From AI pilot to AI in practice: what it takes to make laboratory AI work at scale
The next generation of scientific discovery will be defined not by which organizations adopted AI, but by which ones made it work inside the lab. The difference between a tool that impresses in a demo and one that genuinely accelerates R&D comes down to a single factor: is the AI embedded in the scientific workflow, working from connected data in real time, or is it bolted on as a parallel process that scientists have to work around?
For AI to deliver, the informatics platform underneath the science has to be built for it. A modern, AI-centric platform with a unified data model is not a prerequisite to be checked off before the “real” work begins. It is the condition that determines whether AI delivers practical value at all: surfacing context during decision-making, coordinating analytical tools without manual intervention, and building a scientific record that compounds rather than fragments over time.
Read More
