Rüdiger Seibold
Takeda
Head of Data, Digital & Technology
Empowering Field Excellence with AI: The Rise of the Field Companion
In today's fast-paced healthcare environment, Key Account Managers (KAMs) are expected to deliver confident, compliant, and impactful engagements—often with limited time and overwhelming information. This session introduces #FieldCompanion, an AI-powered knowledge tool codeveloped by Takeda Germany and FFI Ventures, designed to provide KAMs with real-time, contextual support in the field.
Key Learnings:
- Why traditional tools and training fall short in dynamic, real-world KAM settings
- How GenAI can transform field engagement from reactive to proactive
- What the Field Companion does, how it works, and where it's heading next
- Real examples of how it supports personalized, compliant, and consistent HCP conversations
Walk away with a vision—and a practical blueprint—for how AI can empower your field teams to perform at their best, every time. Imagine a KAM stepping into a meeting with a healthcare professional, armed with real-time, AI-driven insights tailored specifically for that conversation.
Sebastian Sbirna
Novo Nordisk
Data Governance Lead
Operationalizing Data Governance for Scalable Impact & an AI-ready Enterprise
This presentation highlights how a coordinated Data Governance approach transforms Novo Nordisk's ability to use data effectively across Commercial and enterprise functions. It illustrates a unified governance model, which creates clarity around data responsibilities, strengthens consistency and reliability, and establishes the foundations required for AI and analytics to scale.
- Enterprise data challenges limiting AI, analytics scalability, and trust
- Strategic vision for governed, insights-ready data across Business Areas
- Practical governance framework, enabling consistent standards, roles and execution
- Key enablers and lessons powering AI adoption for Commercial Excellence
Ewa Gajewska
Merck Group
Head of Product Management, SYNTHIATM
Leveraging the computer-powered retrosynthesis for safe and sustainable pharmaceuticals
Learning Points:
- How computer-assisted retrosynthesis contributes to safe and green pharmaceuticals syntheses
- How modern digital tools like SYNTHIA™ Retrosynthesis Software can help chemists in their daily work
- Learn techniques to enhance efficiency and reduce bottlenecks in drug discovery using SYNTHIA™ Retrosynthesis Software
- Explore customizable synthesis planning to meet specific project requirements
Caroline Ringauf
Fresenius Medical Care
Global Product Lead, Home Digital Ecosystem
Turning the Home into a Real-World Data Engine: How AI Transforms Therapy Safety and Patient Engagement
- Home therapies as high-value real-world data sources: How connected devices and patient-generated data turn everyday treatment into continuous, high-quality clinical insight for pharma and care teams.
- AI-driven early detection and safety monitoring: Demonstrating how predictive models identify risks earlier than traditional clinical follow-ups — improving therapy safety and reducing avoidable interventions.
- Designing for patient engagement, not just data capture: Why human-centered design is critical for adherence, meaningful data patterns, and scalable AI performance.
- Closing the loop between patients, providers, and pharma: How real-time data sharing strengthens clinical decision-making, supports personalised treatment pathways, and generates actionable evidence for research.
- Building globally scalable digital ecosystems: Lessons from developing home-based digital solutions across regions, focusing on interoperability, ethics, and trust to enable AI in real-world care.
Richard Bobek
MSD
Associate Director, Clinical Data Science
Anticipating Regulatory Questions: Using AI to Enrich Clinical Protocol Drafts
- Why protocols attract questions – Typical patterns of regulatory and data queries driven by protocol ambiguities, missing justifications, and inconsistent wording.
- Learning from historical RTQs and queries – Using NLP and ML to mine past RTQs, authority Q&As and data queries to identify common issues and high risk sections in protocols (e.g. endpoints, stats, eligibility, safety monitoring).
- AI copilots for protocol authors – Integrating AI into the protocol drafting workflow to surface relevant historical questions, propose clarifications, and suggest justifications while authors are writing.
- Proactive enrichment of protocol drafts – Embedding anticipatory explanations, decision rationales, and standardized language into drafts to reduce downstream RTQs and data queries.
- Human oversight and governance – Ensuring medical, statistical and regulatory experts remain in control through review workflows, traceability of AI suggested changes, and alignment with internal standards and GCP expectations.
- Impact on trial delivery – Early evidence on reduced rework, fewer repetitive questions, and improved inspection readiness when protocols are “trained” on past questions.
Valerie Eichinger
Roche
Clinical Development Lead
Nothing's gonna stop us now...really?
- What keeps us from truly leveraging AI for patient solutions?
- Validating AI solutions when traditional clinical trials aren't enough.
- How to stay human in a digital-first world?
