Forward-deployed engineers are central to enterprise AI go-to-market strategies, promising speed and product advantage. However, their true value lies in whether their work leads to a compounding product or simply accumulates as delivery labor, a distinction often obscured. At its best, FDE acts as a disciplined product-learning function, transforming edge cases into reusable capabilities. Conversely, at its weakest, it masks a product's limitations by performing manual translations. The core value of FDE is in creating automation that powers a system of intelligence by capturing enterprise context and learning from deployments. This contextual understanding, rather than just model choice, is often the constraint in enterprise workflows. Learning from FDE engagements must be codified into reusable artifacts like semantic mappings or policy modules. A critical test is whether an FDE is working in a sandbox to enhance a general engine or manually building custom solutions in the "mud." The learning from FDE engagements should lead to deployments that start with fewer unknowns and require less custom code over time. Ultimately, a successful FDE organization sees human translation shrink per unit of value delivered, with engineers spending more time extending reusable capabilities.