At this year’s SmartLab Exchange Philadelphia, we started our Think Tank with the following question:
“Where does the gap between AI ambition and lab reality show up most in your organisation?”
Seventeen participants from across pharma, generics, biotech, chemicals, and consumer goods voted. The result was strikingly clear. Data AI‑readiness dominated the discussion, far ahead of culture, orchestration, or data volume itself. The challenge wasn’t a lack of data, it was fragmented formats, missing context, inconsistent metadata, and systems that don’t speak the same language.