Recent years have brought an explosion in the application of advanced AI techniques to the imaging and interpretation of petroleum reservoirs. The ability of these techniques to image features in unprecedented detail, and within very short timeframes, has provided the opportunity for the industry to gain a more complete understanding of hydrocarbon reservoirs than ever before. The rapid evolution of these technologies has brought challenges however, as new workflows must be developed to gain the greatest value from these advancements. In this paper we look at the impact of these technologies on imaging, interpretation and modelling. This is done through an analysis of datasets spanning compressional and extensional systems, looking at both onshore and offshore data. A particular focus is given to recent acreage release areas. Through this analysis we find significant opportunities to revolutionise G&G workflows, but also unexpected challenges in understanding and integrating this newfound complexity. The given examples show that the interpreter is no longer working with the artificial simplicity of manually interpreted structures, but rather with a web of localised planes of slippage. Rather than the challenge of accurately identifying faults, we must focus on how to transfer this complexity into a useful interpretation and then into our static model. In these examples we can see that, through a reduction in manual processes, the interpreter can focus more of their energy on the iterative process of proposing and refining structural models, and that this process proves crucial to working effectively with these new methods.