Active learning is useful when labels are expensive or slow to obtain. Instead of labeling everything up front, you focus annotation effort on the examples that are likely to teach the model the most.
That idea shows up clearly in the active learning and intent classification posts. In those workflows, the bottleneck is often label quality and label budget, not just model architecture.