AI is moving from experimentation into regulated life sciences workflows
The hard part is no longer proving that AI can be useful. It is proving that AI can be governed, validated, monitored, and defended when it influences GxP decisions.
This white paper helps Quality, Regulatory, IT, Data, and executive leaders prepare for AI compliance expectations before pilots become production risk. It explains how to evaluate AI use cases through intended use, data integrity, model transparency, vendor oversight, validation strategy, and lifecycle monitoring.
FDA activity has made the message clearer. In January 2025, FDA published draft guidance for AI-enabled device software functions and draft guidance on AI used to support regulatory decision-making for drugs and biological products. The agency has also used enforcement activity, including the Exer Labs warning letter, to reinforce that AI claims and regulated decision support can trigger device-level and quality-system expectations.
What's inside
- Classify AI risk by intended use: separate productivity tools from AI that influences labeling, safety, quality, clinical, manufacturing, or regulatory decisions.
- Build evidence for AI credibility: connect context of use, training data, model performance, limitations, verification, and human review into a defensible package.
- Control data and model lifecycle: manage lineage, access, versioning, drift, bias, change impact, retraining, and ongoing monitoring.
- Govern vendor-supplied AI: assess embedded AI in QMS, LIMS, MES, CTMS, RIM, eTMF, cloud, and analytics platforms before relying on outputs.
- Prepare teams for inspection questions: align Quality, Regulatory, IT, Data, and business owners around accountability and records.
Why AI compliance is now an operating-model issue
Life sciences companies are already using AI across document review, quality event triage, regulatory intelligence, clinical operations, manufacturing analytics, knowledge search, and commercial workflows. Those use cases can create value, but they also change how decisions are made and how evidence must be maintained.
The compliance question is not simply whether a model is accurate. Teams need to show what the model is intended to do, what data shaped it, where human judgment remains, how outputs are verified, how change is controlled, and how performance is monitored after deployment. A modern, risk-based approach to computer software assurance (CSA) gives teams a way to focus validation effort where AI actually affects quality and patient risk.
Current regulatory signals to watch
FDA's January 2025 AI draft guidance for drug and biological product submissions describes a risk-based credibility assessment framework for AI models used to produce information or data supporting regulatory decisions about safety, effectiveness, or quality. FDA's AI-enabled device software draft guidance focuses on lifecycle management, marketing submission recommendations, and documentation across the total product lifecycle.
Those documents are draft guidance, but they are still useful operating signals. They point toward the same core expectations: define the context of use, document data and model development, evaluate risk, establish credibility, preserve transparency, and monitor performance over time. Many of these expectations are extensions of controls life sciences teams already know from 21 CFR Part 11 for electronic records and signatures.
The Exer Labs warning letter shows the practical risk. When AI-enabled software is marketed or used in ways that support screening, diagnosis, treatment, claims, or other regulated decision-making, companies may face device classification, premarket, design control, CAPA, supplier, training, and quality-system expectations.
Control metrics to track before AI scales
Useful AI governance metrics should tell leaders whether AI use is controlled, explainable, monitored, and ready for inspection. Avoid vanity metrics that only count pilots or users.
Several of these metrics live or die on the underlying data. Strong data integrity in life sciences practices, including ALCOA+, lineage, and audit trails, are what make AI inputs and outputs trustworthy and inspection-ready.
What the white paper covers
- Regulatory posture: how FDA, global regulators, and life sciences quality expectations are converging around trustworthy AI practices.
- Risk-based governance: how to evaluate AI by intended use, business process, decision impact, and patient/product risk.
- Validation and credibility: how to think about model performance, explainability, verification, and documented evidence in regulated workflows.
- Data integrity and transparency: how ALCOA+, audit trails, lineage, access controls, and version history apply to AI inputs and outputs.
- Operational readiness: how Quality, Regulatory, IT, Data, and business teams can work from one shared AI governance model.
Govern the AI you build and the AI you buy
Most life sciences AI risk is not just the models you train. It is the AI already embedded in your QMS, LIMS, MES, CTMS, RIM, eTMF, cloud, and analytics platforms. Treating those vendors with the same rigor you apply to any other supplier, through structured third-party risk management and life sciences cybersecurity controls, keeps embedded AI from becoming an unowned compliance gap.
Who should download it
- Quality and validation leaders building AI governance inside GxP systems.
- Regulatory leaders evaluating how AI affects submissions, labeling, safety, and health authority interactions.
- IT, Data, and AI leaders deploying models, embedded platform AI, analytics, automation, or agentic workflows.
- Executives who need AI adoption to move faster without creating inspection, data integrity, or vendor risk.
FAQ: AI regulatory compliance in life sciences
What does the FDA expect for AI used in regulated life sciences decisions?
FDA's January 2025 draft guidance points toward a consistent set of expectations: define the AI's context of use, document how data and the model were developed, evaluate risk, establish credibility through evidence, preserve transparency, and monitor performance over time. The drug and biological product draft guidance describes a risk-based credibility assessment framework, while the AI-enabled device software draft guidance focuses on lifecycle management across the total product lifecycle.
How do I decide which AI use cases need formal validation?
Classify AI by intended use. Separate productivity tools from AI that influences labeling, safety, quality, clinical, manufacturing, or regulatory decisions. The higher the decision impact and patient or product risk, the more credibility evidence, human oversight, and change control you need. A risk-based computer software assurance approach helps focus validation effort where it actually matters.
Does AI governance apply to vendor and embedded AI, not just models we build?
Yes. AI is increasingly embedded in QMS, LIMS, MES, CTMS, RIM, eTMF, cloud, and analytics platforms. Before relying on those outputs, assess the embedded AI through third-party risk management so vendor-supplied AI is owned, documented, and defensible.
What makes AI inputs and outputs defensible during an inspection?
Documented data lineage, access permissions, retention, audit trails, and ALCOA+ controls. Strong data integrity practices, paired with 21 CFR Part 11 controls for electronic records and signatures, are what let Quality and Regulatory stand behind AI-influenced decisions.
How does USDM help organizations get AI to production safely?
USDM provides AI readiness assessments, use-case inventory and risk classification, validation strategy, data governance, vendor AI assessment, lifecycle monitoring, and operating models for human oversight, all anchored in AI governance and compliance. The goal is to move AI from pilots to production in a way Quality can defend, Regulatory can understand, IT can operate, and leadership can trust.
How USDM helps
USDM helps life sciences organizations turn AI ambition into controlled execution. That includes AI readiness assessments, use-case inventory and risk classification, validation strategy, data governance, vendor AI assessment, lifecycle monitoring, and operating models for human oversight.
The goal is practical: help teams move AI from pilots to production in a way Quality can defend, Regulatory can understand, IT can operate, and leadership can trust.