I’ve spent years watching how money travels through AI projects. It’s not the same as watching patterns in a spreadsheet. It’s the way teams feel the heat of a monthly bill and how that heat
The claim that model quality is the main predictor of AI project success matters because the human and organizational costs hide behind every slick headline about accuracy. If you can’t defe
I keep thinking about the moment after a bad decision ships, when everyone wants it to be “the system’s fault” and nobody wants to be the person who signed the paperwork that made it possibl
The demo always feels clean. Then the inputs get messy, the edge cases show up, and the first “small” model tweak lands as a large business fact. I feel the pressure right there: everyone wa
A few weeks ago, a product update landed and the words felt harmless. The vendor said it would “improve oversight,” and it wrapped that claim in new logging and a new workflow. I read past t
Today I caught myself doing the old legal-ops move: translating a complicated rule into the smallest set of choices that still carry consequences. The EU AI Act does that for high-risk syste
There is a moment that keeps repeating in my work. Not the dramatic one. The quiet one. The moment you realize your system might be technically defensible, but it still could decide wrong. M
The hard part is not getting the system to run. The hard part is telling the person why it acted, when you can feel their hope tighten into anger. If I want accountability, I have to give th
When the demo ends, the pressure starts. I feel it in the pause before someone clicks “go live.” It is not fear of AI as a concept. It is fear of owning what happens after the model answers,
I do not buy “trust” anymore. Not in a world where a demo can turn into a public failure overnight, and where the last people holding the bag are the ones whose names were attached to the wo
The demo always feels clean while it runs. Then it stops. The questions do not.