The capabilities of AI have been over-promised, which can lead to disappointment – which in turn might get in the way of your team finding lasting advantages. Through examinations of multiple case studies, you can reorient your work in medical information and with Digital Opinion Leaders in order to take your medical affairs teams to the next level.

• Review applications for literature searches and medical information management
• Clarify which of your KOL prospects can also be DOLs – those most well-versed and inclined to use AI
• Grasp the uses of machine learning for patient-finding and outreach in clinical studies

Someone who expects to interact with a chatbot in normal conversational language may shy away from understanding how the tool works, or think it is unnecessary. But the true interface for these tools includes the brain asking the questions – so all of your teams need a nuanced understanding of how they work.

• Clarify different types of AI models
• Move away from prompts and towards context engineering
• Ensure teams understand the capabilities of off-the-shelf vs bespoke models
• Encourage inviting IT support to evaluate whether the results generated are accurate

When teammates are inexperienced or uncertain around AI, it is vital to have someone in a champion role who helps others see its value and proper use and understand internal policies. This role requires managing questions on-the-fly, answering questions in both small and large group environments, and listening to pain points during calls – while always taking care to be a realist, not a salesperson.

• Help medical affairs departments stand out to leadership
• Emphasize safeguards and restrictions
• Keep up with the most helpful educational resources

Knowing where to draw the line between AI and human judgment is a skill that takes practice. This panel explores how Medical Affairs teams are learning to collaborate effectively with AI, build confidence through real-world use, and recognize where human oversight is irreplaceable.

• Map which Medical Affairs tasks belong to AI, humans, or both
• Match your verification effort to the risk level of the task
• Build confidence and trust through hands-on, everyday use
• Learn from peers who have already navigated AI skepticism

Congresses generate an overwhelming amount of information and preparing for them takes even more time. In this hands-on session, discover how AI tools like Copilot, ChatGPT, or Claude can transform your congress workflow from start to finish. We'll walk through practical examples of using AI to identify relevant sessions and posters, assess key opinion leaders to connect with, and turn post-congress notes into polished summaries and presentations — all while staying aligned with your company's AI guidelines.

• Identify at least two ways AI can be applied to congress planning and post-congress analysis in a medical affairs context
• Practice building and refining prompts to surface relevant sessions, posters, and insights from conference content
• Leave with ready-to-use prompt templates and tools applicable immediately within your current AI/LLM platform

HCP engagement is shifting from “which channel reaches the physician?” to “which trusted answer gets surfaced when they ask?” AMA's 2026 survey found 81% of physicians use AI professionally, more than double the 38% in 2023. Medical Affairs now has to optimize for content retrievability, credibility, and compliance inside AI-mediated workflows.

• Adapt your journey from search-led to answer-led, with AI answers sitting between Medical Affairs content and the HCP
• Design content to be modular, structured, evidence-linked, and machine-readable so the right science is findable and citable
• Evolve the MSL's role from information deliverer to scientific sense-maker, flagging misinformation, ambiguity, and evidence gaps

Sit at an AI workstation and start to face your biggest questions about your day-to-day projects, with expert facilitation and non-judgmental help.

EXERCISE 1: Sherlock AI

Act out a rapid response scenario in real-time! A field signal is spiking: a jump in negative sentiment, a switching pattern, a recurring HCP objection. Race to find the root cause using AI over a shared dataset of call notes, inquiries, and congress notes - and uncover the true driver of the problem before other teams or companies do! This is where you will see how well you can trust your AI platform and how thoroughly you need to check it, as you verify against the source before taking action.

EXERCISE 2: AI Visibility Audit

HCPs increasingly interact with AI before your MSLs ever contact them. When your team runs your therapeutic through an AI visibility audit, you'll get an unvarnished look at what today's models actually say about it. What will surface? What is missing or outdated? How are competitor narratives filling the space where your evidence should be - and how can you change that? Train Medical Affairs teams to monitor the AI environment for accuracy, currency, and knowledge gaps - because this is the material your physicians will see, and believe, before they ever meet you.

• Hone your skills at root-causing field signals across notes that - until recently - would have been unstructured
• Gain confidence in interrogating field data with AI
• Segment by time and geography
• Separate real drivers from coincidence
• Learn to audit AI output for evidence linkage and competitive framing
• Distinguish real, repeatable gaps from hallucinations
• Translate findings into medical strategy across communications, omnichannel, and field medical