In a Phase IV retrospective on the cargo-demand-forecasting model, Meridian's data science team reports that their first three modeling approaches all technically "worked" in that they trained successfully, but each embedded an assumption (fixed pricing, no seasonal shocks, only one aircraft type) that made it unsuitable for Meridian's real cargo operation. What lesson does this real-world Phase IV example best illustrate?

The correct lesson is that training without errors is not the same as being fit for purpose. One well-documented real-world CPMAI Phase IV pattern is that a model can complete training successfully — no runtime errors, output produced — while still failing to satisfy the actual business requirements, because its underlying assumptions do not match reality. Fixed pricing, ignoring seasonal shocks, and covering only one aircraft type are exactly the kind of simplifying assumptions that make a technically functioning model useless for Meridian's real cargo operation. This is precisely why the model-development requirements defined earlier, such as prediction targets, tolerances, scope, realistic pricing, and seasonality assumptions, exist: they give the team something concrete to check the model against before it is allowed to proceed toward evaluation and a Phase IV Go/No-Go decision. The option saying that the first successful run should have been accepted is wrong because successful training is only a technical milestone, not acceptance; acceptance depends on satisfying the business requirements. The option saying that model development should be judged solely on whether training completes without runtime errors, with business fit evaluated later, is also wrong because it conflates technical completion with business fitness and ignores the earlier requirements that exist to catch this gap. The option saying this shows a failure to follow proper coding practices is wrong because nothing in the scenario suggests bugs or bad code; the issue is that the modeling approach embedded unrealistic business assumptions, which is a methodology and requirements problem, not a software-engineering one.