Life Sciences & Healthcare AI PM Fundamentals
Everything I wish I knew when I started in Life Sciences & Healthcare AI product management - from the regulatory basics to building your first AI product spec.
The ROI of AI: How to Build a Business Case Your CFO Will Approve
Most AI business cases fail the CFO review because they overestimate benefits, underestimate costs, and ignore risk. Here is the framework I use to build business cases that actually get approved.
How Tempus Built a $6B Clinical AI Platform
How We Reduced Clinical Trial Matching Time by 73% with RAG
A real-world implementation of retrieval-augmented generation for matching patients to clinical trials at scale.
How to Write a PRD for an AI Feature (Template Included)
The first AI PRD I ever wrote was a disaster. I used the same template I had for growth features at Mamaearth - user story,
State of AI in Healthcare 2026: From Clinical Trials to Production
1,591 active AI clinical trials. 50 involving LLMs. 17 in Phase 3/4. A practitioner's deep dive into where healthcare AI actually stands in 2026 — the data, the regulatory landscape, what is not working, and predictions for 2027.
Mental Models for AI Product Decisions: When to Ship vs When to Wait
The most consequential decision I make as an AI PM is not which model to use or how to structure the evaluation pipeline. It is
Building a Real-Time Adverse Event Detection System for Post-Market Surveillance
From 6-week manual review cycles to real-time signal detection — how we rebuilt pharmacovigilance with NLP.
Ethics and Responsible AI: A PM's Practical Guide
Responsible AI for PMs is not an ethics lecture. It's a set of practical decisions that affect whether your product is trusted, adopted, and sustainable in regulated environments. Here's how to embed responsible AI practices into your actual product process.
Viz.ai: AI Triage from FDA Clearance to 1,400+ Hospitals
Defining MVP for AI Products: Less Is Different
The classic MVP framework - build the smallest thing that tests the riskiest assumption - breaks for AI products because the model quality is part of the product. A 60% accurate AI is not a minimal viable version of a 90% accurate AI. It's a different product that might create different...
FDA AI/ML Regulatory Checklist for Life Sciences & Healthcare
Go-to-Market Strategy for AI Products
AI products have a trust problem that traditional software doesn't. Your GTM strategy has to solve for skepticism, unclear ROI timelines, and buyers who have already been burned by AI hype. Here's how to design a GTM motion that actually converts.
LangChain vs. LlamaIndex for Healthcare RAG: A Technical Comparison
If you're building RAG (Retrieval-Augmented Generation) applications for healthcare - clinical decision support, medical literature search, protocol Q&A, pharmacovigilance - you&
Building Cross-Functional AI Product Teams
AI products fail more often because of team dynamics than technical capability. Data scientists who can't communicate uncertainty to stakeholders, engineers who won't push back on infeasible ML requirements, PMs who don't understand what a model can't do. Here's how to build teams that ...