In my experience, the problem is that once a codebase passes like ~50,000 lines, it's too big for AI to make good architectural choices (this is controversial, but I have yet to see evidence otherwise).

So what I do is mostly use the AI to understand things like architecture, encapsulation, deployment pipelines, types, migration structure, etc.

It's a lot of "Audit the callers of X and give me a report" as well as reading code. I'm looking for stuff that doesn't pass my smell test, especially relating type types/interfaces. I look at the AI recommendations, but they're only useful once I've scoped them down to more granular questions, such as assessing the interfaces between specific layers of a pipeline.

That process is about understanding the shape of the solution that exists and then trying to formulate what shape it should have.

Then it's a lot of identifying a very boring, vanilla architectural target and moving towards it in phases, using the strangler fig pattern pretty aggressively. I review every PR, but mostly at the level of architecture rather than every specific line.