Responsible AI in Life Sciences Report: From Adoption to Trust at Scale
ACTO’s original industry research reveals how Life Sciences leaders are navigating AI governance, risk, and trust. Explore findings from executive roundtables and surveys, plus a new framework for evaluating and scaling agentic AI responsibly.
Responsible AI in Life Sciences: RAIL Report 2026
New industry research reveals how Life Sciences leaders are approaching AI governance, managing risk, and building the trust needed to scale agentic AI responsibly.
AI adoption is accelerating across Life Sciences,
but governance and accountability aren’t keeping pace.
ACTO’s Responsible AI in Life Sciences (RAIL) Report 2026 brings together global insights from eight executive roundtables with over 80 participants and a survey of 135 leaders in customer-facing roles across pharmaceutical, biotech, and medical device companies.
Discover what’s holding organizations back, where leaders see the greatest opportunities, and how the new RAIL Score provides a practical framework for evaluating agentic AI in this highly regulated industry.
The Industry is Ready for AI. Is It Ready to Trust It?
but concerns around governance, accountability, and responsible adoption remain.
Key research findings:
64%
Of leaders say their organizations have an AI governance process established or in development
58%
Say the Life Sciences industry lacks clear standards for responsible AI
54%
Remain concerned about how AI is being used within their own organizations
79%
Agree AI risk and ROI should be measured against how people perform the work today
These findings reveal a critical challenge: the ability to scale AI depends on more than technical readiness. Organizations need a clearer way to assess trust, manage risk, and demonstrate value.
What You'll Learn
What Does It Take to Scale Agentic AI Responsibly?
The ACTO RAIL Report goes beyond adoption trends to examine the decisions Life Sciences leaders are making as AI becomes part of everyday work.
Explore five critical areas:
1. Building Governance that Goes Beyond Approval
Understand why initial approval isn’t enough and how leaders are approaching ongoing oversight, accountability, and control.
2. Closing the
AI Trust Gap
Explore the disconnect between leaders’ personal confidence in AI and their concerns about how it’s being deployed across their organizations.
3. Prioritizing the Right
AI Use Cases
Learn how organizations are balancing potential value with governance complexity to determine where to start and what to build next.
4. Redefining Human and AI Responsibilities
Examine the importance of human judgment, connection, and strategic decision-making as AI agents take on more work.
5. Measuring AI Risk,
Value, and Performance
Discover why leaders are looking beyond efficiency and comparing AI outcomes against existing human-led processes.
A Practical Framework for Evaluating Responsible Agentic AI
How can Life Sciences organizations determine whether an AI system is trustworthy enough to scale?
The report introduces the RAIL Score, a structured assessment framework that evaluates agentic AI systems against six essential components:
- Governance
- Security
- Testing & Certification
- Human Centricity
- Value Creation
- Orchestration
Each component is scored to help organizations assess AI readiness, identify gaps, and make more informed deployment decisions.
The report also includes practical tools to help put the framework into action:
Move from evaluating what AI can do to understanding what it takes to deploy it responsibly in Life Sciences.
Build Your Responsible AI Strategy on Evidence
The decisions Life Sciences organizations make today will shape how agentic AI is governed, adopted, and scaled.
Get the research, insights, and practical tools to help your organization move forward with greater clarity and confidence.
Complimentary Original Industry Research from ACTO