We show how the convergence time of an adaptive network can be estimated in a distributed manner by the agents. Using this procedure, we propose a distributed mechanism for the nodes to switch from using fixed doubly-stochastic combination weights to adaptive combination weights. By doing so, and by knowing when to switch, the agents are able to enhance their steady-state mean-square-error performance without degrading the rate of convergence during the transient phase of the learning algorithm.