Artificial Intelligence promises to revolutionize tire production with predictive quality, real-time monitoring, and adaptive control. But how far have we really come? This talk critically examines the gap between expectations and current capabilities. Using examples from laser-based processes, such as inner-liner cleaning, sidewall marking and mold cleaning, we explore where AI delivers tangible benefits and where challenges remain. Issues like data quality and availability, on-premise training, and real-time decision-making often limit practical implementation. Are we ready for fully autonomous quality control, or is AI still more hype than reality?

• Where AI already creates value in tire manufacturing, illustrated through laser-based applications such as inner-liner cleaning and sidewall marking.
• Which technical and organizational barries limit AI adoption, including data quality, on-premise model training and real-time decision-making.
• How close the industry is to autonomous quality control, and what steps are needed to move to reliable, scalable production solutions.