The "you think about the problem and draw diagrams" part of you describe probably makes up less than 5% of a typical engineering workflow, depending on what you work on. I work in a scientific field where it's probably more than for someone working in web dev, but even here it's very little, and usually only at the beginning of a project. Afterwards it's all about iteration. And using AI doesn't change that part at all, you still need to design the high level solution for an LLM to produce anything remotely useful.
I never encountered the problem of not understanding details of the AI's implementation that people here seem to describe. I still review all the code and need to ask the LLM to make small adjustments if I'm not happy with it, especially around not-so-elegant abstractions.
Tasks that I actively avoided previously because they seemed like a hassle, like large refactorings, I no longer avoid now because I can ask an AI to do most of it. I feel so much productive and work is more satisfying because I get to knock out all these chores that I had resistance to before.
Brainstorming with an AI about potential solutions to a hard problem is also more fun for me, and more productive, than doing research the old ways. So instead of drawing diagrams I now just have conversations.
I can't say for certain whether using LLMs has made me much more productive (overall it likely has but for certain tasks it hasn't), but it definitely has made work more fun for me.
Another side effect has been that I'm learning new things more frequently when using AI. When I brainstorm solutions with an AI or ask for an implementation, it sometimes uses libraries and abstractions I have not seen before, especially around very low level code that I'm not super familiar with. Previously I was much more likely to use or do things the one way I know.