When I was an engineer at Stripe circa 2017, I pitched a machine learning system that would cut our support headcount in half. I thought I was solving the biggest cost to the company: people. After all, isn’t that the point of automation?

The head of support’s response caught me off guard: “Congratulations. You’ve automated the easy part.”

I realized the real problem was the workflow. Agents were toggling between 10 different tools. Institutional knowledge was stuck in silos. Work was being routed manually, without any visibility into patterns or bottlenecks. The biggest cost wasn’t people. It was the broken process.

Cutting labor costs in the name of AI has often proven to be a losing proposition. Take Klarna, for example: in early 2024, their OpenAI‑powered assistant took on the workl

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