In recent years, industries are striving to adapt immersive technologies in the real-time assembly shop floor setting to achieve effective outcomes in terms of enabling the worker to handle the complex cognitive assembly task execution/training circumstances. The manufacturers are presently facing difficulties with the process of Augmented reality (AR) instruction creation for the required product assembly system. The existing AR instruction development process demands highly skilled experts and more time consumption. Hence, the manufacturers are presently relying on third parties to acquire the required AR instructions by providing the necessary assembly particulars as input. Where, the cost of developing AR instruction is another big challenging concern. Predominantly, the significance of providing exact assembly input is vital for the experts to develop the appropriate AR content without any errors. An automated approach is developed in this study to resolve the difficulties with assembly sequence input for the AR instructions development process. The automated approach affords four different provisions that include validation of an assembly sequence input prior to the AR instructions development stage, suggestions for assembly sequence input generation, simulation of validated assembly sequence in the Virtual reality (VR) environment for user perception, and generation of virtual content for AR visualizations. Subsequently, the generated digital contents from the automated approach are imparted into the AR Unity platform to develop the required AR instructions for the product assembly system. Notably, this study explores the AR customized marker technique to visualize the AR content in the user’s view of the physical context. Finally, the AR guidelines are built as apps for the selected 11-part assembly and deployed on the shop floor for the user to perceive the AR content through visualization devices for execution of assembly operations in a real-time context. In addition, the effectiveness of the automated approach is analyzed with the existing approach, and the consequences are explored through the process of receiving feedback from the experts about the error rate, cognitive level, and creation time.