Bringing AI Applications from Prototype to Production: The Last Mile (www.simplyblock.io)

AI makes prototypes trivial, but turning those MVPs into production-grade systems remains the “last mile.” The article argues that while tools can spin up apps in minutes, enterprises still face months of work to handle scale, compliance, monitoring and real-world data complexity — explaining why MIT estimates >95% of AI initiatives never reach production. It calls out a deeper root cause: many failures aren’t model flaws but environmental ones (synthetic datasets, scaffolded backends, lack of enterprise data access), producing brittle systems that break on edge cases or when integrated into live workflows. The piece also introduces Vela as an example of a “business backend” aimed at closing that gap. Technically, the solution stack must provide production-like testing and safe access to live data: instant database cloning with built-in anonymization, row-level security and governance, self-service staging environments, and decoupled compute/storage to control cost. Teams need automated migration/rollback, observability, versioning and RBAC so non-engineers can iterate without risking production. The implication: platform engineering should be treated as a business enabler — organizations that build these capabilities will move faster, reduce rework and actually realize AI’s business value instead of losing projects to the GenAI Divide.