Vishal Sirohi, CEO & Co-Founder, Island Computing said: The most significant shift is from experimentation to discipline. Through 2024, enterprises measured AI success by the number of pilots launched. In 2026, MIT research shows only 5% of AI pilots produce measurable P&L impact, and RAND puts the enterprise AI failure rate at 80%. Enterprises are now treating compute, data, and intelligence as three portfolio assets that require unit economics per workload, bounded budgets, sovereign control planes for regulated data, and audit surfaces designed for autonomous agents. The FinOps Foundation’s State of FinOps 2026 finds 98% of FinOps teams now manage AI spend, up from 31% two years ago, though only 22% produce per-workload unit economics monthly. Alongside the discipline shift, the dominant AI workload itself is moving from short, stateless model inference to long-running, stateful, tool-calling agentic execution, and the infrastructure built for one does not run the other at production scale. The shift underneath is from AI-as-feature to AI-as-asset. The enterprises that install the safety and cost mechanisms today set the operating baseline for the next decade of production AI.