In this talk I present a $(1-1/e)^2$ -approximation for adaptive seeding of general monotone submodular functions. Our algorithm uses a new concept we call \emph{locally-adaptive} policies. This policies combine a global non-adaptive structure with local adaptive decisions. We show this policies have good adaptivity gap and allow for good approximations.