I spent a good chunk of the pandemic doing startup advisory, and some of that has lasted up until the holiday season, so I can tell you that there was a marked shift one or two years ago where the investment board I had a seat on decided to point blank stop looking at startups that were solely focused on AI, because even the non-technical board members realized that there was little to no added value there -- and we were getting like 70% of new prospects in that category.
I would go into a demo, look at it, ask them how they did RAG on the data, ask to speak to the people doing their AI models, etc. And then sometimes I would spend a couple of hours wiring up a Node-RED flow to show my colleagues how trivial it was.
The stuff we (now they, since I'm taking some time "off" that moonlighting gig to recover from burnout) ended up prioritizing are companies that focus on business processes where triage and "human augmentation" can actually benefit from LLM summarization, some automated decision making, and some data "integration" (not just summarization, but broad correlation of events, etc.)
There's a _lot_ to be done in many fields where, say, you will notice a spike in some piece of data (using conventional ML), gather the data around that event and present it to a human (with pros and cons, including trying to flag if the data is reliable). Think GitHub issues for crop management, and you're halfway there.
_Those_ companies really need to have their use cases sorted out, and not just try to be "the Uber for greenhouse management" because they wrapped weather forecasts into an LLM.
So, in short, the real added value is expediting or improving use of domain expertise captured from (or still held by) humans.