AI Isn’t Software. It’s a Coworker That Needs Training.
For years, rolling out new technology followed a familiar script:
Then along comes AI, especially agents, and that approach doesn’t quite apply.
Because AI doesn’t behave like traditional software. It behaves more like a new hire. A fast one. A tireless one. One that can draft, analyze, summarize, and recommend at scale. But also one that can confidently hand you something that looks right and isn’t.
So the question isn’t just how do we train people on AI?
It’s, How do we train people to work with it?
Before we throw out the old playbook, it’s worth noting: a few fundamentals still make sense.
Your sales team doesn’t care about marketing use cases. Your CS team doesn’t care about pipeline generation. Relevance is still oxygen.
“Here’s how to cut content generation time by 90%” will always land harder than “here are 12 general capabilities.”
Executive sponsorship, clear expectations, reinforcement loops; none of that goes away. If leadership expects everyone to just “be productive!”, sub-optimal adoption follows.
Office hours, quick wins, internal champions; same story, new tool. So far, nothing shocking. Now for the parts that are quite different.
Traditional tools are procedural.
Do this → then that → get result.
AI is interpretive. It requires users to constantly evaluate:
That’s not a workflow. That’s a mindset. That’s not a typical part of training.
This is the part people underestimate.
AI is only as good as the instructions it’s given. A lot of people aren’t well-versed with AI prompting, at least at first. Same thing with the early days of using Google.
Think of it like managing a very capable intern with zero context:
Training needs to include:
Otherwise, users hit friction early and don’t reach the promised productivity..
Most software behaves predictably (deterministically). AI doesn’t always.
Agents can evolve:
Agent behavior isn’t frozen in time. The expectation should be:.
“This is how it works now. It will learn and evolve every day.”
With traditional software, misuse is limited. With AI, misuse can scale…fast.
Training needs to clearly define:
This isn’t a feature conversation. It’s a boundaries conversation.
Most users fall into one of two camps:
Both are risky. The goal is a middle ground:
AI is powerful. And I’m still responsible/in charge.
That balance doesn’t happen by accident. It has to be taught, reinforced, and modeled.
This is the quiet shift that changes everything.
Software helps you do tasks.
AI helps you think through tasks.
That means training needs to cover:
In other words, you’re not just teaching usage. You’re teaching partnership.
They train AI like it’s Salesforce, a marketing platform, or a project tool.
Feature walkthroughs. Navigation demos. Maybe a few canned examples.
Then they wonder why adoption stalls.
Because none of that teaches people how to work with AI in the flow of real decisions.
If you want this to stick, flip the model. Start here:
What slows each role down today? Start there.
Give people the language to get useful outputs.
Teach them how to spot strong vs. weak responses.
Be explicit about risk, data, and boundaries.
Not sandbox examples. Actual tasks they care about.
When AI adoption lags, it’s because people don’t know how to use it well, not its lack of capabilities.
And in a world where everyone claims AI will make them faster, cheaper, and smarter, the companies that win won’t just have the best tools.
They’ll have the best-trained humans working in partnership with AI.
.svg){kind=icon}
