You finish your part faster. Does the person waiting for the result get it any sooner?
Take a hypothetical sales proposal. Sales uses AI to draft it, finance to check the margin, operations to assess capacity. Each team saves time. But the proposal still sits in inboxes because nobody changed how the work moves between them.
My concern is that faster production can become more work for whoever comes next. If review capacity stays the same, producing more drafts may just create a longer queue.
I’d test one workflow from the initial request to the accepted result. Measure elapsed time, total human effort, corrections and time spent waiting. Then ask who owns the result once everyone has finished their individual task.
This isn’t an argument for removing human approval. I want people making decisions, not chasing inputs. AI could assemble the relevant information and run routine checks, with exceptions reaching someone who has the authority to resolve them. Some handoffs may not need to exist at all.
There are two gains worth keeping separate: less effort for the individual and a faster, more reliable process for everyone involved. Either can matter. But measuring one doesn’t prove the other.
For people using AI at work: where did the bottleneck move? What actually helped you fix it: a better tool, connected systems, clearer ownership, or fewer steps? Concrete examples, including cases where individual time savings were enough, would be useful.
[link] [comments]