Anecdote:

Someone I know works for a municipality in digital transformation. They have a public facing website where local residents can report things to the municipality, such as potholes, water system issues, noise complaints, etc.

The UI has this huge taxonomy with like 200 categories with three levels of nesting that route to different departments. There are multiple "other" categories.

When a resident chooses other, it becomes an employee's job to choose a different category. But 30% of the categories employees choose are wrong (about the same for residents).

It's a UX problem, a taxonomy problem, a training problem, a change management problem, a work routing problem, and all these contribute to longer resolution times.

My friend started tinkering with a small quantized local LLM, testing whether it could classify the reported issues more accurately than the public/staff. Of course it could. They're preparing to integrate it into production. Installing it will dramatically improve the UX for the residents, save staff time (at least hundreds of hours a year), improve resolution time, etc.

My friends boss mentioned it at a conference and apparently "no one else is doing this".

So yeah, we are just barely scratching the surface of the productivity opportunities LLMs offer. It's early, not because the technology isn't developed enough to help (it is), but because most people are still figuring out how it can help.

Sorting a decision tree and hitting a leaf from fuzzy context is the one thing I've also noticed that AI does well. It's basically what LLMs are designed to do. They can and should take these bullshit tasks away from humans.

Wait for the next admin to unplug the decision tool so they can re-add the jobs and backfill with their private staffing service.