INS and GPS are commonly integrated using a Kalman filter (KF) to provide a robust navigation solution, overcoming situations of GPS satellite signals blockage. This research presents an alternative method of bridging GPS outages requiring no prior knowledge of the INS and GPS sensor characteristics, called the artificial-intelligence-based segmented forward predictor. This method uses radial basis function neural networks to predict INS position and velocity errors during GPS outages, resulting in reliable performance. The performance of the proposed method is examined using real field test data of both navigational and tactical grade INS integrated with GPS. The results have shown that the proposed method outperforms KF, especially during long GPS outages.