Everyone is talking about AI in R&D. Fewer people are talking about what makes it actually work. The bottleneck isn't the algorithm. It's not data volume...
It's structure, and specifically, the absence of it.
Most scientific data today has been digitized. But digitization is not orchestration. Experimental records exist, but the continuity of intent, process, material state, and decision-making doesn't travel with the work. Context gets left behind at every handoff, from discovery to development to manufacturing.
The result? AI that can analyze data in isolation, but can't reason meaningfully about how that work was actually done.
The shift that changes this isn't a better model. It's process modeling: explicitly defining every step of an experiment as structure, not narrative. When scientific work is represented that way, AI gains something it otherwise lacks: a stable, contextualized foundation to reason from.
The payoff extends beyond AI performance, too. It creates a common data language across the entire R&D lifecycle, one where insight generated at the bench doesn't get lost before it reaches development, scale-up, or manufacturing.
Structured science isn't a constraint on innovation. It's the multiplier of it.
Read the full article here: https://lnkd.in/gwuKAxbu