Material delivery plays a critical role in manufacturing efficiency, with manual retrieval introducing non-value-added (NVA) time and disrupting workflow continuity. Autonomous mobile robots (AMRs) can improve performance by enabling overlap between material transport and productive work, but their effectiveness depends on how they are deployed. In this work, a convolutional neural network (CNN)-based autonomous dispatch framework was implemented and tested in a controlled experimental setting. This study utilized a representative aerospace assembly task to evaluate three material delivery approaches across 60 runs, including manual walking, manual AMR dispatch, and autonomous AMR deployment. System performance was assessed using total operation time, panel lead times, and non-value-added time. Results showed that manual AMR dispatch significantly increased total operation time and non-value-added time due to sequential task execution. Autonomous deployment reduced this inefficiency by enabling preemptive material transport and overlap with operator activity, but did not significantly outperform manual walking under the tested conditions. Operator variability also influenced non-value-added time under automated dispatch. These results indicate that AMR effectiveness depends strongly on deployment timing and workflow synchronization, with the greatest potential benefits expected in environments that allow greater overlap between transport and productive work.