A short course covering the vocabulary, risks and controls you need before your first pilot.
Key takeaways
Before evaluating any vendor, it helps to separate three kinds of tools: one that extracts or classifies information, one that drafts text for review, and one that makes an autonomous decision. Almost everything worth using at a Canadian energy-services or infrastructure firm falls into the first two categories.
This lesson gives operators and office staff a shared, plain-language vocabulary so vendor conversations don’t get lost in marketing language that means different things depending on who is using it.
The risk in most field-services AI deployments isn’t the occasional error — every process makes those. It’s the absence of a clear step for catching and correcting a mistake before it reaches a client, a crew, or a regulator.
This lesson walks through common failure points: a missing review step, unclear ownership of a filing, or a vendor contract that never specifies what happens to your field data.
Field photos, tickets and location data need to go somewhere once they’re captured, and you should know where. Where the data is stored, how long it’s retained, and whether it’s used to train a model beyond your own use are contractual questions, not technical mysteries.
This lesson gives a short checklist to bring into a vendor conversation so data handling is settled before a pilot starts.
The final lessons cover how to design a proportionate control environment — sign-off checkpoints, logging, an escalation path — and how to scope a first pilot narrow enough to evaluate properly within one season.
By the end, operators leave with a one-page framework for judging any AI proposal that lands on their desk, rather than evaluating every vendor pitch from scratch.
A 30-minute call is enough to tell you whether AI pays for itself here.
