We consider the problem of mining high-utility plans from historical plan databases that can be used to transform customers from one class to other, more desirable classes. Traditional data mining algorithms are focused on finding frequent sequences. But high frequency may not imply low costs and high benefits. Traditional Markov Decision Process (MDP) algorithms are designed to address this issue by bringing in the concept of utility, but these algorithms are also known to be expensive to execute. In this paper, we present a novel algorithm AUPlan which automatically generates sequential plans with high utility by combining data mining and AI planning. These high-utility plans could be used to convert groups of customers from less desirable states to more desirable ones. Our algorithm adapts the Apriori algorithm by considering the concepts of plans and utilities. We show through empirical studies that planning using our integrated algorithm produces high-utility plans efficiently.