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At FreyrONE, regulatory experts came together in focused breakout groups to discuss emerging regulatory trends, the role of AI, and how the industry can navigate complexity without compromising quality or patient safety. Here’s what stood out across the conversations:
AI Adoption Must Be Value-Driven and Risk-Aware
AI adoption should start with clear value, not technology hype.
The most effective regulatory AI use cases should demonstrate:
- High impact with low regulatory risk
- Clear business and compliance value
- Strong feasibility and speed to implement
AI is not intended to replace regulatory expertise. Instead, it should augment human judgment, supported by appropriate controls, validation, and governance framework
Data Is the Foundation of Regulatory AI
Participants emphasized that successful AI adoption depends on:
- Data quality, integrity, and traceability
- Clear ownership of RA, safety, clinical, labeling, and quality data
- Strong understanding of data lineage, authenticity, and “age of data”
Without trusted data, AI introduces risk rather than reducing it.
Focus Areas for Near-Term AI Enablement
The most promising and practical use cases identified included:
- HA correspondence and responses
- Change impact assessments (CMC, labeling, artwork)
- Content QC and consistency checks
- RIM data management and document generation
- Lifecycle maintenance activities (comparisons, sanity checks, standardization)
- AI agents and chatbots for process guidance
- KPI tracking and strategic risk analysis
These areas offer quick wins with measurable time and effort savings.
Quality, Compliance & “Right-First-Time” Submissions
AI can meaningfully improve submission quality when applied responsibly.
- Reduce manual effort and repetition
- Lower human error and subjectivity
- Improve submission quality and compliance
- Enable right-first-time regulatory outcomes
This directly translates to time savings and reduced cycle times.
Human-in-the-Loop Is Non-Negotiable
While AI can drive efficiency, participants highlighted the importance of:
- Human oversight and validation
- Clear understanding of model limitations
- Robust QC checks and governance
- Thoughtful prioritization of use cases
AI should support decisions—not make them in isolation.
Patient & Consumer Focus Remains Central
Ultimately, all innovation must tie back to:
- Patient safety
- Better risk mitigation
- Faster access to high-quality medicines
Technology is an enabler, but patient impact is the true north.
The Takeaway
Regulatory transformation is no longer about whether to adopt AI—but how responsibly, strategically, and collaboratively it’s done. The future lies in integrated data, governed AI, and empowered regulatory professionals.
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