Some off the top of my head...

- Instead of trying to get LLMs to answer user questions, write better FAQs informed by reviewing tickets submitted by customers

- Instead of RAG for anything involving business data, have some DBA write a bunch of reports that answer specific business questions

- Instead of putting some copilot chat into tools and telling users to ask it to e.g. "explain recent sales trends", make task-focused wizards and visualizations so users can answer these with hard numbers

- Instead of generating code with LLMs, write more expressive frameworks and libraries that don't require so much plumbing and boilerplate

Of course, maybe there is something I am missing, but these are just my personal observations!

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menaerus 3 hours ago | parent [-]

With all due respect, all of those examples are the examples of "yesterday" ... that's how we have been bringing money to businesses for decades, no? Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned.

Businesses are incentivized to be more productive and cost-effective since they are solely profit-driven so they naturally see this as an opportunity to make more money by hiring less people while keeping the amount of work done roughly the same or even more.

So "classical" approach to many of the problems is I think the thing of a past already.

	▲	auxiliarymoose 3 hours ago \| parent [-]
		> Today we have AI models that can already do as good, almost as good, or even better than the average human in many many tasks, including the ones you mentioned. We really don't. There are demos that look cool onstage, but there is a big difference between "in store good" and "at home good" in the sense that products aren't living up to their marketing during actual use. IMO there is a lot of room to grow within the traditional approaches of "yesterday" - The problem is that large orgs get bogged down in legacy + bureaucracy, and most startups don't understand the business problems well enough to make a better solution. And I don't think that there is any technical silver bullet that can solve either of these problems (AI or otherwise)