When I worked on Google Ads, we used time series forecasting to compute the odds of an ad campaign reaching its goal (and to tell users how likely they were to hit them).

A ton of (unsophisticated) advertisers would just draw a line from zero to the number they are at today and project that line to the end of the month to forecast the amount of conversions/spend they were going to hit. This of course doesn't take into account various seasonalities (day-of-week, time-of-year, etc.) and gives you a pretty poor forecast. Compared to those, time-series forecasting is much more accurate.

Is it perfectly accurate? No, that's impossible. But when you can train a model on all advertising campaigns, you can give good 95% confidence intervals.