This session explores the future of biotech through the story of a problem uncovered in real lab environments: the growing disconnect between how experiments happen and how they’re documented. We’ll trace how firsthand frustrations with manual data entry and traditional Electronic Lab Notebooks (ELNs) revealed hidden costs in time, reproducibility, and insight—and why those limitations could no longer be ignored. From that discovery emerged a new approach to lab workflows. We’ll unpack how real-time experiment capture and AI-driven data structuring can sit on top of existing ELNs to reduce documentation burden and unlock deeper downstream analysis. Along the way, we’ll separate AI hype from reality, sharing practical insights into what AI-enhanced labs can do today—and how tools like Dimenso are helping teams move toward more efficient, reproducible, and truly AI-native lab environments.
This session explores the future of biotech through the story of a problem uncovered in real lab environments: the growing disconnect between how experiments happen and how they’re documented. We’ll trace how firsthand frustrations with manual data entry and traditional Electronic Lab Notebooks (ELNs) revealed hidden costs in time, reproducibility, and insight—and why those limitations could no longer be ignored. From that discovery emerged a new approach to lab workflows. We’ll unpack how real-time experiment capture and AI-driven data structuring can sit on top of existing ELNs to reduce documentation burden and unlock deeper downstream analysis. Along the way, we’ll separate AI hype from reality, sharing practical insights into what AI-enhanced labs can do today—and how tools like Dimenso are helping teams move toward more efficient, reproducible, and truly AI-native lab environments.