Most automation projects fail quietly. They work in the demo, ship, and then slowly stop being trusted because the edge cases pile up faster than anyone can patch them.
Design for the weird inputs
Real documents are skewed, half-scanned, and inconsistent. Real workflows have exceptions the process owner forgot to mention. Treat these as the main case, not the afterthought.
Make it observable
Every automated decision should be inspectable: what came in, what the model or rules concluded, and why. When something looks off, the fix should take minutes, not a forensic investigation.
Keep a human in the loop where it matters
Confidence thresholds route uncertain work to people. Over time, as the system earns trust on a category, you raise the threshold. That is how automation compounds instead of eroding.