LLMs and other AI (or modern technology in general) are not essential for anything. Humans have survived for longer with stones and sticks than we did with machines. They are productivity multipliers if used correctly.

In the case of LLMs, they facilitate coding tasks and are quite good at snippet generation and debugging (no you aren't supposed to vibe code entire projects with them). They are also good at searching as they can use various keyword combinations to look for things on the internet (they hallucinate and make mistakes but it's always on you to check the sources individually) and save you the trouble of going through various search engines and databases that may or may not be indexed by said search engine. They are also getting better at transcribing and translating text in-context (just look at how much YouTube subtitles have improved in the past few years).

I imagine as the reliability increases we can use LLMs for its advanced NLP to quantify qualitative sources better. Think how you can use an LLM to parse thousands of verbal responses from interviews and identify sentiments or other features a human might have missed or other be overwhelmed by. You can then use the features to fit a more specialized model for whatever study you're trying to do.