Author: Alison Chappell – Sales and Marketing Manager – Infomill Ltd
Would You Trust AI With a Spanner? AI in Field Service – A Conversation Worth Having
If you’re running a field service operation today, AI is probably already on your radar.
Maybe your engineers are using it. Maybe your competitors are talking about it. Maybe you’re even being asked when you’re going to introduce it into your own systems.
And on the surface, it feels like a no-brainer.
Faster answers. Less reliance on senior engineers. Quicker fixes. Higher first-time fix rates.
I thought the same.
In fact, I was fully on board with embedding AI-generated answers directly into our own software. The idea of giving engineers instant, intelligent support in the field is incredibly compelling.
But once we started digging into it properly, a few uncomfortable truths started to emerge.
The Promise Is Real
Let’s start with the good—because there’s plenty of it.
AI can genuinely transform how your engineers access information. Instead of searching manuals or calling back to the office, they can just ask a question and get an answer in seconds using a virtual assistant.
For newer engineers, that’s huge. It levels the playing field. It gives them confidence. It reduces dependency on your most experienced people.
From a business perspective, that means faster job completion, fewer repeat visits, better customer experience. All things you care about.
But Here’s the Problem No One Talks About Enough
AI doesn’t know things. It generates answers based on data. And that data? It can be:
- Out of date
- Incomplete
- Poorly structured
- Or just plain wrong
The issue isn’t that AI sometimes makes mistakes—it’s that it can deliver incorrect answers with complete confidence. Now put that into a field service context.
An engineer standing in front of a live piece of equipment…Trying to diagnose a fault…Relying on an answer that sounds right…But isn’t.
That’s not just inefficient—that’s a risk.
‘In this space, answers have to be trusted. That’s why with AnswersAnywhere we’re using a RAG-based approach, grounding AI in the customer’s own data—equipment, documents and parts lists.’
A Real-World Scenario
An engineer is on-site with a boiler showing an unfamiliar fault code. Instead of calling for help, they ask an AI tool: “What does E168 mean and how do I fix it?”. The response is instant: circulation issue—check and replace the pump. It sounds right, so they do exactly that.
But the real fault? A sensor issue introduced in a recent firmware update, only noted in a technical bulletin. Now you’ve got a perfectly good pump replaced, extra cost, a second visit, and a frustrated customer.
The AI didn’t fail—it just didn’t know the right thing. And that’s the problem.
This Is Where My Thinking Changed
I’ve spent over 12 years delivering software that provides technical answers to the field service industry, so when AI arrived, I saw it as a game changer. And it is—but only if it’s controlled.
In this space, answers have to be trusted. That’s why with AnswersAnywhere we’re using a RAG-based approach, grounding AI in the customer’s own data—equipment, documents and parts lists.
The result is AI that’s not just fast, but reliable. And that’s what really matters.
So What’s the Alternative?
We haven’t stepped away from AI—but we’ve changed how we approach it. Instead of letting AI pull from broad, uncontrolled data sources, we’re focusing on RAG (Retrieval-Augmented Generation) models. In simple terms, that means:
The AI only answers using data that exists inside your system
That data is known, structured, and controlled
And ideally, it’s validated and up to date
So rather than “guessing” based on the internet or generic training data, the AI is grounded in your technical documentation, your processes, and your equipment data. It becomes less of a free-thinking assistant—and more of a highly efficient, context-aware support tool.
What This Means for You as a Service Leader
AI absolutely has a place in field service. Ignoring it isn’t the answer. But blindly trusting it isn’t either. The real question is:
Do you want fast answers… or reliable ones?
Because in this industry, the two aren’t always the same—unless you design for it properly. The organisations that get this right will be the ones who control the data behind the AI, understand its limitations and build it into their workflows responsibly. But in field service, it shouldn’t replace trusted knowledge—it should be built on top of it. That’s the shift.
From “AI knows everything”
To “AI knows what we trust it to know”
And that’s where it starts to become genuinely valuable—without becoming a risk.
