This report investigates the incorporation of Artificial Intelligence (AI) into the product quality control process in the automotive manufacturing industry. Traditional quality inspection practices must compete with limitations due to human error, use of limited sample sizes, inconsistencies in working practices, and time increases in the inspection process. AI technologies, such as machine learning, computer vision, and predictive analytics have provided innovative ways to efficiently and accurately detect defects while allowing for worldwide tracking of processes. Using secondary data, case studies, and industry reports, the report details how leading players in the automotive space (Tesla, BMW, and Ford) use AI systems to improve product accuracy, minimize material waste, and modify operational practices. The study uses a thematic analysis and compare approach to examine the differences between AI methodologies for quality inspection compared to standard practices. By examining speed, cost effectiveness, and defect followup, this method of detecting defects with AI provides numerous advantages to as standard inspection practices. The report notes there is a five-phase execution model of project implementation and uses an interpretation of action planning based on the Six Sigma DMAIC (Define, Measure, Analyse, Improve, Control) methodology for continuing improvement. Also considered, significant challenges to an AI quality application were identified including costs, data protection and shift in working practices. Future directions were proposed, including the possibilities of generative AI, IoT enabled predictive maintenance, and robotics to create not just automated practices but opportunity for fully adaptable intelligent manufacturing ecosystems responsible for self-correcting defects. The report illustrates an opportunity to produce theoretical basis for AI to redefine automotive product quality assurance and develop strategic initiatives to incorporate AI in production.