I lead AI work inside a globally distributed team. The artifacts I spend most time on are synthetic datasets that behave correctly under real questions, agent libraries other teams can actually reuse, and evaluation harnesses that hold up to a skeptical reviewer.
Most of what I've learned doesn't generalize as a technique. It generalizes as a posture toward where the failure modes hide and who will own the output once it ships.