The New Scientific Method: Today’s Digital Lab Revolution

Speaker: Leah O’Brien, VP Global Head Digital Lab and Plant Operations, Sanofi

Zifo’s SiEE event 2026

The Ultimate Mission: Halving Time-to-Market with AI

Leah emphasizes that at Sanofi, the true driver behind digital lab revolution program is accelerating the delivery of life-saving innovations to patients. She notes that currently, the industry average for a new drug to go from discovery to a patient’s hands is 13 years. However, Leah shares Sanofi’s ambitious goal: to cut this timeline in half over the next few years using AI.

In this insightful presentation, Leah explores the delicate balance between pushing the boundaries of futuristic AI labs to achieve this goal, while ensuring the foundational “basics” of data management are set up for success.

Here are the key takeaways from the speech:

The “Lab of the Future” is Frictionless

• Ambient Capture Over Keyboards: The future lab replaces manual data entry with ambient data capture, disrupting traditional Electronic Lab Notebooks (ELNs) and LIMS freeing up scientists’ time.
• “Lab in the Loop”: Wet labs and dry labs are becoming so deeply integrated that predictive models guide bench tests, while real-time results from the bench continuously refine the models.
• Adaptive Robotics: Leah says the company is moving away from brittle, rigid automation toward “physical AI” and robots that evolve seamlessly alongside the science.

The Scientist of the Future is “Bilingual”

• AI Superpowers: Tomorrow’s scientists will leverage AI to assist with experimental design, automate protocol authoring, and generate predictive hypotheses out of billions of data points.
• Fluency in Both Science and AI: The future scientific workforce will be “bilingual”—fluent in both core sciences and AI tools (such as simulation, modeling, and data visualization).

Data “Vegetables” Before AI “Ice Cream”

• AI requires perfectly structured foundations. As Leah playfully notes, quoting Julie Huxley Jones, the VP at Vertex: “You can’t have your AI ice cream until you eat your data vegetables.” Achieving AI-ready, FAIR data is no longer an afterthought, but a heavily funded priority.

Transforming Infrastructure: Edge Computing & Scaling

• Edge Compute in the Lab: With massive data-generating instruments (like Cryo-EM) and the demands of a 24/7 “Lab in the Loop,” running analytics purely in the cloud causes latency issues. Leah says the company is seeing a critical shift toward heavy edge computing right inside the physical lab.
• Change Management: Successfully scaling AI pilots isn’t just a technology problem; human-centered design and effective business change management are more important than ever to drive real adoption.
• Biggest Opportunities for Speed: According to value framework mapping, the biggest current opportunities to shave off development time reside in optimizing clinical trials, regulatory reporting, and CMC (Chemistry, Manufacturing, and Controls) processes.