- The evolution of AI in enterprises has shifted from initial experimentation to production, with concerns now focusing on ROI, cost, and data governance.
- Production AI requires reliability, auditability, cost control, and governance, highlighting the need for infrastructure rather than just models.
- Recent changes include increased adoption velocity leading to fragmentation, cost opacity, and governance gaps as AI expands across sectors.
- Managing multiple AI providers creates operational complexity with custom failover, spreadsheets for cost tracking, and manual tuning, which is unsustainable.