AI adoption is growing, but ROI isn’t. The gap comes from execution, not models—value depends on integration, workflows, and system design.
AI systems aren’t truly autonomous. Structured orchestration, constraints, and human oversight define what actually works in production today.
AI progress is shifting from models to data. Scarcity, synthetic datasets, and proprietary data are becoming key drivers of performance.
Developer productivity isn’t limited by coding speed anymore. Complexity, coordination, and cognitive load are now the main constraints.
The EU AI Act makes compliance an architectural constraint—forcing teams to embed traceability, auditability, and governance into AI systems from day one.
AI-triggered SaaS sell-offs signal a structural reset—forcing teams to rethink vendor lock-in, pricing models, and the build-vs-buy equation.
