If K-Means is so limited, why is it still widely used in industry?

Because it is fast, scalable, interpretable, and works surprisingly well on many real-world datasets.

Despite limitations, K-Means is computationally efficient (linear scaling), easy to implement, and produces interpretable results. Many real-world datasets are approximately clusterable enough for it to be useful as a baseline or feature generator.