Radio astronomy relies on bespoke, experimental and innovative computing solutions. This will continue as next-generation telescopes such as the Square Kilometre Array (SKA) and next-generation Very Large Array (ngVLA) take shape. Under increasingly demanding power consumption, and increasingly challenging radio environments, science goals may become intractable with conventional von Neumann computing due to related power requirements. Neuromorphic computing offers a compelling alternative, and combined with a desire for data-driven methods, Spiking Neural Networks (SNNs) are a promising real-time power-efficient alternative. Radio Frequency Interference (RFI) detection is an attractive use-case for SNNs where recent exploration holds promise. This work presents a comprehensive analysis of the potential impact of deploying varying neuromorphic approaches across key stages in radio astronomy processing pipelines for several existing and near-term instruments. Our analysis paves a realistic path from near-term FPGA deployment of SNNs in existing instruments, allowing the addition of advanced data-driven RFI detection for no capital cost, to neuromorphic ASICs for future instruments, finding that commercially available solutions could reduce the power budget for key processing elements by up to three orders of magnitude, transforming the operational budget of the observatory. High-data-rate spectrographic processing could be a well-suited target for the neuromorphic computing industry, as we cast radio telescopes as the world's largest in-sensor compute challenge.