Every AI-in-HR case study seems to follow the same arc: the pilot works, and then somehow it's running at scale. What's missing is the middle. That's the stretch where the model underneath doesn't yet match what the tool actually needs, where early wins nearly come apart, and where teams quietly re-design, re-work, and patch their way to something that finally runs across the whole organization. This session pulls back the curtain on that middle stretch the part no case study shows. Drawing on enterprise HR transformation experience, including leading workforce transformation, this talk maps what it actually takes to move AI in HR from a promising pilot to something that runs operationally, at scale: resetting role expectations, fixing the data and exception-handling architecture underneath the tool, measuring outcomes, and building the governance guardrails that keep the whole system trustworthy. Whether you're steering a rollout, building the tools, funding the initiative, or new to HR and trying to make sense of the hype, this session offers a clear, practical map of the work the highlight reel leaves out.