Vistara Insight
Why AI value erodes after go-live when Phantom Adoption, Orphaned AI patterns, and unmanaged FinOps costs are not engineered out of the operating model.
AI programs often do not fail at launch. They fail after apparent success, when the delivery team closes, the business moves on, and no one owns whether the capability continues to create value.
Recommendations are overridden, but the reasons are not captured. Model outputs drift, but recalibration ownership is unclear. Usage declines, but adoption is reported at a surface level. Inference costs rise, but FinOps accountability is not connected to business value.
These are not isolated delivery issues. They are signs that sustainment was never designed. The organization deployed a capability without engineering out Phantom Adoption, Orphaned AI patterns, and unmanaged FinOps costs from the operating model.
Leaders should test whether there is a named owner for post-go-live value, a monitoring cadence, a feedback loop, override review, cost-value governance, and a formal mechanism to decide when the model should be retrained, retired, expanded, or constrained.
Go-live is not the end of AI governance. It is the point where governance becomes more important. Sustainment must be designed before launch, funded after launch, and governed through the life of the capability.
