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, and actionable next steps
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.
What organizations like yours are achieving with Claude
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
50–75%
Faster regulatory document production, at Bluenote
100x
Faster literature reviews, with reviews completed in days instead of months, at FutureHouse
Take the assessment and download the guide
Your assessment results
You're ready to build AI right from day one
You're at the start of your AI journey. Beginning here means you get to build on solid ground: with executive sponsorship and clear governance established early, your first initiatives are set up to gain traction and stick.
Where to focus
Secure an executive sponsor who can clear obstacles and commit beyond a single budget cycle.
Establish governance early: access controls, usage guidelines, and quality standards built for GxP and 21 CFR Part 11 from day one. Building compliance in from the start is one of the biggest advantages of beginning here; you'll never have to retrofit it.
Start with one or two narrow pilots in low-risk, high-value areas like scientific documentation, where AI speeds up existing workflows without altering scientific judgment.
Step 1 of the guide, "Lay the foundation," covers this ground in detail.
- You've built momentum, now it's time to operationalize
You have real momentum: you’ve assembled the right team, early infrastructure, and a few impactful pilots. It’s time to lean into scaled initiatives that engage cross-functional teams.
Where to focus
Run three to five strategic pilots while you keep strengthening your foundation. Scientific documentation, literature review, and protocol analysis are proven starting points.
Set success metrics before you launch: adoption, efficiency, quality, scientific impact, and satisfaction. Track them weekly.
Design pilots to showcase cross-functional potential, so a win in research sparks ideas in clinical and regulatory.
Step 2 of the guide, "Launch a pilot," walks through pilot selection and post-mortems.
You're ready to scale
Your foundation is strong: committed leadership, modern infrastructure, and AI already in production. You've moved past proving AI works — now you're positioned to scale it, weaving AI through discovery, clinical development, and regulatory work.
Where to focus
Launch a comprehensive program with multiple pilots across functions, backed by a structured rollout.
Stand up a center of excellence with technical architects, domain experts, and data scientists, plus rotation programs that keep it connected to the science.
Invest in upskilling: hackathons, peer mentorship, and certification tied to real workflows rather than compliance modules.
Step 3 of the guide, "Scale impact," along with the Novo Nordisk, Bluenote, Biomni, and FutureHouse examples, show what this looks like in practice.
Get the Enterprise AI Transformation Guide for Life Sciences
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.
Our guide to deploying AI in regulated science covers what matters at each step, built alongside Anthropic customers already running these programs.
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.
On June 30, we’ll go further at The Briefing: AI for Science. Tune in for live product announcements, conversations with peers running these programs, and a look at where leading organizations are heading next.
Get advice on your AI strategy
Discuss your use cases and an AI implementation strategy that fits your regulated industry.