When most organizations plan an AI rollout, training lands at the end of the project plan. Pick a platform, configure it, roll it out, then send everyone a recorded video explaining how to use it. That's the sequence we see over and over, and it's backwards.
The first step should be to start with training. Before the platform is chosen, the policy is written, and anything is deployed. It sounds counterintuitive, so let me walk through why we do it this way and what we've learned it changes.
The Training That Matters Isn't About Any Specific Platform
The first thing we deliver is foundations training, and the key word is foundations. We're not teaching anyone how to use Copilot. We're not teaching anyone how to use Claude. We're teaching what AI actually is: what it's good at, what it's bad at, when you should use it, when you shouldn't, what data belongs in it and what doesn't, and what actually happens to the information once you type it in and hit enter.
That last part is the one people remember. Most employees have never thought about where their prompt goes. When you explain that a free consumer account may retain what you enter and use it for training, while a properly licensed business account doesn't. You can watch the room change. People aren't careless on purpose. Nobody ever told them how these systems work.
We also cover the basics of writing a good prompt, because there's a real difference between "write me an email" and a request with context, constraints, and an example. None of this is tied to a vendor. If the company switches platforms in a year, and given how fast this market moves, it might; everything from the training still holds.
Training Is Also How You Find Out What's Actually Happening
Here's the part that surprised us when we started running programs this way: the training session doubles as a discovery conversation.
You stand up in front of a department and explain what AI is and what it can do, and hands start going up.
"Wait. I've been using ChatGPT to draft client proposals for six months."
"I use it to summarize meeting notes."
"Can it read our contracts?"
Some of what you hear is a relief. Some of it is exactly the kind of thing a shadow AI audit flags. Either way, you now know things about your own organization that you didn't know an hour earlier.
That feedback is gold when you're building the rest of the program. People will tell you the tasks they want help with, bottlenecks in their workflow, and reports they rebuild from scratch every month. Those will become the use cases you can build into the platform when you deploy it. Without the training session, you'd be guessing at all of that.
It Sets The Tone Before Anyone Else Does
There's a timing problem with waiting to train. Your employees probably won’t be waiting, especially because they're already using AI on personal accounts, on their phones, or in tools you don't know about. If the first official word your organization says about AI is a policy document that arrives after the fact, the message people hear is "you did something wrong."
When you start with training instead, the message is different. It becomes: “We know AI is coming, we have a plan, and here's what you need to know to be part of it.”
That framing matters. It turns AI from something people hide into something people talk about, and an organization where people talk about their AI use is an organization you can actually govern.
The Alternative Is A Rollout That Quietly Fails
We've watched what happens when companies skip this step. They buy licenses, flip the switch, and send an email with a link. Two things follow, and both cost money.
First, people don't know how to use the tool, so they try it a few times, get mediocre results, and go back to doing things the old way. Now you're paying per-seat licenses for software nobody opens.
Second, and this is the one that usually catches leadership off guard, some people do dive in, untrained, and start experimenting all day. They have good intentions, and they’re using real effort, but productivity goes down instead of up, because they're building things nobody asked for and burning through consumption credits while they're at it.
Training first prevents both. People who understand what the tool is for use it where it helps. People who understand what it costs use it efficiently.
Where Training Fits In The Sequence
To be clear, training first doesn't mean training only. It's step one of a sequence.
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Train: so you know where your organization stands.
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Audit: so you know what's already in use.
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Write the policy: informed by what the training surfaced.
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Pick the platform and roll it out in phases: with the use cases you collected along the way.
Every step after the training is easier and better-informed because the training happened first. Skip it, and you're building a program on assumptions.
Interested In Learning How to Roll Out an AI Tool The Right Way?
NetCov recently hosted a webinar that discussed why picking an AI tool is the last choice you should make. It includes a four-step framework that helps you roll out AI to your organization the right way and provides a practical way to evaluate any AI platform. Give it a watch here.
