The Part Is Already on the Shelf
At my AEM Annual workshop on Agentic AI, we’ll follow an illustrative equipment-service story from the first machine signal to a reserved part and a planned service appointment, with people making the decisions.
The part is already on the shelf.
That’s where the story starts. But how did it get there before the customer even knew there was a problem?
This is one of the stories I’ll be walking through at AEM Annual during my workshop on Agentic AI.
Picture a farmer heading into harvest. His tractor is still running, but the data it already sends is showing signs of wear. Something is changing. There’s time to do something about it.
In this illustrative scenario, AI co-workers connect the steps that usually require somebody to notice a problem, make a call, look something up, and chase somebody else.
One checks the machine’s history and flags a likely issue, with the evidence attached. Another validates the correct part against the tractor’s serial range and checks the dealer’s inventory.
Then comes my favorite question: Who else?
Are other machines in the territory showing the same pattern? Should the dealer be getting ready for those customers, too?
The farmer gets a message from his dealer. Here’s what we’re seeing. Here’s the part we’ve reserved. Here are three times you can come in before harvest.
He picks a time.
Meanwhile, the service manager reviews the recommendation and approves the additional parts order. A technician still confirms the diagnosis before anything gets replaced.
The co-worker recommended. The person decided.
That’s the kind of conversation I want us having about Agentic AI. What information do we already have? What work could it set in motion? Where does a person need to make the call?
For the farmer, the value is a planned stop that fits his schedule. For the dealer, it’s parts and service demand they can prepare for. For the manufacturer, it’s a dealer network helping customers keep working.
Scheduled downtime, not a fire drill.
At AEM Annual, we’ll use this story to make Agentic AI concrete, from the first equipment signal to the part on the shelf.
If you’re joining me, bring a workflow where your team spends too much time chasing information. That’s a good place to start.
