FIELD NOTES
Practical takes on product management, enterprise AI adoption, cloud strategy, and regulated workflows — grounded in hands-on product and technical experience.
When Payment Infrastructure Meets a Regulatory Deadline: What I Learned Running a Cloud-Native Connectivity Migration
Modernizing payment network connectivity at a Fortune 20 financial institution isn't primarily a technical problem. The architecture is the easy part. The hard part is everything that happens before and after the engineering work.
Read postarrow_forwardHow a Payment App Rebuild Contributed to 3x Revenue Growth
We rebuilt the core payment, transaction, and card-balance workflows on a top-ranked financial-services mobile app, contributing to 3x revenue growth and 54% higher satisfaction within twelve months.
The $2.5M Decision That Changed How I Think About Enterprise Technology
Early in my career I built something that saved $2.5M annually. What I remember most isn't the architecture. It's the conversation I had to have before a single line of code was written.
How a Feedback Loop Moved a Banking App's NPS by 10 Points in Six Months
The hardest part of improving a product that millions of people use isn't the engineering. It's deciding what to fix first. Most teams get this wrong in the same way.
What It Takes to Scale Identity Verification Across a 650,000-Employee Ecosystem
An identity product spanning a 650,000-employee ecosystem is a different problem than a feature operating in one channel. The release criteria, operating model, and cross-functional coordination all have to work at enterprise scale.
How We Cut $1.4M in Annual Cloud Costs Without Touching a Single Application
Most cloud cost problems look like engineering problems. They're not. They're governance problems wearing an engineering mask. Here's how we fixed one — and what we found underneath.
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.
From Advisor to Builder: Why I Started Shipping My Own AI Products
I spent 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
LLM agent systems fail in production in ways demos rarely reveal. The expensive problems are usually reliability, context, security, and observability — not model intelligence.
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.