AI didn’t fail our field technicians. The incentive system did.

A few years ago, we rolled out new automation and AI support into an established field service operation.

On paper, it was solid. Faster diagnostics. Better routing. Clearer recommendations. The tools worked exactly as designed.

The outcomes didn’t.

Escalations dropped sharply. At first, that looked like success. Over time, it became obvious something else was happening. Technicians were working around the system, delaying signals, and absorbing risk locally.

In field services, behavior always follows incentives.

Technicians had learned that early escalation slowed them down, triggered scrutiny, or made them look incompetent. So the AI received cleaner data but less truthful data. The system didn’t surface problems because people had learned not to surface them.

At scale, AI doesn’t create truth. It reflects the behavioral environment it’s trained inside.

Once we realigned incentives and made escalation a protected behavior, the same tools started producing radically different outcomes. Signal quality improved. Issues surfaced earlier. Trust recovered.

This pattern shows up repeatedly in field operations. Technology rarely fails on capability. It fails when behavioral design is treated as secondary.

Michael Blystone has written extensively about this dynamic in Field Services Rewired, where field services are treated as human-centered systems, not just logistics problems.

How are others here ensuring that AI and automation in field services reinforce the right behaviors, not just faster reporting?