Life sciences AI adoption index
Learn your next steps for adopting AI in life sciences
AI is moving faster than most implementation playbooks. Take our AI Adoption Assessment and download the step-by-step guide to building, piloting, and scaling AI in regulated science.
Your AI adoption score
Answer nine questions covering executive commitment, data infrastructure, regulatory readiness, and more. You’ll see where your organization lines up.
A roadmap to guide your journey
Based on your score, you'll get insights and next steps specific to your stage, whether you're building the foundation, running early pilots, or ready to scale.
The Enterprise AI Transformation Guide for Life Sciences
A step-by-step playbook on AI governance, pilot selection, and scaling in regulated science with results from Novo Nordisk, FutureHouse, Garvan Institute, Bluenote, and Biomni.
10 min
Clinical study reports that previously took 10+ weeks, at Novo Nordisk
800x
Faster bioinformatics analysis: 35 minutes instead of three weeks, at Biomni (opens in new tab)
50–75%
Faster regulatory document production, at Bluenote (opens in new tab)
100x
Faster literature reviews, with reviews completed in days instead of months, at FutureHouse (opens in new tab)
In life sciences, AI errors have real consequences: a misplaced finding in a regulatory submission, a flawed protocol, a citation that doesn't hold up under review. Data is fragmented across discovery, development, and manufacturing. Frameworks like GxP, 21 CFR Part 11, and the EU AI Act set a compliance bar that most general-purpose AI deployments weren't designed to clear.
Build the architecture that makes AI deployments stick
How to secure executive sponsorship, build cross-functional coalitions, and address scientific skepticism directly. Includes governance frameworks your compliance and quality teams can sign off on before the first user logs in.
Choose the right projects and learn rigorously from every one
Explore use cases that work best for pilot programs, including scientific documentation, literature synthesis, and protocol analysis. Learn how to set success metrics before you go live, and how to design pilots that create cross-functional momentum.
Move from isolated wins to AI across discovery, clinical, and regulatory
earn how to structure centers of excellence with cross-functional representation, build upskilling programs that stick like hackathons, mentorship, or rotations, and govern AI that can scale.
Before-and-after numbers from four organizations
Each customer example covers their challenge, what they built on Claude, and the measured outcome across documentation time, analysis speed, and submission quality.
Discuss your use cases and an AI implementation strategy that fits your regulated industry.