ISO 42001:2023 — the world's first international standard for AI Management Systems — has moved from aspirational framework to enforcement-adjacent reality. The EU AI Act, which began phased enforcement in 2024, explicitly references ISO 42001 as a conformity pathway for high-risk AI systems. For pharmaceutical and biotech companies deploying AI in regulated contexts, this changes the compliance calculation significantly.
Which Life Sciences AI Use Cases Are Affected
Not all AI systems require the same level of documentation rigor. Under both ISO 42001 and the EU AI Act, risk classification drives documentation requirements. High-risk systems in life sciences include AI used in clinical decision support, pharmacovigilance signal detection, drug discovery candidate screening, and manufacturing quality control with autonomous decision capability.
Lower-risk applications — administrative automation, document summarization, internal chatbots — require lighter documentation but still benefit from a structured AI governance framework.
The Four Documentation Requirements That Matter Most
1. AI System Inventory — A current, maintained register of all AI systems in use, their risk classification, their intended use, and their validation status. Many organizations are surprised to discover how many AI-adjacent tools have been deployed informally across departments.
2. AI Impact Assessment — For high-risk systems, a documented assessment of potential harms, bias risks, and patient safety implications. This is distinct from a traditional computer system validation risk assessment — it must address AI-specific failure modes including model drift, training data bias, and explainability gaps.
3. Human Oversight Controls — Documentation of human-in-the-loop (HITL) checkpoints for all high-risk AI decisions. FDA guidance on AI/ML-based Software as a Medical Device (SaMD) requires that AI recommendations in clinical contexts have defined override mechanisms and auditability.
4. Ongoing Monitoring Protocol — AI systems degrade over time as real-world data drifts from training data. ISO 42001 requires a defined monitoring program with trigger criteria for model revalidation.
Practical Starting Point
If your organization is deploying AI in any GxP context, start with an AI system inventory and risk classification exercise. This typically takes two to four weeks with appropriate subject matter expertise and provides the foundation for all subsequent governance documentation. Organizations that wait for regulatory mandates to formalize AI governance consistently find remediation costs significantly higher than proactive program development.