AI art in creative ops: where's it actually adding value vs. creating new problems

been thinking about this a lot lately because the gap between the hype and the, day-to-day reality still feels pretty wide, even now that the tools have matured a lot. on the good side, using image gen for early concepting has genuinely changed how fast we can get to a visual direction. showing 8 rough concepts in a first meeting instead of 2 is real, and clients respond well to it. there's research pointing to meaningful productivity gains for teams that adopt text-to-image tools, though the numbers vary a lot depending on the workflow, and who's using them, so I'd take any specific figure with a grain of salt rather than treating it as a universal benchmark. the tools themselves have gotten noticeably better for iteration too. reference image support, inpainting, style consistency across a set, that stuff has improved enough that some of the old pain points are less painful. but the messy part nobody talks about enough is what happens after the generation step. the handoff from AI output to something actually usable still takes longer than people budget for, especially when brand consistency, specific characters, or print-ready specs are involved. typography in particular is still largely a human job, generated type is unreliable enough that most teams I know just don't trust it and redo it anyway. there's also a creeping homogenisation thing happening where the work starts feeling samey if nobody's actively pushing against the tool's defaults. that's gotten more noticeable as more teams are running the same models with similar prompts. the differentiation has to come from the art direction, not the generation step. the copyright and IP side is still genuinely unresolved. most teams I know default to Firefly or similar commercially-licensed tools for client-facing work because of the licensing positioning, but even that feels more like risk management than a real answer, especially as regulations keep shifting by region. the smarter move longer term seems to be fine-tuned or private models where you actually control the training, data, but the setup and governance overhead is real and not every team has the bandwidth for it. curious how others are handling the governance side of this. do you have actual guidelines around when AI output is okay to use as-is vs. when it needs a full human rework pass, or is it still pretty ad hoc?