Anecdotally this is pretty typical of the evolution of mo systems. Some heuristic/standard algorithm works pretty well, but people see cases it didn't catch an

Anecdotally this is pretty typical of the evolution of mo systems. Some heuristic/standard algorithm works pretty well, but people see cases it didn't catch and think it could be better. An engineer/scientist takes a look and realizes the extra features required need a more complex/ml algorithm to add support for. Years later a model has made it to production, but there are now complaints about too lenient matching. In my experience, ML works best in scenarios where there is either such immense data volume that .2% improvement is a real benefit (these are rare) or the very notion of a heuristic method simply wouldn’t work.