The Theory of Inventive Problem Solving (TRIZ) holds substantial promise for driving innovation, yet widespread adoption remains limited due to practitioners’ difficulty in relating its abstract principles to practical, industry-specific scenarios. This study details an innovative approach, integrating the Claude.ai Sonet 3.7 large language model to contextualize TRIZ principles specifically for the road construction industry. Through targeted prompts and clear visual illustrations, stakeholders transitioned from initial skepticism to enthusiastic acceptance. A comprehensive case study indicates that AI-generated contextual examples significantly reduced resistance to TRIZ implementation by approximately 78%, simultaneously improving productivity in initial sessions by about 64%. This paper elaborates on the methodology, outcomes, practical challenges, and provides recommendations for future research directions. Additionally, the study highlights specific TRIZ principles most effectively demonstrated by AI-generated examples, providing deeper insight into their practical applications within road construction. The research further discusses how incorporating artificial intelligence into TRIZ not only addresses initial adoption barriers but also fosters continuous innovative thinking and problem-solving within traditionally resistant sectors.