AI-Powered TRIZ: Elevating Corporate Creativity

This text explores how AI-powered TRIZ can be used alongside other creative thinking techniques to develop professionals' creative thinking skills.

The original study was presented at the 31st Paris TRIZ Conference on 18-19 September 2025.

The executive summary explains this approach to developing creative thinking skills among corporate professionals.

Authors: Yağız Bora Kaykayoğlu and Celaleddin Ruhi Kaykayoğlu

Organization: Kaykayoglu Innovation Group

1. The Need for Creative Thinking in Organizations

Changing markets, disruptive technologies, and complex customer demands are testing organizations' established problem-solving methods. This is why creative thinking is needed across different parts of an organization [1,2]. Structured creativity methods can help organizations work through these problems more methodically.

1.1 The Limits of Traditional Methods

Idea-generation methods such as classic brainstorming depend heavily on free association, chance, and how well participants work together [3]. Three limitations stand out in organizations:

• Low Repeatability: Results depend heavily on the participant's current mood, energy level, and the quality of the facilitator.

• Cognitive Bias: Groupthink frequently occurs in high-stakes corporate environments. Dominant voices steer ideas toward repetitive, low-risk, or safe patterns.

• Weak Connection to Implementation: Ideas generated in brainstorming are often disconnected from actual technical constraints and economic viability. Ideas stall at an early stage.

The challenge is to structure the thinking process without suppressing creativity. Teams need a way to consider ideas alongside technical constraints and the conditions for implementation.

1.2 Systematic Problem Solving with TRIZ

TRIZ (Theory of Inventive Problem Solving) was derived from the empirical analysis of hundreds of thousands of successful patents worldwide [4]. It treats inventive problem solving as a teachable, systematic process. The first step is to identify the system's central contradiction. For example, the goal might be to increase a product's strength while reducing its total weight. TRIZ provides the following tools for working through these opposing demands:

• Contradiction Analysis: Mapping opposing system requirements and eliminating trade-offs.

• 40 Inventive Principles: A universal catalog of cross-industry solution strategies (e.g., Segmentation, Dynamics).

• Contradiction Matrix: A matrix mapping 39 different engineering parameters to the relevant Inventive Principles.

• Substance-Field Analysis: Visualizing internal resource interactions and identifying paths to innovation.

2. Using TRIZ Tools with AI

Using AI to work with TRIZ's complex engineering tools can make these methods easier for different teams to use. The TRIZGPT framework we developed at Kaykayoglu Innovation Group uses natural language processing, pattern recognition, and semantic reasoning. Its functions include:

• Automated Contradiction Extraction: Natural language processing tools analyze design briefs, customer data, and technical incident reports to identify engineering contradictions that are not explicitly stated in the text.

• Computational Recommendation: Machine learning models process extensive data pools to automatically recommend targeted Inventive Principles.

• Ideality Scoring Systems: AI calculates the effectiveness of ideas by comparing their projected benefits with system costs and harms.

This combination allows teams to work on problems that have yet to arise as well as those they already face. Complex inventive methods become more accessible to early-career professionals and cross-functional teams.

3. Working with Classical Creativity Techniques

AI-powered TRIZ adds an analytical structure to creativity exercises based on generating ideas in different directions [5,6,7]. Here is how this combination can be used with different techniques:

• Structured Brainstorming: Instead of open-ended brainstorming, AI-powered TRIZ tracks natural language descriptions, isolates the core technical challenge, and suggests directions for thinking through relevant principles (e.g., Principle 1 Segmentation, Principle 15 Dynamics) [8,9,10].

• Working with SCAMPER: Each SCAMPER question category is mapped to relevant TRIZ principles. TRIZGPT is used to find analogies from different industries and explore possible outcomes through simulations.

• Mind Mapping: TRIZGPT automatically extracts the system's functional constraints. Visually clustered, spontaneously developing ideas are linked to the TRIZ contradiction matrix. The result is a semi-structured map that brings ideas and system constraints into the same view.

