Typical AI curve: pilot week accuracy looks good; after launch business uses it occasionally; quarterly review finds advice untrusted. Team says model aged. Root cause is usually not algorithm—it is field results not returning. QC decisions, adoption flags, close-after-chase—labels not written back mean the model still lives in a three-month-old world.
AI without feedback fails around three months. It rarely alarms first—it still suggests; nobody clicks.
Why Decay Is Silent
Product mix shifts, complaint types shift, suppliers change—training distribution frozen at launch. Business bypass shows distrust; bypass does not log; data team thinks service continues. Complaints arrive after the correction window. Re-labeling is expensive; project gets replaced by a new slogan.
Without an owner on adoption and error, decay is tolerated—not an incident.
Minimum Loop Components
- Each suggestion logs adopt, edit, ignore—ignore needs a reason.
- Final business outcomes—pass, close, collect, complain—write back by key; weekly drift check.
- Drift over threshold: down-weight or offline—not show stale accuracy.
- Ops role owns the board; model lead and business owner co-sign before relaunch.
The XYN digital intelligence system hangs AI on work orders and QC nodes—write-back lives on the same document. Keep effect by keeping the loop. No loop—smart agents are a one-season demo.
