A model trained on an organization's historical data is, by construction, trained on the past. That's a useful capability and a real limitation at the same time, and the limitation tends to get less attention than it deserves, because it's less interesting to talk about than the capability.
The specific thing a model can't see is the reason behind an exception. It can flag that a particular contract, region, or account behaves differently from the pattern. It cannot tell you, on its own, whether that difference reflects a real anomaly worth correcting or a deliberate, well-reasoned adaptation that a person made for a reason that never made it into the data. Both look identical in the numbers. Only someone with institutional context can tell them apart, and that person increasingly has to be deliberately kept in the loop rather than assumed to still be there.
This is where a lot of well-intentioned modernization quietly goes wrong. As decisions get pushed toward dashboards and automated thresholds, the informal knowledge that used to get applied at the point of exception, often by someone who'd simply been doing the job long enough to recognize a pattern, gets squeezed out of the process, not through any single bad decision but through the cumulative effect of many reasonable-looking efficiency gains.
The fix isn't to distrust the model. It's to be explicit about which decisions still require someone with real context to sign off, and to design the process so that requirement survives the next round of efficiency improvements rather than getting quietly automated away because nobody defended it. That's a governance question as much as a technical one, and it's worth treating it that way from the start rather than discovering the gap after the context has already left the building.