Three years into widespread enterprise AI adoption, i’ve oberved a pattern. Companies that have invested seriously in automation are generating faster and more output yet still not seeing it show up on the bottom line. A developer finishes work let’s say 50% faster and still wait in the same review queue. A team that used to take two weeks to prototype now takes days and waits the same three weeks for sign-off. AI accelerates the work but the work still waits… For companies at an early stage of adoption this is less visible because the wins are real, and the expectations are still being set. Individual productivity goes up no doubt, certain workflows get cheaper to run, and that feels like progress because it is. But it is not the kind of progress that compounds. What compounds is decision speed, which requires rethinking who owns what call, at what threshold, and how quickly the business can move from signal to action. The question for most of our automation clients is no longer what to automate. It is whether the organizational structure around those automations is built to absorb what is already being produced. That is a harder problem than deploying another tool and it is the one that actually moves the number submitted by /u/Y00011000
Originally posted by u/Y00011000 on r/ArtificialInteligence