Most technical problems don’t fail because solutions are missing.They fail because the goal was never precise enough to guide real thinking. “We need better quality.”“We need lower cost.”“We need higher throughput.” These sound reasonable.They’re not specific. In process industries: • “Quality” could mean purity, selectivity, polymorph stability, or biological potency• “Cost” could mean yield loss, […]

AI is powerful at accelerating work once the structure of the problem is clear. It can search faster. Explore broader design spaces. Test variations humans wouldn’t have time to consider. But AI does not decide what structure matters. That’s the limit most teams run into — quietly. When the problem is framed around tuning parameters,

AI is very good at answering questions.That’s also the problem. In technical R&D, the most dangerous situations aren’t where answers are missing —they’re where confidence arrives too early. Physics doesn’t fail loudly.It fails quietly. Small deviations compound.Margins shrink.Instabilities appear late. AI struggles precisely in these regions. Here’s why. AI systems are designed to reduce uncertainty.Physics,

Most engineering is optimization.And that’s not a failure. The economy runs on optimized trade-offs: These aren’t mistakes.They’re how products ship, plants run, and businesses stay profitable. If optimization didn’t work, modern industry wouldn’t exist. So when does the problem actually start? Not when a trade-off exists —but when a specific trade-off becomes too expensive to

Most R&D teams track innovation through ideas.New concepts. New formulations. New configurations. New “solutions.” But ideas aren’t where innovation succeeds or fails. The real unit of innovation is the contradiction the idea is trying to resolve. Here’s why. Two teams can generate very different ideas — and still fail in exactly the same way.Not because

Optimization often succeeds at exactly what it is told to optimize.In many AI-assisted TRIZ systems, that target is an abstraction rather than the physical mechanism that creates it. Most AI-augmented TRIZ systems start by encoding classical TRIZ parameters: Quality, Productivity, Cost, Reliability, Speed. On paper, this looks reasonable. These are the labels engineers use. But

Classical TRIZ is powerful — but it was built for a world of gears, levers, and mechanical assemblies. That matters. In mechanical systems, contradictions are often physical and visible: • Stronger material → heavier part• Faster motion → more wear• Higher force → more deformation The parameters are tangible.The mechanisms are clear. Process industries are

In technical R&D, I’ve learned something uncomfortable: Most teams don’t fail because solutions are unavailable.They fail because they’re solving the wrong problem — very efficiently. In chemical, biochemical, food, and pharmaceutical engineering, projects often start with statements like: These sound like problems.They’re not. They’re outcomes. What actually blocks progress usually sits underneath, unspoken: These aren’t