• Ideation Workshops: TRIZGPT can serve as a digital assistant during a workshop. Initial ideas are compared with scientific and empirical data. Eliminating paths that appear unfeasible helps the team move on to other options more quickly.

4. Real-World Applications and Results

The article describes this combination through four corporate application examples:

• Example 1: Next-Generation Wearable Technology (Product Development): A technology startup combined Mind Mapping with TRIZ Ideality parameters to develop the concept of an optimally designed, zero-energy-consumption smartwatch. Drawing on data from AI trained on patent data, the team overcame historical design errors, identified thermoelectric energy harvesting pathways, and developed a prototype that reduced battery consumption by 30%.

• Example 2: Ecosystem-Inspired Retail Strategy (Biomimicry): A retail company experiencing declining customer engagement paired Swarm Intelligence models with TRIZ Substance-Field Analysis. AI built consumer profiles based on the optimization behaviors of ants and honeybees, balanced personalization with data privacy through camouflage analogies (e.g., cuttlefish), and designed an ecosystem-style loyalty program that increased customer loyalty by 25%.

• Example 3: Eco-Friendly Product Packaging (Metaphorical Thinking): A food manufacturer sought an eco-friendly packaging solution that protected the product and was easy to open. Using the structural metaphor of a “banana peel,” AI mapped the problem to TRIZ Principle 1 (Segmentation) and Principle 30 (Flexible Shells). Biodegradable packaging was developed that maintained the required structural integrity and reduced opening effort by 60%.

• Example 4: Multisensory Hotel Customer Experience (“8 Minutes, 8 Ideas”): A hotel chain whose customer satisfaction score had plateaued applied the “8 Minutes, 8 Ideas” rapid ideation technique. Guided by emotional forest metaphors and aided by TRIZ Principle 32 (Color Changes) and Principle 24 (Intermediary), the customer experience team introduced customizable room environments and increased the pilot location's satisfaction score by 22%.

5. Conclusion

In this approach, human intuition, TRIZ contradiction analysis, and AI suggestions work on the same problem. Defining the contradiction clearly gives the search for a solution a direction. Classical creativity techniques can then be used to develop different options within that direction.

For an initial trial, list tasks that recur each month and the opposing demands within them. Write one contradiction per line, then map the contradictions to the TRIZ contradiction matrix. After choosing three inventive principles and using AI to generate more options, test one direction in a small trial. Evaluate the result in terms of added benefit and reduced cost.

References

1. Osborn, A. F. (1953). Applied Imagination: Principles and Procedures of Creative Problem-Solving. New York: Charles Scribner’s Sons.

2. De Bono, E. (1992). Serious Creativity: Using the Power of Lateral Thinking to Create New Ideas. New York: Harper Business.

3. Runco, M. A., & Pritzker, S. R. (Eds.). (2020). Encyclopedia of creativity (3rd ed.). Academic Press.

4. Karen Gadd, TRIZ for Engineers: Enabling Inventive Problem Solving, John Wiley & Sons, ISBN: 978-0470741887, 2011.

5. Pheunghua, T., et al. (2023). Enhancing Idea Generation and Problem Solving: Leveraging Generative AI and TRIZ Tools. In ICSI/GCSI 2023 Proceedings.

6. Zhang, J. (2021). Exploring the Application of Traditional Elements in Cultural and Creative Product Design. Art and Design Review, 9, 332–340.

7. Tanasak, P. (2024). The evolving landscape of TRIZ: A generative AI-powered perspective. In IFIP Advances in Information and Communication Technology. Springer.

8. Orloff, M. A. (2020). ABC-TRIZ: Introduction to Creative Design Thinking with Modern TRIZ Modeling. Springer.

9. Cavallucci, D., Brad, S., & Livotov, P. (Eds.). (2020). Systematic Complex Problem Solving in the Age of Digitalization and Open Innovation. Springer.

10. AutoTRIZ Team. (2024). AutoTRIZ: Automating Engineering Innovation with TRIZ and Large Language Models. arXiv preprint. https://arxiv.org/abs/2403.13002

The application examples and percentages in this article are claims made in the source text. They are not presented as verified client results achieved by Kaykayoğlu.