The phenomenon of modernization in financial services in the recent decades has been largely attributed to technology transfers. Shift workloads to non legacy platforms. Avoid any proprietary middlewares and stick to APIs. Break up monolithic apps into services. Adopt...
Most personalization systems start out simple, and they should. A scheduled job computes recommendations overnight, writes them to a table, and an API serves them on request. It is cheap, predictable, and for a lot of products it is entirely good enough. The problem...
Right now, somewhere in a planning meeting, someone’s asking “can we make this real-time” and everyone’s nodding. Sounds much better, obviously. New data. Fast decisions. Dashboards on the move. Who would advocate for slower? I’ve made this jump a few times in...
It’s easy to get caught up in the excitement of doing WAY more in less time. Work that once required days of data cleaning, analysis, modeling, presentation building, and insight development can now be delivered in hours with AI. Managers want faster results, teams...
In most of the commerce and fulfillment systems, the problems get noticed too late in the game. A delivery issue usually appears only after the shipment is already late and a payment gateway issue may become visible only after the customer has already experienced the...
Your best analyst gets things wrong. Misremembered figures. Stale assumptions. A confident error in a board deck. You trust them anyway, and the reason explains more about enterprise AI than any benchmark does. You trust them because their errors get...
Everyone’s racing to get AI to “automate insights” right now. But honestly, after sitting through too many quarterly business reviews where everything looked flawless on paper, I’m starting to wonder if we’re missing the point. When Data Lies One meeting really sticks...
AI agents are on the rise, and it’s quickly shifting from a buzz word to reality. Development of agentic systems is fast moving from research labs and demos to production systems that can plan, act and learn from user workflows. The large language models (LLMs) that...
I spent months overengineering an AI chatbot. Then I threw most of it away and got better results in two weeks. This is an anecdote of what actually worked when I built production RAG systems at scale, and why the lessons surprised me. The Complexity Trap I was tasked...
Most data and AI projects don’t fail because teams chose the wrong infrastructure or modeling approach. They fail because the project breaks down somewhere across the lifecycle, long before folks realize. By the time something reaches production (if it does), teams...