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...
With the .com boom of late 90’s and early 2000’s and wide adoption of email across the World, capturing the attention of users through an email was fairly easy compared to the current saturated digital space. Increasing volume of promotional content within...
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...
The insurance organizations have a very special niche in the world of data. In sharp contrast to most other fields where data related to financial information or information about personal identity is processed individually, insurers are used to processing both...
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...
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...
Artificial intelligence has evolved beyond its origins as a specialized technical domain and is now experiencing widespread adoption across all business sectors—and Product Managers who speak its language are becoming translators driving this transformation. They play...