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AI Governance for Life Sciences: The Enterprise Framework for Compliant, Scalable AI

AI governance for life sciences. FDA and EU AI Act readiness, GxP validated systems, and ISO 42001 guidance for compliant, scalable AI.

Covered in the paper: 37 pages / 8 chapters
Framework topics: EU AI Act (Aug 2026), FDA CSA, GAMP 5, ISO 42001
Platform risk discussed: Veeva, Box, ServiceNow, Microsoft
90-day pathway for a governance baseline

AI governance framework

Controls, lifecycle, and partner risk in one operating model.

90-day
pathway

Governance

Define ownership, review points, and decision rights.

Validation

Map intended use, test strategy, and change control.

Data controls

Protect lineage, access, and policy boundaries.

Vendor AI risk

Track embedded AI across platform updates.

Lifecycle controls

01
Assess
02
Govern
03
Validate
04
Monitor

Focus areas covered

Life sciences governance, platform AI risk, and a practical 90-day path.

37 pages8 chapters90-day path
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AI governance for life sciences. FDA and EU AI Act readiness, GxP validated systems, and ISO 42001 guidance for compliant, scalable AI.

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Table of contents

What the framework covers

01

State of AI in Life Sciences

Why pilots stall before controlled production.

02

AI System Lifecycle

Governance from ideation through decommissioning.

03

Strategic Value Pillars

Revenue, efficiency, innovation, and risk mitigation.

04

Partner AI Blind Spots

Embedded AI in validated platform environments.

05

Vendor Assessment

AI-specific third-party risk dimensions.

06

Citizen Development

Business-led AI inside GxP guardrails.

07

USDM AI Services

Assessment, governance, and AIGaaS.

08

90-Day Pathway

Three entry points for a governance baseline.

Key stats from the paper

Why AI governance is now an operating-model issue.

The numbers are useful only when they point to action: controlled intake, validated lifecycle practices, platform AI oversight, and measurable governance baselines.

70–80%

of AI pilots fail to reach production

Why governance has to start before pilots scale.

20–40%

higher cost for under-optimized Vault environments

Platform operating models matter before AI gets layered in.

30–50%

reduction in administrative overhead

When governance, workflow, and evidence capture are designed together.

35–45%

reduction in compliance program costs

Using a unified framework instead of fragmented controls.

"The question is no longer whether AI belongs in life sciences. The question is whether your organization can govern it well enough to survive what comes next."

Life sciences organizations know what AI can do. What most don’t yet have is the infrastructure to do it safely, repeatably, and in a way that survives regulatory scrutiny.

In this comprehensive whitepaper, USDM’s AI Center of Excellence lays out the operational framework to close that gap — covering the AI system lifecycle, partner platform risks, vendor assessment, citizen development, and how to get started in 90 days.

Why This Matters Now

Three forces converging in 2026

Organizations that build unified AI governance infrastructure gain a compounding advantage. Those that don’t face a new category of regulatory exposure that will only become clearer as FDA and EU enforcement guidance matures.

Who This Is For

  • Quality and Regulatory Affairs leaders managing AI adoption in GxP environments
  • CIOs and CTOs responsible for validated platform governance
  • Chief AI Officers and Digital Transformation leaders building scalable AI programs
  • Compliance and Validation teams navigating FDA, EU AI Act, and ISO 42001 simultaneously

Contributors

Want proof that AI works in regulated environments? Download USDM’s AI in Life Sciences: 47 Use Cases for Quality, Regulatory, Clinical, and Manufacturing Teams — with quantified outcomes and inspection-ready architecture to help you move from pilot to production with confidence.

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