Connected equipment has a coordination problem, not a data problem
Insight
The question after connectivity
The theme of this year's MHI Annual Conference in Palm Springs was Connecting the Supply Chain. For equipment OEMs, that theme lands differently than it did ten years ago.
Back then, the challenge was getting machines online. Today, most fleets already report back. The question has shifted from how to connect equipment to what to do once the data is flowing.
Connectivity is mostly solved
There are now more than 50 million connected industrial machines in the field, across material handling, agriculture, mobility, robotics, and more. Telematics providers have made it straightforward to stream faults, sensor readings, and usage data off a machine.
The result is that the average OEM now holds around 15 years of historical field data. Most of it has never been put to work. A decade of investment in connectivity produced plenty of data, but not a clear way to act on it.
Where service breaks down
The data exists. The coordination doesn't. Machine data sits in telematics, service history in service management, warranty in the ERP, and procedures in manuals. A typical service team works across five or more of these systems, and they rarely share context.
That gap shapes how field service runs today:
The customer finds the failure first. A case opens when someone picks up the phone, not when the telemetry flags a problem.
Tickets start from scratch. There is no automatic path from a fault code on the machine to a work order in the system of record.
Techs investigate more than they fix. The connectivity data is there, but it rarely reaches the technician before the visit.
Backlogs stretch response times. In peak season, critical equipment can wait 7 to 10 days for service.
The numbers reflect it. First-time fix rates across the industry sit at 60 to 65%, and roughly 30% of jobs need a return trip. From the moment a customer notices an issue to the moment the work is closed, a single service event can take 80 to 120 hours.
What closing the loop looks like
The fix isn't more data. It's connecting the data that already exists to the moments where service teams make decisions. When that happens, the same service event runs very differently:
The machine raises its hand first. An anomaly is caught at the asset, and the service team is alerted before the customer notices.
The case opens with a diagnosis attached. The likely cause, the machine's history, and the relevant procedure are already in the ticket.
Scheduling lines up parts and people. The visit is booked when the right part is on hand and a qualified tech is available.
The tech arrives ready to fix. Investigation happens before the truck rolls, so the visit lands on the first try.
The work records itself. Resolution notes, codes, and parts used are captured at close-out and fed back into every system.
Each closed job makes the next diagnosis better. Over time, field service shifts from reacting to customer calls to acting on machine signals.
Where Aerovy fits
Aerovy is the AI operating layer for hardware OEMs. It reads from where machine and enterprise data already live, such as telematics, data warehouses, and ERP, and writes into the systems teams already use, such as service management, parts ordering, and team chat. Nothing in the existing stack gets replaced. AI agents handle diagnostics, scheduling, parts, and reporting, so technicians spend their time on the work that needs human hands.
The next connection
The theme at MHI 2026 was connecting the supply chain. For OEMs, the next connection is inside their own business: linking the data they already have to the people who need it in the field. That's where faster response times, higher first-time fix rates, and new aftermarket revenue come from.
If you're working through the same challenge, we'd love to hear how your team is approaching it. Book a demo and let's pick up the conversation.
