I work in forecasting retail sales, e.g. predicting next week's sales of a particular stock-keeping unit in the presence of promotions, price changes, day of week, calendar events, seasonality and tons of other drivers. (I used to tell my kids that daddy makes sure there is always enough ice cream at the supermarket.)
Often, the retailer will wonder just why a particular forecast was off. Why was a promotion underforecast so badly, or the Christmas sales overforecast? To answer that, and to improve forecasts going forward (so we don't have too much product clogging the shelves, or spoiling in the case of perishables, or too little product and unhappy customers), you need to understand the data, and the model, and understand if the model could be improved. Or you may need to help the customer understand that the model did the best it could, and that there is simply a lot of residual variation. In which case the question becomes one of how best to deal with this residual variation, by using higher safety stocks, or consciously allowing for stockouts. At this point, business logic enters.
Also, when we have a new retailer ramping up with our forecasting product, we need to map their promotion landscape to our model. Retailers can have quite complex promotions (buy $n$ units of product $X$ and $m$ of $Y$, then you get $p$ units of $Z$ at $x\%$ off, and $y$ bonus points on your shopper card, and $z$ airline miles...). Again, you need understanding here. AI is not quite there yet.
Related, though closed: Data science without knowledge of a specific topic, is it worth pursuing as a career? My answer there focused on the necessity of communication and business understanding to a data scientist, both of which a website won't provide.