Multicollinearity does not affect much the level of the coefficient. So, "If I increase x1 by 1 unit, how much does the target value change?" will not be addressed directly by multicollinearity considerations.
Andre indicated that you can address multicollinearity by using regularization. And, you have done that. Watch out that regularization can dramatically change the explanatory power of your model. It can do that by reducing the relative influence of the most impactful variables and also at times by even changing the sign of some variables coefficients.
I think a better approach is to detect whether any of your variables are truly multicollinear. It is actually relatively rare that two variables are multicollinear. This is because they would have to be correlated at a level of 0.90 which corresponds to a Tolerance of close to 0.20 and a Variation Inflation Factor (VIF) of 5 times. The latter is a fairly common standard of two variables being multicollinear. If you do have multicollinear variables, the easiest way to fix that is to remove the variable that is less statistically significant and/or less explanatory. Then, you are fine. And, the regression coefficients of your resulting model directly answer your question in a robust way.