Data Strategy

Workflow demos are usually polite. They show the agent reading a request, calling a tool, checking a result, and producing an answer before anything embarrassing has time to happen. The real test begins later. Not at step three. At step twenty-seven, when a previous decision constrains the next one, a small drift compounds, and the system must still remember what “done correctly” means. This is where many AI products discover that knowing the rule is not the same as applying it repeatedly without wobbling. A charming discovery, preferably not made inside a production accounting workflow. ...

The expensive habit of feeding the machine Data teams have a familiar ritual. The model disappoints. Someone asks for more data. Another person asks for cleaner data. A third person, usually with a spreadsheet and a suspiciously calm face, asks whether the extra labeling budget is approved. Then the pipeline expands. More driving clips. More corner cases. More annotated scenes. More storage. More training runs. More dashboards explaining why the latest model is still not quite where it should be. ...