Blog

Production AI where enterprise context feeds deployments that learn AND agents that decide—compounding, not frozen. Executive Summary After 18-24 months of pilots, enterprises are giving up on AI. Not pausing—abandoning. 42% scrapped their AI initiatives last year, a 2.5× increase from the year before. Another 95% of pilots never made it to production in the first place. More than $500B sits frozen in "AI programs" that don't actually run anything. The problem isn't model qua

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