Why receiving is worth automating
Receiving creates the record that everything else depends on. If a serial number is mistyped at the dock, the error can follow the device into the erasure record, the certificate, the settlement, and the client’s report.
It is also repetitive work under time pressure. A large shipment means hundreds of labels in different positions and formats, keyed in by staff who also need to keep the dock moving. Reading those labels from a photo reduces the typing and gives your team a picture to check against.
People stay in the loop
Labels are worn, small, or missing, and some devices carry more than one serial number. AI will not read every one correctly, so a receiving process should not act as if it can.
Results that need a second look go to a person, who compares the reading with the image and confirms or corrects it. If a label cannot be read at all, your team keys it in as they do now.
Evidence you can come back to
A photo taken at intake is useful long after the truck has left. If a client later disputes what was received or what condition it arrived in, your team can look at the image rather than rely on memory.
Images sit on the asset record alongside custody events, test results, and erasure evidence, and they work alongside the multi-webcam capture used at the bench.
How to evaluate AI at receiving
Do not judge it on a clean demo image. Test it with your own devices and your worst labels: faded stickers, small print, screens, and devices with two serial numbers. Ask to see what happens when a reading is wrong.
Then check the trail. You should be able to tell who confirmed a result and when, and whether a value came from the image or was corrected by a person. Grading is a separate question: ask exactly what the module does and does not decide on its own.