Most teams blame model quality when AI output is weak, but the bigger issue is usually workflow design. In this Ignite talk, I use a fast compare-and-contrast format to show how common AI usage patterns create slop, then contrast them with lightweight workflow shifts that produce better outcomes. The focus is practical: clearer task framing, better handoffs, verification loops, and one extra iteration before giving up. This is not a deep implementation tutorial. It is a concise, field-tested framework to help teams reduce rework and improve output quality quickly. Attendees will leave with simple behavior changes they can apply immediately.