We spend a lot of effort trying to understand what is happening across an operation. Information sits in different systems, people fill the gaps, and getting a complete picture takes work. Connecting that information is one of the biggest opportunities I see for AI.
But as more work passes through AI, there is another visibility problem to consider: will the team still understand how the process operates?
It might become easier to get a result while becoming harder to work out what produced it. That matters when something goes wrong. The people responsible for the operation need to see what information went in, what happened to it and which actions followed.
Suppose AI helps move order information between systems and flags a shipment as short. The team needs access to enough of the process to investigate. Which records were compared? Were the quantities expressed in the same units? Was something missing when the comparison happened? An unexplained result leaves an experienced operator with more work to do before deciding how to respond.
AI check at 09:46: 20 cases short.
Order: 100 cases. Receiving record: 80 cases, still open.
At 09:51, the final 20 cases were recorded and the receipt closed.
The load wasn’t short. Receiving was still in progress when the check ran. With access to the records, the team can spot the cause and change the check to wait until receiving is complete.
An AI check flags a shortage. Open the alert to see what it used.
The check compared these records. Follow the 80 cases back to receiving.
This was the receiving record when the AI check ran.
The load wasn’t short. Receiving was still in progress when the check ran.
With access to the records, the team can spot the cause and change the check to wait until receiving is complete.
That operator may also know why a particular mismatch occurs and how the operation normally handles it. Their involvement has value beyond correcting the immediate result. It can help improve the procedure, identify information that needs to be included, or establish where review is necessary.
As AI takes on more routine work, people need to stay involved in reviewing how that work is done. Otherwise, the team can gradually lose its understanding of why a process works a particular way. Those reviews should improve the working process and keep its operating logic familiar to the team.
The people running the operation need enough visibility to question a result, investigate a problem and intervene where necessary. Some uncertainty may remain; teams still need to know the limits of what they can verify.
Build that visibility into the process from the beginning, while the team still has the knowledge needed to shape how the work is done.
