The graph-based semantic representation Abstract Meaning Representation (AMR) incorporates Proposition Bank (PropBank) sense IDs to indicate the senses of nodes in the graph and specify their associated arguments. While this contributes to the semantic information captured in an AMR graph, the utility of incorporating sense IDs into AMR graphs has not been analyzed from a technological perspective, i.e. how useful sense IDs are to generating text from AMRs and how accurately senses are induced by AMR parsers. In this work, we examine the effects of altering or removing the sense IDs in the AMR graphs, by perturbing the sense data passed to AMR-to-text generation models. Additionally, for text-to-AMR parsing, we quantitatively and qualitatively verify the accuracy of sense IDs produced from state-of-the-art models. Our investigation reveals that sense IDs do contribute a small amount to accurate AMR-to-text generation, meaning they enhance AMR technologies, but may be disregarded when their reliance prohibits multilingual corpus development.