A lot of criticism gets aimed at “AI + TRIZ.”
Some deserved. Some not.
Before talking about what’s missing, it’s worth being clear about what TRIZ-AI researchers have gotten right.
Because there is real progress here.
Three insights stand out.
First: Inventive problems have structure.
Hard engineering problems repeat because the same conflicts repeat. Contradictions aren’t random — they follow patterns across domains.
Encoding those patterns gives AI something better than raw data: structure.
Second: Abstraction enables transfer.
Mapping diverse problems into structured representations allows AI to recognize recurring conflict structures — and retrieve analogous cases across industries.
That’s powerful.
Third: Solution families are constrained and recurring.
Separation strategies, field substitutions, coupling and decoupling — these recur. AI can explore structured solution directions instead of searching blindly.
This is why TRIZ-AI research is ahead of most mainstream AI applications in R&D.
They moved beyond pure optimization toward structured, multi-criteria reasoning.
They treat knowledge as reusable — not anecdotal.
And most serious researchers aren’t claiming full automation of invention.
They frame AI as:
• A decision support tool
• A hypothesis generator
• A way to expand the search space
That restraint matters.
The issue isn’t the method.
It’s the domain.
Process industries expose weaknesses that don’t appear in discrete or mechanical systems.
When systems are continuous, coupled, regulated, and safety-bounded, contradictions are embedded in:
• Phase behavior
• Transport limitations
• Kinetic boundaries
• Regulatory constraints
These aren’t surface parameters. They’re mechanistic.
And methods that perform well in cleaner domains start to strain under that complexity.
Next: where TRIZ-AI research still struggles — especially in process systems.
Which part of TRIZ-AI research feels most promising in your work — and which part feels hardest to trust in practice?