Idea in Brief
Most Medical Affairs teams collect more insights than they can use. AI can change this only when it is designed as an agentic system, not a smarter search engine. Agentic systems create continuous learning loops that sense, decide, execute, and learn in real time. The outcome is faster intelligence with compliance intact.
A word from our sponsor
Find out why 100K+ engineers read The Code twice a week.
That engineer who always knows what's next? This is their secret.
Here's how you can get ahead too:
Sign up for The Code - tech newsletter read by 100K+ engineers
Get latest tech news, top research papers & resources
Become 10X more valuable
The Problem: Insights That Expire Before They Are Used
Field medical teams surface high-value observations every day. Safety questions. Dosing patterns. Gaps in evidence. In many organizations these signals move through manual paths and arrive after the HCP conversation has shifted. The lag between sensing and acting is now a strategic liability.
The Opportunity: From Tools to Systems
Analysts estimate AI can reduce review cycles by a meaningful margin. Gains stay limited when AI is used as a tool that returns answers. Momentum appears when leaders build systems that coordinate sensing, reasoning, action, and learning.
Simple rule: tools create output. Systems create operating rhythm.
The Framework: The Decision Intelligence Loop
Agentic systems work because they run in loops, not lines. The loop compounds learning across four connected capabilities.
Sensing
Detect relevant signals across evidence generation, HCP sentiment, and field intelligence.
Example: NLP identifies spikes in questions about combination therapy during oncology congress sessions.
Deciding
Frame hypotheses with explicit assumptions and decision triggers. Prioritize what matters.
Example: Agentic AI ranks signals by strength and strategic impact, then routes a one-page brief with linked sources to Medical Review.
Executing
Operationalize decisions through compliant workflows with clear ownership.
Example: Upon approval, updated FAQs and booth content publish to field channels. A real-world evidence query is scheduled.
Learning
Capture structured feedback on what worked, what failed, and why.
Example: The system measures which updates resolved questions fastest and reuses effective patterns in the next cycle.
Outcome: each iteration makes the next one smarter. Over time you build institutional intelligence.
Case Study: From Field Insight to Strategy in 48 Hours
Sensing
The system monitors MSL notes, congress transcripts, and HCP sentiment. It flags a rise in “dosing variability” tied to your product.Deciding
The signal is scored for frequency and relevance. The brief with source links moves to Medical Review through a single interface.Executing
After approval, the workflow updates booth materials and field FAQs within 48 hours. Content governance is recorded automatically.Learning
All actions and outcomes are logged. Future dosing signals are cross-referenced to this case, shortening the next response cycle.
Result: a step-change reduction in time from observation to strategic action, with traceability for inspection readiness.
Governance: Human in the Loop, Always
Agentic systems amplify expertise. They do not replace it.
Every recommendation carries human validation.
Approvals and rationale are captured in an auditable trail.
Accountability stays with Medical Affairs leadership.
Short version: AI recommends. Experts decide.
Metrics That Matter
Track the few measures that reinforce learning velocity.
Insight-to-Decision Time
Time from observation to approved action. Shorter cycles prevent signal decay.Governance Velocity
Average time from AI flag to human approval. Measures speed with integrity.Learning Reuse Rate
Percent of prior insights reused in new strategies. Indicates compounding intelligence.Signal Quality Index
Ratio of signals that drive meaningful actions. Quantifies sensing precision.
Implementation: Start With One Decision in 30 Days
Week 1
Pick one decision that matters. Choose a recurring decision with real consequences.Week 2
Map the signals you actually need and the approvals required. Remove redundant steps.Week 3
Build the workflow. Connect the signals. Add the approval gates and audit logging.Week 4
Run it. See what works. Capture what you learned. Adjust the loop. Repeat.
The Leadership Imperative
Agentic systems are not an IT project. They are a leadership discipline.
Pick one decision. Build one loop. Prove it works. Scale across therapeutic areas and regions.
The leaders who move first will shape scientific dialogue in real time. Everyone else will optimize yesterday’s insights.
