Vision-guided robotic systems play a key role in industrial automation, particularly in flexible pick-and-place tasks. We present a modular approach that integrates automatic data extraction of 2D assembly drawings, object recognition, automatic robotic part placement, and augmented reality for quality inspection. The system combines a YOLOv8-based segmentation pipeline with classical shape-matching techniques to detect, identify, and align components during assembly. We apply a hybrid method to interpret assembly drawings that merge traditional computer vision with neural segmentation masks. The robotic setup features a 6-DOF arm controlled via Robot Operating System (ROS), with overhead cameras for object localization. This paper presents ongoing work. Preliminary results indicate improved segmentation accuracy using AI-based methods for extracting the foundational data from the 2D assembly drawings. Future work will focus on enhancing grasp and path planning, increasing placement precision, and improving the robustness of part detection. Additionally, we aim to generalize the system to previously unseen assembly plans and expand the feature extraction capabilities for complex assembly drawings.