In a recent post, Eugene Yan argues that building prototypes helped him secure buy-in in situations where roadmaps and design documents had not. He found that static planning artifacts often fail to make a project's potential tangible enough for stakeholders to support it.
Prototypes, by contrast, give people something they can see and interact with, which can make the intended outcome clearer than abstract descriptions. Yan's experience suggests that for AI and ML teams, prototyping is not just a technical exercise but a practical way to align stakeholders and move projects forward.
Because this article reflects one practitioner's experience, it is a single data point rather than a general rule. Still, the lesson is straightforward: when documentation is not enough, a working prototype may be the more persuasive argument.