Four out of five companies now run at least one AI agent in production. By 2027, Gartner expects 40% of those projects to be scrapped.
Read the post-mortems, and you notice something: the model is rarely the cause.
The reasons are boring and structural. Bad data. No way to measure whether the thing works. No visibility into why it does what it does. Software the company rented but never owned. A shiny demo bolted onto a broken process.
None of that is a machine learning problem. It's an engineering discipline problem. And the teams whose AI survives contact with production all do the same three boring things.
I run an AI product engineering shop. We build production systems for mid-market companies, and we've watched this pattern enough times to bet the company on it. Here's the discipline.
