FIELD NOTES
Practical takes on AI deployment, cloud architecture, and enterprise technology — from someone who builds and ships, not just advises.
The Career I Didn't Plan — And the Thread That Connects All of It
I didn't set out to become a person who sits at the intersection of AI strategy, enterprise cloud, and product leadership. I set out to be a good infrastructure engineer. What happened between those two things is what I want to write about.
Read postarrow_forwardFrom TAM to Builder: Why I Started Shipping My Own AI Products
I spent 20 years helping enterprises navigate technology decisions. At some point I realized that advising was no longer enough — I needed to build.
5 Mistakes I See in Every Enterprise GenAI Deployment
After advising and building GenAI systems across financial services and cloud, the same five mistakes appear in nearly every enterprise deployment. They're all preventable.
Your AI Roadmap Is Lying to You
Most enterprise AI roadmaps are a list of use cases with no mention of the data that would make them work. That's not a roadmap — it's a wish list.
Executive QBRs That Actually Move the Needle
Most QBRs are a PowerPoint deck of metrics that the customer already knows, followed by an ask. Here's how to run one that changes the relationship.
LLM Agents in Production: What Actually Breaks
I've been building and deploying LLM agent systems in enterprise environments for the past two years. The failure modes in production are almost never what the demos suggest they might be.
Multi-Account AWS: The Governance Strategy Nobody Talks About
Most AWS customers treat account structure as a billing concern. It's actually a security, governance, and operational posture decision that becomes very expensive to undo later.
What Amazon's Working Backwards Method Taught Me About Product
I spent years at Amazon and Amazon Web Services. The 'Working Backwards' process is genuinely one of the best product frameworks I've used — and also one of the most misunderstood.
RAG vs. Fine-Tuning: How I Actually Choose
Every enterprise AI team faces this question. Most people are choosing based on what they've heard, not what they've measured. Here's the decision framework I actually use.
The Hidden Cost of Lift and Shift: A FinOps Retrospective
Moving workloads to the cloud without re-architecting them is sometimes the right call. But the bill that arrives three months later is almost always a surprise — and it doesn't have to be.
Why Most Enterprise AI Pilots Die Before Production
I've watched dozens of AI pilots get greenlit, celebrated, and then quietly shelved. The failure mode is almost always the same — and it has nothing to do with the technology.