Move from AI experiments to governance by design — not to slow progress, but to institutionalize trust, auditability, and reuse.
Many teams adopt GenAI faster than they update policies. Risk creeps in through unlogged prompts, unverifiable outputs, and unclear approvals when regulators ask, “Who reviewed this?”
Treat AI outputs like scientific evidence — reviewable, auditable, explainable. Build the controls into the workflow so integrity scales with speed.
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Policy Define what “responsible AI” means for Medical Affairs. Specify permitted use cases, data constraints, and human-in-the-loop checkpoints. |
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People Assign accountable roles — model stewards, reviewers, and business owners. Train teams on prompt hygiene and validation. |
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Process Log prompts, sources, decisions, and approvals. Maintain traceability from input to output to final use. |
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Platform Centralize model access, permissioning, and monitoring. Use model cards to document purpose, datasets, and reviewers. |
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Performance Track explainability, reproducibility, quality, and cycle time — not just volume. Use feedback to improve prompts and guardrails. |
- Higher adoption as confidence and clarity increase.
- Shorter approval cycles via pre-validated workflows and audit trails.
- Inspection readiness that turns risk management into an advantage.
Several leading pharmas use internal model cards to summarize purpose, datasets, and reviewer details for GenAI tools. The result: clearer accountability, reduced rework, and more reproducible outputs.
“In AI, governance is not bureaucracy — it is velocity with integrity.”
