LOCAL TIME
From pilots to production: AI across R&D, safety, regulatory, and clinical
Life sciences organizations are moving quickly to explore generative and agentic AI, but many are still working through how to move from promising pilots to production-ready solutions that can scale across regulated workflows. As AI becomes embedded in areas such as pharmacovigilance, regulatory operations, clinical development, medical affairs, precision medicine and commercial processes, organizations need more than experimentation — they need trusted AI strategies that balance speed, quality, governance and human oversight.
In this webinar, experts from IBM and GNQ Insilico will discuss:
- How Agentic AI and AI-first strategies have accelerated time-to-value in regulatory submission processes related to IND, NDA and BLA applications, helping bring products to market faster.
- What it takes to operationalize AI across complex life sciences environments, and how biological reasoning models and quantum computing are changing what AI can actually explain — not just predict — across the molecule-to-patient journey.
The conversation will share solutions currently in use and explore how organizations can identify high-value use cases, demonstrate human-in-the-loop workflows, build trust with business and compliance stakeholders by grounding AI outputs in auditable, mechanism-based reasoning rather than opaque correlation, and measure impact beyond initial proofs of concept. Speakers will also share perspectives on the data, governance and change management foundations needed to scale AI responsibly while supporting better decision-making, efficiency and patient-centered outcomes.
Key Takeaways
- How healthcare and life sciences organizations can move from AI pilots to scalable production use cases
- How Agentic AI enables humans to accelerate the authoring and review of R&D, safety, regulatory and clinical documents, with support for GxP, 21 CFR Part 11 and HIPAA/GDPR requirements
- How causal, mechanism-based AI reasoning — augmented by quantum computing — moves beyond pattern-matching to explain why a treatment works, not just predict that it might
- Where trusted AI can create value across regulated workflows such as safety, regulatory and R&D operations
- Why human oversight, auditability and governance are essential for enterprise AI adoption
- How to think about ROI, adoption and operational impact when scaling AI across the life sciences business
