Early Essays on Building AI Products in Healthcare (2018–2020)
Between 2018 and 2020, I was leading data science teams in healthcare and spent a lot of time thinking about a problem that many organizations faced at the time: how do you turn machine learning work into real products, not just models or blog posts?
These essays reflect that period. They focus on integrating data science into product development, building feedback loops, and avoiding the trap of treating AI as a standalone technical function. The domain was healthcare, but the underlying question — how intelligence becomes operational — continues to shape my work today.
• What I’ve Learned Working with 12 Machine Learning Startups (2019)
Lessons from advising early ML companies on why technical talent alone doesn’t create value — and how product alignment and integration determine outcomes.
• How to Build an AI Moat (2019)
An argument that defensibility in AI comes from product-embedded data loops, not from isolated model performance.
• 4 Product-Driven Steps to an AI Roadmap (2020)
A practical framework for ensuring AI efforts start with product value and workflow integration, rather than technical novelty.