Enterprise AI Spend to Reach $2.5 Trillion in 2026 as Agentic Architectures Expose Data Silos

The MIT Technology Review Insights report identifies the agentic shift as a move from isolated models to operating models that require real-time connections between data, processes, and governance. Process-first firms redesign workflows before model selection, while the majority retrofit after deployment and stall. Sovereign composable layers that query data in place without centralization address residency constraints and multicloud fragmentation.

Data readiness metrics from McKinsey's 2025 State of AI survey show only 18 percent of firms maintain queryable, permissioned datasets across functions, correlating with sustained ROI above 15 percent. The original coverage understates the operational cost: each additional agent multiplies access-control surface area, and sovereignty decisions now intersect with EU AI Act Article 10 obligations on training data provenance. Firms that treat architecture as static incur compounding latency when models update quarterly.

Next phase requires verifiable execution logs and cross-jurisdictional model routing tested at production scale. Enterprises that embed these controls before 2027 will compound agent performance; others will face audit blocks and vendor lock-in as composability erodes.