Use of AI in the TRIZ Innovation Process: A TESE-Based Forecast

Currently, AI is mainly used in the TRIZ innovation process to find solutions to well-defined technical problems using TRIZ tools such as Inventive Principles, Standards and, occasionally, Functional Oriented Search (FOS). In practice, however, problem solving is usually the least time-consuming part of the innovation process, with most effort normally spent defining the overall goal of the innovation, identifying and analyzing the initial problem, selecting the best solution from the set of solutions found, and justifying its feasibility. Therefore, AI would be much more useful if it were introduced into these labor-intensive parts of the TRIZ innovation process as well, and undoubtedly AI developers will eventually try to automate the entire innovation process. The objectives of this paper are (1) to assess the current effectiveness of using AI in real TRIZ projects, (2) to predict the most likely sequence of future AI implementation in different parts of the TRIZ innovation process, and (3) to identify related challenges. The objectives are achieved by analyzing the composition and timing of various activities in a typical TRIZ project and applying the Trend of Decreasing Human Involvement to these activities, where a TRIZ project is considered a technological process that transforms an initial, poorly formulated problem into a viable solution/product. These results can be used by AI and TRIZ specialists to create a roadmap for integrating AI and TRIZ to produce a fully automated innovation process that is applicable to technical systems and, potentially, to business systems.