What a forecasting model can and can't tell you
A forecast is a range of likely outcomes, not a promise. Treat the single headline number as the least important thing it produces.
A forecasting model trained on your past data will produce a number. The temptation is to treat that number as a prediction of the future. It isn't one: it's a statement about what tends to happen under conditions similar to what the model was trained on, projected forward with some degree of uncertainty, whether or not that uncertainty is stated out loud.
The uncertainty is the actual deliverable. A model that reports "120 units, ±15 at 90% confidence" is far more useful for planning than one that reports "120 units," even though the second number looks more decisive. Point estimates without a stated range invite exactly the kind of over-confident planning that got most teams into forecasting trouble in the first place.
A forecast also degrades the moment the underlying conditions shift (a new competitor, a pricing change, a supply disruption) in ways the historical data never saw. This is why we deploy forecasting models with monitoring against live error, not just a one-time validation. A model that was accurate at launch and is silently wrong six months later is worse than no model, because it's still trusted.
The honest use of a forecasting model is as one input into a planning decision made by people who understand its range and its blind spots. It is not an oracle that removes the judgement call.