A $2M problem. Brilliant engineers. Cutting-edge tech. Unlimited budget.8 months stuck. The client: “If only we could think outside the box.”My first question: “Can you define the box you’re stuck in?”The room: silence. Most teams can’t. What I mean by “the box” Your box = your current search space—the defaults you treat as given: goals, […]
Post 1. The Box, where smart people go wrong Read More »
Across this series, one theme kept returning: Most teams do not need more ideas first.They need clearer contradictions, better judgment, and stronger discipline about what not to pursue. That is why TRIZ-AI will not fail because of algorithms. It will fail because leaders accept the wrong outputs. If you are responsible for high-stakes technical decisions,
Post 16 — What R&D Leaders Should Demand from TRIZ-AI Tools Read More »
Most innovation systems are built around ideas. Generate more. Test faster. Filter harder. That works, until the real problem is not a shortage of ideas. When innovation is reframed as constraint resolution, the change is not just in output. It is in control. Ideas compete. Constraints decide. In idea-driven work, discussions drift. Preferences dominate. Feasibility
Post 15 — What Changes When Innovation Becomes Constraint Resolution Read More »
Every organization has trade-offs it has learned to live with. That is normal. Engineering and business do not run on inventive solutions alone. They run on cost, speed, risk, validation burden, capital, and operational disruption. In practice, trade-offs and optimization are often the default not because people lack imagination, but because the alternative is harder
Post 14 — The Cost of Treating Trade-offs as Inevitable Read More »
Many organizations do not fail because they lack ideas. They fail because they solve the safest problem first. The safe problem is familiar.It fits existing tools.It stays within one team.It avoids regulatory risk.It produces clean progress slides. So teams optimize what is easy to change. A setpoint.A yield metric.A local inefficiency. Meanwhile, the real constraint
Post 13 — Why Organizations Solve the Safest Problem First — Not the Right One Read More »
Not every contradiction can be eliminated.That makes people uncomfortable. It shouldn’t. When TRIZ says “no,” it’s often doing its most valuable work. Most innovation approaches — and nearly every AI tool today — assume every goal is achievable. When teams hit resistance, the default response is to try harder, add complexity, or assume someone else
Post 12 — When TRIZ Says “No” — and Why That’s a Feature Read More »
Process systems don’t fail at the surface. They often fail at boundaries. Phase transitions. Stability limits. Transport ceilings. Regulatory thresholds.Many failure modes are delayed. Some are irreversible. That’s what makes process industries the ultimate stress test for TRIZ-AI. 1) You can’t isolate the system In many domains, components can be tested independently.In process industries, thermodynamics,
Post 11 — Why Process Industries Are the Hardest Test for TRIZ-AI Read More »
TRIZ-AI has made real progress.Process industries expose where its assumptions start to strain. Here’s where most approaches still fall short. 1. Continuous systems don’t respect discrete logic Most TRIZ-AI frameworks decompose problems into neat states: before vs. after, conflict vs. resolution. Process systems don’t behave that way. Variables shift continuously. Effects overlap. Small changes propagate
Post 10 — Where TRIZ-AI Research Still Struggles in Process Systems Read More »
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
Post 9: What TRIZ-AI Researchers Get Right Read More »
Not all “improvements” are progress.Sometimes a trade-off disappears. More often, it relocates. Lower temperature reduces degradation — but increases cycle time.Higher mixing improves uniformity — but increases shear damage.Stricter control improves quality — but narrows operating windows. The tension didn’t vanish. It shifted. Optimization redistributes trade-offs.Contradiction resolution removes them. How do you tell the difference?
Post 8: The Difference Between Eliminating a Trade-Off and Moving It Read More »
