Field service’s best AI lesson, learned from four visionaries you’ve never heard of

The loudest AI conversations are about machines replacing people. The most interesting builders in AI are doing the opposite: solving one human problem well. Field service should take note.

Author: Bryan Burns

Field service technician learning on a tablet on site, showing AI supporting rather than replacing technicians

Ask someone to name an AI visionary and you’ll get the famous handful: the lab founders, the chip CEO, the researchers on stage warning about superintelligence. Look past them, though, and you’ll find builders most field service leaders have never heard of. That’s worth pausing on, because what those builders are doing looks a lot less like science fiction and a lot more like the work we do every day.

Four of them have been on my mind, because each one teaches field service something specific.

Lesson 1: The people closest to the problem have the best ideas

Anton Osika founded Lovable, one of the fastest-growing software companies in the world, on a simple conviction: the best ideas belong to the people closest to the problems, and most of them are locked out because they can’t code. His answer was to let AI do the building so those people can act on what they know.

Field service should recognize itself in that sentence. Nobody is closer to the problem than a dispatcher who has covered the board for ten years or a technician who can diagnose a unit by its sound. The AI programs that work start with what those people already know. The ones that stall usually started with a tool and went looking for somewhere to put it.

Lesson 2: Agents take the toil, people keep the judgment

Spiros Xanthos built Resolve AI to do for software engineers what they least enjoy: the 2 a.m. troubleshooting, the log-digging, the operational grind that burns people out. His agents investigate incidents autonomously so engineers spend their time on judgment calls and building.

Swap “engineer” for “technician” and you have the field service playbook. An agent that briefs a tech before the job, answers questions on site, and writes the wrap-up afterward clears the toil that surrounds the work. The human hours go where human judgment counts.

Lesson 3: AI’s best use is bringing in people the industry overlooked

Keely Cat-Wells founded Making Space, a platform that connects disabled professionals to real careers, working with employers like Netflix, Coca-Cola, and Microsoft. Her team describes AI as assistive technology: something that turns lived human experience into skills an employer can see and use.

For an industry that never stops talking about the technician shortage, that’s a hiring strategy. Field service solves its labor problem by widening who can do the work and supporting every one of them better once they’re in it.

Lesson 4: Even AI runs on field service

Chase Lochmiller runs Crusoe, the company that built the first phase of the Stargate AI data center project in Abilene, Texas. Crusoe picked the site for its surplus wind power: so much of it that local wind farms were sometimes paying customers to take it. It’s about as futuristic as infrastructure gets.

It’s also turbines, substations, cooling systems, backup generation, and thousands of skilled people building and maintaining physical assets in the West Texas heat. The most advanced AI in the world exists because crews in hard hats keep it powered and cooled. The trades are the foundation of the AI era, and demand for them is growing because of it.

None of these four AI innovators are building a machine to replace people. Each one picked a human problem instead, and solved it well.

You don’t need to be a world-recognized visionary

That’s the real lesson in this list. Each of these builders started with a problem they understood deeply, often from lived experience, and applied AI where it made the human work better. Strip away the conference stages and the funding rounds, and that’s all being a visionary is: seeing a problem clearly and doing something about it. Which means if you’ve been running a field service operation, you’ve probably been one all along, without the title. AI is your next opportunity to do it again.

Field service is full of people like that. You know where the wasted trips are, where the paperwork eats the day, where the expertise walks out the door at retirement. You don’t need a research lab to act on that. You need a clear read of where your operation stands and the discipline to improve things in the right order.

Start where the visionaries start: with the problem

Our Next-Gen Service Assessment is a short, free way to get that read. It draws on 20+ years of working alongside field service companies, and it only works if you bring what you know about your own operation. No prep required. It maps your service operation domain by domain and shows where AI would take the toil off your people first.