AI Advisory Board Prep for Medical Affairs Teams
Medical affairs teams spend 40–60 hours preparing for a single advisory board meeting. Most of that time goes to KOL profiling, literature synthesis, and slide deck preparation — work that AI can compress by 80% without sacrificing quality.
The preparation burden
A typical advisory board requires:
- KOL profiling: 15–20 hours researching 8–12 advisors. Publication history, clinical trial involvement, speaking engagements, conflict disclosures, treatment preferences.
- Literature synthesis: 10–15 hours reviewing recent publications, conference abstracts, and competitor data to frame discussion topics.
- Material preparation: 10–15 hours creating briefing documents, discussion guides, and slide decks.
- Compliance review: 5–10 hours ensuring all materials meet fair market value, anti-kickback, and transparency requirements.
Where AI fits (and where it doesn't)
AI excels at:
- Aggregating KOL publication data across PubMed, ClinicalTrials.gov, congress abstracts, and institutional profiles
- Identifying emerging treatment patterns from recent literature
- Cross-referencing advisor expertise against discussion topics
- Generating first-draft briefing documents with citations
- Flagging potential compliance issues in draft materials
AI doesn't replace:
- Relationship judgment — knowing that Dr. X and Dr. Y have a contentious history
- Strategic framing — deciding which topics to prioritize based on commercial goals
- Facilitation planning — reading the room dynamics and planning interventions
The compressed workflow
Teams using AI-augmented preparation report compressing the 40–60 hour preparation cycle to 8–12 hours:
- Morning: AI generates KOL profiles, literature summaries, and draft materials (2 hours of review vs. 30 hours of creation).
- Afternoon: Team reviews AI outputs, applies strategic judgment, refines discussion guides (4–6 hours of high-value work).
- Next morning: Compliance review of final materials (2–4 hours, streamlined by AI pre-screening).
The quality argument
The concern with AI-generated materials is quality. In practice, the opposite happens: AI-augmented prep produces more comprehensive KOL profiles (it doesn't forget to check congress abstracts), more current literature reviews (it doesn't rely on what the team remembers), and more consistent compliance (it doesn't skip the disclosure check when rushed).