RNNs (Recurrent Neural Networks) are needed because they can effectively process sequential data by capturing temporal dependencies. Unlike feedforward networks, RNNs have feedback connections that allow them to maintain and utilize information from previous steps. This makes them suitable for tasks involving sequential data, such as natural language processing, speech recognition, and time series analysis. RNNs can handle inputs of variable length and are capable of modeling context and sequential patterns, making them essential for tasks that require understanding and processing sequential information over time.