Even though soft-margin SVM allows us to fit a classifier when a few outliers lay outside of their linearly separable classes, it doesn’t mean that a linear decision boundary should always be used. For example consider the below scatterplot. A soft-margin SVM might converge to a find a solution by drawing a diameter to the circle, however this is not at all sufficient, since we will have 50% accuracy. Your classifier is effectively a coin toss.

We would instead want to fit a boundary that looks like the inner circle in the below image. In comes non-linear Kernel methods to save the day!

img src - www.mathworks.com/help/stats/support-vector-machines-for-binary-classification.html