What a forecasting model can and cannot tell you
A forecast is a distribution, not a promise. Treat the point estimate as the least important number it produces.
A forecasting model trained on historical data will produce a number. The temptation is to treat that number as a prediction of the future. It is not — it's a statement about what happened under conditions similar to the training data, extrapolated forward with a stated (or unstated) degree of uncertainty.
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 — not as an oracle that removes the judgement call.