Masterclass 1: From Pilot to Practice, A 3-Phase Roadmap for Responsible AI Implementation
Most organizations know they need AI. Few know how to make it stick. This Masterclass offers a concrete, field-tested three-phase framework for implementing AI in complex, regulated environments, with pharma as the primary lens.
Phase I covers how to identify quick wins that build internal trust without creating regulatory risk: data audits, compliance checkpoints, and the first use cases that deliver measurable proof of value within weeks. Phase II addresses the harder challenge of scaling beyond the pilot: knowledge transfer, new role definitions (AI Officer, CAIO), governance rituals, and the cultural shift that turns skeptics into multipliers. Phase III tackles what most frameworks ignore: how to make AI audit-proof, operationally stable, and genuinely measurable in day-to-day routines, aligned with the EU AI Act.
Participants leave with a practical orientation for all three phases, including the KPIs that actually matter, the organizational structures that hold, and the common pitfalls, from shadow AI to change fatigue, that derail even well-funded initiatives.
All topics and examples are up for discussion.
Masterclass 2: AI in Healthcare, Why Optimization Is Not Enough
AI is already delivering results in healthcare: pattern recognition in oncology, radiology, dermatology. The wins are real. But the system around those wins remains structurally broken, and optimizing a broken system is not transformation.
This Masterclass examines what genuine disruption in healthcare could look like, and what it actually requires. The convergence of AI, sensor technology, robotics, and biotechnology opens possibilities that go far beyond cost reduction or headcount planning. Patient communication data alone, largely untapped today, could reshape product development, logistics, and care delivery. But none of that happens without intention, governance, and a clear framework for how AI is deployed and communicated.
The session explores where the real leverage points are, why most current healthcare AI initiatives are set up to underperform, and what a "Manual for Readiness" would need to contain: not just technical deployment, but the human competence and organizational clarity to match it.
All topics and examples are up for discussion.
