Most AI pilots don’t die because the models are weak; they die because we chase quick visibility and never build a trustworthy core. The pattern is familiar: a shiny dashboard, a dump of static role documents, a synthetic task list—and early applause. Underneath: stale inputs, no practitioner validation, silent drift. That’s the pilot trap. This session shows how to replace it with a working foundation: continuous, high‑resolution capture of what people (and systems) actually do; a simple autonomy spectrum to tell augmentation from real substitution; governance and consent built in at the start; fast human validation loops; and drift sensing before decisions harden. You’ll leave with a phased execution model, a small set of survivability metrics (input fidelity, adoption vs. confidence gap, drift index), and bias guardrails so leadership pauses when excitement outruns evidence. The outcome: moving AI from demo theater to something teams can trust—and improve—week after week.