Why Enterprise AI Fails: It's an Operations Problem

What We Set Out to Understand In 2026, the dominant narrative around AI failure still points at the same suspects: outdated infrastructure, a shortage of ML engineers, insufficient GPU budget. We built several outbound automation pipelines over the past year and kept running into a different wall entirely. The models worked. The APIs responded. The pipelines broke anyway, because the organizations running them weren't operationally ready to absorb what the automation produced. That friction s...