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A common outcome of an AI project is a working system that almost nobody uses. The pilot went well, the rollout happened, and three months later usage sits with the four people who were enthusiastic from the start. AI adoption fails for reasons that have very little to do with the technology, and the reasons are consistent enough to plan around. It has to be faster than the current way People are comparing the new tool against what they do now, including all the shortcuts and habits accumulated over years. A tool that is theoretically better and practically slower loses

Most systems pick one model and send everything to it. That is the right way to start and a poor place to stay, because workloads are rarely uniform: classifying a support ticket and drafting a legal summary have very different requirements and very different costs. Model routing means matching the request to the capability it actually needs. Done carefully it reduces cost substantially and improves latency on the majority of requests. Segment the workload first Before routing anything, look at what your system is actually asked to do and in what proportions. Almost every workload turns out to be dominated