Recent years have produced a patchwork of promising methods for netlist reverse engineering, especially in partitioning and module identification. However, only a small fraction of this research translates into tools that are reliable, scalable, interoperable, and usable in practice. This talk argues that the dominant challenge today is no longer the invention of isolated algorithmic techniques, but the systematic integration of existing methods into an automated, end-to-end workflow that can be deployed, maintained, and trusted as an industry-grade tool. Only such an integrated approach enables reverse engineers to move beyond low-level structural recovery toward higher-level objectives, including the identification of implemented algorithms and the development of actionable, system-level understanding. We therefore need to examine how current state-of-the-art methods perform when applied to realistic netlists, and which techniques are sufficiently robust to be incorporated into a practical reverse engineering pipeline. Beyond purely algorithmic aspects, we also examine the organizational constraints and usability requirements that such a pipeline must address. This includes, for example, the need for on-premise deployment, support for multi-user collaboration, secure sharing of reverse engineering results, and a productive interplay between automated analysis and human expertise, acknowledging that reverse engineering outcomes are inherently imperfect. Finally, we ask how the field can move beyond fragmented and short-lived research prototypes toward a more unified and maintainable platform, and what would be required to make this transition viable for all stakeholders. We outline a roadmap of the technical, operational, and usability challenges ahead, with the goal of fostering discussion around the features and capabilities needed to make automated netlist reverse engineering feasible in practice.