KOL conversations, congress meetings, advisory exchanges, and investigator discussions are some of the richest sources of scientific intelligence in the organization. When those insights are not recognized or connected to research strategy, their value disappears into dashboards, debriefs, and hallway conversations. Reframe external scientific engagement as the front end of evidence generation, where cross-functional colleagues must work from the same playbook.

• Reframe KOL engagement and field medical insights as drivers of evidence strategy
• Translate external insights into a structured evidence gap assessment that supports broader evidence strategy
• Identify where external scientific intelligence gets lost between teams
• Align MSL, Medical Affairs, External Research, and IIT teams around shared value
• Build a more connected model from scientific exchange to research decision-making

MSL teams are gathering more insights than ever, but volume does not equal value. The real challenge is knowing which KOL signals point to meaningful evidence gaps, which insights should influence medical strategy, and which recurring questions may deserve research support. Explore how leading teams separate signal from noise and move field intelligence into decisions that actually change strategy.

• Distinguish actionable field insight from anecdotal feedback
• Create escalation pathways for recurring evidence gaps
• Connect KOL feedback to Medical Affairs, portfolio, and evidence-generation priorities
• Define what makes an insight useful enough to influence research strategy

MSLs are often closest to investigators and KOLs, but that does not mean they should own every step of the IIT process. Without clear boundaries, teams risk inconsistent communication, duplicated work, operational confusion, and compliance exposure. Do your MSLs clearly recognize where ownership must shift to ClinOps, Grants, or other colleagues?

• Define appropriate MSL involvement across the IIT/IIS lifecycle
• Clarify when MSLs should engage, escalate, or step back
• Build compliant communication pathways between investigators and internal teams
• Reduce operational confusion through clearer role ownership

A strong scientific question can still fail if it is forced into the wrong research model. IITs, collaborative research, real-world evidence, Phase IV studies, registries, and sponsored trials each carry different levels of control, cost, risk, speed, ownership, and operational complexity. Choose the right pathway before expectations, timelines, contracts, and compliance questions become tangled.

• Compare IITs, collaborative research, RWE, Phase IV, and other external research models
• Match research questions to the most appropriate evidence pathway
• Evaluate control, risk, cost, ownership, and operational complexity earlier
• Avoid misclassifying research in ways that create downstream friction

Novel medical device technologies can address meaningful unmet needs, but market clearance alone does not guarantee clinical adoption. For many Class 2 medical devices, especially those authorized through the De Novo 510(k) pathway, the evidence story is often built in parallel with launch. Without a clear data strategy, consistent terminology, defined outcomes, and strong KOL/SME engagement, promising innovation can stall before it becomes a new standard of care.

Using the example of a novel Class 2 medical device granted De Novo marketing authorization, examine how Medical Affairs and field teams can help translate external scientific insight into a practical clinical evidence strategy. Explore how prospective studies, real-world data, investigator-led research, publication planning, and KOL engagement can work together to support adoption, demonstrate patient and economic value, and protect the treatment space created by innovation.

• Identify where KOL and SME insight can strengthen clinical strategy for novel medical devices entering the market with limited premarket clinical data
• Define common terminology, core outcomes, and data expectations that support consistent evidence generation across studies
• Balance prospective clinical studies, real-world data, and investigator-led research to support adoption, guideline consideration, and long-term product strategy
• Use publication, podium, and peer-engagement strategies to translate evidence into clinical confidence
• Build a short- and long-term evidence roadmap that connects unmet need, patient outcomes, economic value, and market adoption

Scientific expertise is essential, but Medical Affairs teams also need to understand the business context shaping evidence priorities, stakeholder questions, and leadership decisions. Whether supporting a Part B or Part D product, working in a small biotech preparing for capital raise or IPO, or aligning to a focused therapeutic-area portfolio, medical teams need stronger fluency in how companies fund, position, and grow products. Break down the business side of Medical Affairs in practical terms so teams can communicate more effectively with leadership, understand strategic priorities, and connect scientific insights to the broader product and portfolio story.

• Understand how reimbursement pathway, product lifecycle, portfolio strategy, and company stage influence Medical Affairs priorities
• Build fluency in business concepts such as capital raise, IPO readiness, asset strategy, therapeutic-area focus, and portfolio growth
• Communicate the business relevance of field insights without crossing promotional or commercial boundaries
• Translate scientific and KOL feedback into language that resonates with leadership, business operations, and cross-functional stakeholders
• Strengthen MSL and Medical Affairs credibility by understanding what the company values and how decisions are made

AI is moving quickly into Field Medical workflows, but many MSL teams are still working through where it helps, where it creates risk, and how to use it without losing the human judgment scientific exchange depends on. Examine practical, real-world use cases for KOL preparation, pre-call planning, insight synthesis, roleplay simulations, scientific exchange coaching, and pattern recognition across field intelligence.

• Identify practical AI use cases for MSL pre-call planning, KOL preparation, insight synthesis, and roleplay-based coaching
• Explore how AI can help MSLs enter KOL conversations with stronger context, sharper questions, and better understanding of stakeholder priorities
• Address field team hesitation around AI replacing, flattening, or over-standardizing the MSL role
• Improve the consistency, quality, and actionability of field insights without losing scientific nuance or relationship context
• Build governance guardrails for AI-supported coaching, documentation, insight analysis, and cross-functional use of field intelligence

An MSL hears a recurring scientific concern from multiple KOLs. An investigator later proposes a related study. Internal teams now need to decide whether the idea should move forward, who owns the relationship, what evidence pathway makes sense, and what compliance guardrails must be in place. In this working session, attendees map the handoffs, decision points, documentation needs, and ownership questions that determine whether the opportunity becomes useful evidence or vanishes forever.

• Map the full journey from field insight to study review
• Identify handoff points that create delay, duplication, or risk
• Clarify cross-functional ownership at each decision stage
• Leave with a practical model for improving insight-to-action workflows