If your operation still opens folder after folder of “work order photos,” you do not have a control tower. You have a dead archive with Wi-Fi.
Exception-based management starts when the capture already arrives classified.
The problem is not a lack of cameras
Every technician has a smartphone. The bottleneck is human: someone has to look, compare against the standard, and decide whether to release the job. At scale, that becomes a queue, delay, and inconsistency across supervisors.
What AI vision changes
In Form Builder, AI Vision fields answer a configurable rubric. They can return a score from 0 to 10, typed answers, and rationale. Offline, media stays pending; analysis completes when there is network.
Below the threshold, the capture stays inconclusive. Above it, the tower can act by exception.
The tower does not need to see the ten thousand good photos. It needs the ones that fell below the threshold, failed EPI, or diverged from before/after.
Three rules to roll out without theater
- Set the threshold with the quality team, not with marketing.
- One question per field whenever possible. Long rubrics create noise.
- Inconclusive is a feature. Below confidence, escalate to a human. Do not force “green.”
How to tie it into the tower
Useful dashboards show:
- approved vs. inconclusive capture rate
- most frequent reasons for score drops
- average time to human release
- SLA by branch / provider
Without those cuts, “AI in the field” becomes a kickoff slide.
Build the exception-based tower
We show the full flow: capture, score, and deviation queue in the panel.