From data fragmentation to AI-ready discovery

Pharmaceutical R&D spending is projected to exceed $340 billion by 2030, yet 90% of drug candidates still fail in clinical development with nearly half of those failures attributed to a lack of biological efficacy. The problem is not a shortage of data. Across the Design-Make-Test-Analyze (DMTA) cycle, experimental context is lost at every handoff: intent disappears, traceability breaks down and knowledge erodes. The result is repeated experiments, unreliable AI outputs and compliance risk, quietly draining the value of your research investment.

The true advantage isn’t better AI, it’s better foundations. When experimental intent is captured at the source and preserved across every stage of the R&D lifecycle, science becomes reproducible, traceable and AI-ready.

View the infographic to see how Siemens is helping pharma companies turn fragmented data into executable science.