Adaptive designs have been quickly adopted in recent years by pharmaceutical companies to enhance drug development efficiency, especially in pre-confirmatory clinical trials. Even though interim adaptation allows opportunities to adjust imperfect assumptions made prior to initiation of trials, it is also critical to consider inherent variability in future data to be collected when making interim decisions on trial design adjustment. Bayesian prediction is proposed for such a consideration. We will illustrate the approach with applications to interim data monitoring, sample size re-estimation, and adaptive patient allocation.