This is the problem. The practical uses cases as I understand them pretty much fall into the same buckets we use NLP for now. If you don't want to use LLM or GenAI technology we already have a lot of really great ML/NLP tools that do a really really good job.
In fact a lot of these tools would do a better (or at least more reliable) job of about 70% of what I see companies deploying in the customer service chat bot space, they'd be much cheaper and faster too. I have tried to explain to several clients, "You know you could do all this with Google DialogFlow" but no they'd rather wank around building MCP/SEE/Agenic replacements for the REST services they already have, futz around with prompt design, and then figure out how to test for abuse cases all so they can pay for tokens..
By they time you chain down Gemini/CoPilot/GTP down to respond in corporate approved ways half of customers could not tell the difference anyway and most would probably enjoy an experience that is consistent focused and quick.
And so it seems to go with 2Brains here, seems like an expensive and complicated way to do things we have been able to do well with NLP for 15 years now. Using LLM at scale means an expensive and complicated pile of machinery, but what is attractive about using them places where they are not really needed is "Its what all the cool kids are doing" not the expensive and complex part... Good luck 2brains...