The BizTech piece on AI proof-of-concept failure is worth a read… and worth one small correction.
The article correctly names the “last mile” problem: you build an impressive demo, and then the remaining edge cases take as long as everything that came before. And that last mile is brutal, I’m not arguing that. But the article frames it as an engineering gap… better data capture, more subject matter expert involvement, process redesign before deployment.
That’s all good advice. It’s just applied in the wrong place.
The last mile problem isn’t a production problem. It’s a problem definition problem… and it starts on day one. When you start with the technology, when your first conversation is “let’s build an AI prototype,” you naturally design for the happy path. It’s a demo. Of course it impresses, that’s the whole point of it. What it doesn’t do is surface the exceptions, the edge cases, the institutional knowledge gaps that show up when real work runs through it.
Start instead with a specific, measurable business problem, and scope it with the people who actually do the work before a single line of code runs. The edge cases don’t disappear, you know. They stop being surprises. They’re in scope.
Every stalled rollout I’ve watched has the same shape. The constraint is rarely the model. It’s the problem definition upstream of the model.
So before the next rollout, I’d ask one question: what number is this supposed to move, and by how much? That’s the conversation that saves you the last mile.
Source: Why AI Proofs of Concept Fail When They Reach Production
Originally posted on LinkedIn.
RationalAI helps organizations turn ideas like this into results: AI strategy and advisory services. Previous article: Regulation isn’t a dial — tier it by risk
