Multicollinearity cannot always be fixed. Sometime, collecting additional observations will help. This may not matter if you are just trying to predict fitted values of the dependent variable and the existing multicollinearities will persist indefinitely into the future. However, if there is any possibility that the troublesome variables will begin to vary with some degree of orthogonally in the forecasting period, you will have a misleading model. The oft-advocated strategy of dropping offending variables subjects the coefficients on the remaining variables to possible omitted variables bias.
Point estimates are virtually never the objective of econometric analysis.
You want to be able to test hypotheses. With the incorrectly calculated parameter standard errors produced by OLS in the presence of heteroscedasticity or serial correlation, all hypothesis tests are invalid. In the long run, most employers would be more upset by false hypothesis tests that the additional minutes of time it now takes to do it right. The values of most economic variables are determined by complex interrelationships in a naturally functioning economy. Then a simple regression of Y on X1 would produce a slope estimate that is completely uncontaminated by omitted variables bias or endogeneity.