Industrial AI planning systems are complex, combining predictive models, optimization logic, controls, and governance rules. When such a system improves, it is tempting to credit any recent addition. But the paper argues that an improvement in the complete policy does not reveal whether one addition made an independent contribution.
To address that, the work puts forward an "observable evidence standard" for determining when an added planning capability creates operational value. The goal is to isolate the effect of a single component rather than attributing the whole system's improvement to it.
This matters for engineering decisions: adding capabilities costs money and complexity, and companies need a clear way to test whether a change is worth keeping. The standard, as described, offers a way to make that call based on observable outcomes.