Evaluating the Effectiveness of Generative AI in TRIZ: A Comparative Case Study

Abstract

The rapid advances in generative AI technologies have sparked a debate among researchers on their role in the innovation process, particularly regarding their problem-solving and idea-generation capabilities. While researchers theorise the potential of generative AI in conjunction with TRIZ (Theory of Inventive Problem Solving), evaluating its current state and understanding its practicality is equally critical. Hence, this paper provides evidence of generative AI’s ability to offer solutions in real innovation projects. Our exploratory study compares the results of an actual innovation project in a professional consulting-like setting using traditionally applied modern TRIZ tools against generative AI-assisted results for the same customer-defined problem. The comparison focuses on the solutions’ degree of similarity, depth, and breadth. Additionally, our research identifies the advantages, disadvantages, and feasibility of using generative AI in problem-solving and innovation projects. Our findings indicate that combining generative AI and TRIZ produces feasible, cross-domain preliminary conceptual directions with satisfactory scientific substantiation. Lastly, we recommend suitable use cases for innovation managers and TRIZ practitioners, highlighting how the TRIZ-GPT combination can save considerable time exploring preliminary concepts and idea generation during problem-solving.